| Title: | Estimation Statistics with Confidence Intervals |
|---|---|
| Description: | A collection of functions and 'jamovi' module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on 'statpsych' and 'metafor'. This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions. Data sets are included for all examples from Cumming & Calin-Jageman (2024) <ISBN:9780367531508>. |
| Authors: | Robert Calin-Jageman [aut, cre, cph] |
| Maintainer: | Robert Calin-Jageman <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.6 |
| Built: | 2024-12-23 06:49:14 UTC |
| Source: | CRAN |
CI_diamond_ratio returns the diamond ratio and CI for a
meta-analytic effect, the ratio of the random-effects CI width to the
fixed-effects CI width. The diamond ratio is a measure of effect-size
heterogeneity.
CI_diamond_ratio(RE, FE, vi, conf_level = 0.95)CI_diamond_ratio(RE, FE, vi, conf_level = 0.95)
RE |
metafor object with random effects result |
FE |
metafor object with fixed effects result |
vi |
vector of effect size variances |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Calculation of the CI is based on code provided by Maxwell Cairns (see Cairns et al., 2022). Specifically, this function implements what Cairns et al (2022) called the Sub-Q approach, which provides the best CI coverage in simulations. For comparison, this function also returns the CI produced by the bWT-DL approach (which generally has worse performance).
Returns a list with 3 properties:
diamond_ratio
LL - lower limit of the conf_level% CI, Sub-Q approach
UL - upper limit of the conf_level% CI, Sub-Q approach
LL_bWT_DL - lower limit of the conf_level% CI, bWT-DL approach
UL_bWT_DL - upper limit of the conf_level% CI, bWT-DL approach
Cairns, Maxwell, Geoff Cumming, Robert Calin‐Jageman, and Luke A. Prendergast. “The Diamond Ratio: A Visual Indicator of the Extent of Heterogeneity in Meta‐analysis.” British Journal of Mathematical and Statistical Psychology 75, no. 2 (May 2022): 201–19. https://doi.org/10.1111/bmsp.12258.
mydata <- esci::data_mccabemichael_brain # Use esci to obtain effect sizes and sample variances, storing only raw_data mydata <- esci::meta_mdiff_two( data = mydata, comparison_means = "M Brain", comparison_ns = "n Brain", comparison_sds = "s Brain", reference_means = "M No Brain", reference_ns = "n No Brain", reference_sds = "s No Brain", random_effects = FALSE )$raw_data # Conduct fixed effects meta-analysis FE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method="FE" ) # Conduct random effect meta-analysis RE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method="DL" ) # Get the diamond ratio res <- esci::CI_diamond_ratio( RE = RE, FE = FE, vi = mydata$sample_variance )mydata <- esci::data_mccabemichael_brain # Use esci to obtain effect sizes and sample variances, storing only raw_data mydata <- esci::meta_mdiff_two( data = mydata, comparison_means = "M Brain", comparison_ns = "n Brain", comparison_sds = "s Brain", reference_means = "M No Brain", reference_ns = "n No Brain", reference_sds = "s No Brain", random_effects = FALSE )$raw_data # Conduct fixed effects meta-analysis FE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method="FE" ) # Conduct random effect meta-analysis RE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method="DL" ) # Get the diamond ratio res <- esci::CI_diamond_ratio( RE = RE, FE = FE, vi = mydata$sample_variance )
CI_smd_ind_contrast returns the point estimate
and confidence interval for a standardized mean difference (smd aka Cohen's
d aka Hedges g). A standardized mean difference is a difference in means standardized
to a standard deviation:
\[d = \frac{ \psi }{s}\]
CI_smd_ind_contrast( means, sds, ns, contrast, conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE )CI_smd_ind_contrast( means, sds, ns, contrast, conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE )
means |
A vector of 2 or more means |
sds |
A vector of standard deviations, same length as means |
ns |
A vector of sample sizes, same length as means |
contrast |
A vector of group weights, same length as means |
conf_level |
The confidence level for the confidence interval, in decimal form. Defaults to 0.95. |
assume_equal_variance |
Defaults to FALSE |
correct_bias |
Defaults to TRUE; attempts to correct the slight upward bias in d derived from a sample. As of 8/9/2023 - Bias correction has been added for more than 2 groups when equal variance is not assumed, based on recent updates to statpsych |
Returns a list with these named elements:
effect_size - the point estimate from the sample
lower - lower bound of the CI
upper - upper bound of the CI
numerator - the numerator for Cohen's d_biased; the mean difference in the contrast
denominator - the denominator for Cohen's d_biased; if equal variance is assumed this is sd_pooled, otherwise sd_avg
df - the degrees of freedom used for correction and CI calculation
se - the standard error of the estimate; warning not totally sure about this yet
moe - margin of error; 1/2 length of the CI
d_biased - Cohen's d without correction applied
properties - a list of properties for the result
Properties
effect_size_name - if equal variance assumed d_s, otherwise d_avg
effect_size_name_html - html representation of d_name
denominator_name - if equal variance assumed sd_pooled otherwise sd_avg
denominator_name_html - html representation of denominator name
bias_corrected - TRUE/FALSE if bias correction was applied
message - a message explaining denominator and correction status
message_html - html representation of message
A standardized mean difference turns out to be complicated.
First, it has many names:
standardized mean difference (smd)
Cohen's d
When bias in a sample d has been corrected, also called Hedge's g
Second, the choice of the standardizer requires thought:
sd_pooled - used when assuming all groups have exact same variance
sd_avg - does not require assumption of equal variance
other possibilities, too, but not dealt with in this function
The choice of standardizer is important, so it's noted in the subscript:
d_s – assumes equal variance, standardized to sd_pooled
d_avg - does not assume equal variance, standardized to sd_avg
A third complication is the issue of bias: d estimated from a sample has a slight upward bias at smaller sample sizes. With total sample size > 30, this slight bias becomes fairly negligible (kind of like the small upward bias in a sample standard deviation).
This bias can be corrected when equal variance is assumed or when the design of the study is simple (2 groups). For complex designs (>2 groups) without the assumption of equal variance, there is now also an approximate approach to correcting bias from Bonett.
Corrections for bias produce a long-run reduction in average bias. Corrections for bias are approximate.
When equal variance is assumed, the standardized mean difference is d_s, defined in Kline, p. 196:
\[ d_s = \frac{ \psi }{ sd_{pooled} } \]where psi is defined in Kline, equation 7.8:
\[ \psi =\sum_{i=1}^{a}c_iM_i \]and where sd_pooled is defined in Kline, equation 3.11 \[sd_{pooled} = { \frac{ \sum_{i=1}^{a} (n_i -1) s_i^2 } { \sum_{i=1}^{a} (n_i-1) } }\]
The CI for d_s is derived from lambda-prime transformation from Lecoutre, 2007 with code adapted from Cousineau & Goulet-Pelletier, 2020. Kelley, 2007 explains the general approach for linear contrasts.
This approach to generating the CI is 'exact', meaning coverage should be as desired if all assumptions are met (ha!).
Correction of upward bias can be applied.
When equal variance is not assumed, the standardized mean difference is d_avg, defined in Bonett, equation 6:
\[ d_{avg} = \frac{ \psi }{ sd_{avg} }\]Where sd_avg is the square root of the average of the group variances, as given in Bonett, explanation of equation 6:
\[sd_{avg} = \sqrt{ \frac{ \sum_{i=1}^{a} s_i^2 }{ a } }\]The CI is derived from lambda-prime transformation using df and se from Huynh, 1989 – see especially Delacre et al., 2021
This is also an 'exact' approach, and correction can be applied
CI is approximated using the approach from Bonett, 2008
An approximate correction developed by Bonett is used
Bonett D. G. (2023). statpsych: Statistical Methods for Psychologists. R package version 1.4.0. https://dgbonett.github.io/statpsych
Bonett, D. G. (2018). R code posted to personal website (now removed). Formally at https://people.ucsc.edu/~dgbonett/psyc204.html
Bonett, D. G. (2008). Confidence Intervals for Standardized Linear Contrasts of Means. Psychological Methods, 13(2), 99–109. doi:10.1037/1082-989X.13.2.99
Cousineau & Goulet-Pelletier (2020) https://osf.io/preprints/psyarxiv/s2597
Delacre et al., 2021, https://osf.io/preprints/psyarxiv/tu6mp/
Huynh, C.-L. (1989). A unified approach to the estimation of effect size in meta-analysis. NBER Working Paper Series, 58(58), 99–104.
Kelley, K. (2007). Confidence intervals for standardized effect sizes: Theory, application, and implementation. Journal of Statistical Software, 20(8), 1–24. doi:10.18637/jss.v020.i08
Lecoutre, B. (2007). Another Look at the Confidence Intervals for the Noncentral T Distribution. Journal of Modern Applied Statistical Methods, 6(1), 107–116. doi:10.22237/jmasm/1177992600
estimate_mdiff_ind_contrast for friendly version that also
returns raw score effect sizes for this design
# Example from Kline, 2013 # Data in Table 3.4 # Worked out in Chapter 7 # See p. 202, non-central approach # With equal variance assumed and no correction, should give: # d_s = -0.8528028 [-2.121155, 0.4482578] res <- esci::CI_smd_ind_contrast( means = c(13, 11, 15), sds = c(2.738613, 2.236068, 2.000000), ns = c(5, 5, 5), contrast = contrast <- c(1, 0, -1), conf_level = 0.95, assume_equal_variance = TRUE, correct_bias = FALSE ) # Example from [statpsych::ci.lc.stdmean.bs()] should give: # Estimate SE LL UL # Unweighted standardizer: -1.273964 0.3692800 -2.025039 -0.5774878 # Weighted standardizer: -1.273964 0.3514511 -1.990095 -0.6124317 # Group 1 standardizer: -1.273810 0.4849842 -2.343781 -0.4426775 res <- esci::CI_smd_ind_contrast( means = c(33.5, 37.9, 38.0, 44.1), sds = c(3.84, 3.84, 3.65, 4.98), ns = c(10,10,10,10), contrast = c(.5, .5, -.5, -.5), conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE )# Example from Kline, 2013 # Data in Table 3.4 # Worked out in Chapter 7 # See p. 202, non-central approach # With equal variance assumed and no correction, should give: # d_s = -0.8528028 [-2.121155, 0.4482578] res <- esci::CI_smd_ind_contrast( means = c(13, 11, 15), sds = c(2.738613, 2.236068, 2.000000), ns = c(5, 5, 5), contrast = contrast <- c(1, 0, -1), conf_level = 0.95, assume_equal_variance = TRUE, correct_bias = FALSE ) # Example from [statpsych::ci.lc.stdmean.bs()] should give: # Estimate SE LL UL # Unweighted standardizer: -1.273964 0.3692800 -2.025039 -0.5774878 # Weighted standardizer: -1.273964 0.3514511 -1.990095 -0.6124317 # Group 1 standardizer: -1.273810 0.4849842 -2.343781 -0.4426775 res <- esci::CI_smd_ind_contrast( means = c(33.5, 37.9, 38.0, 44.1), sds = c(3.84, 3.84, 3.65, 4.98), ns = c(10,10,10,10), contrast = c(.5, .5, -.5, -.5), conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE )
CI_smd_one STILL NEEDS WORK TO VERIFY APPROACH FOR SE and MoE
CI_smd_one(mean, sd, n, reference_mean, conf_level = 0.95, correct_bias = TRUE)CI_smd_one(mean, sd, n, reference_mean, conf_level = 0.95, correct_bias = TRUE)
mean |
Mean for a single group for the outcome measure |
sd |
Standard deviation, > 0 |
n |
Sample size, an integer > 2 |
reference_mean |
defaults to 0 |
conf_level |
The confidence level for the confidence interval, in decimal form. Defaults to 0.95. |
correct_bias |
Defaults to TRUE |
Returns a list with these named elements:
effect_size - the point estimate from the sample
lower - lower bound of the CI
upper - upper bound of the CI
numerator - the numerator for Cohen's d_biased; the mean difference in the contrast
denominator - the denominator for Cohen's d_biased; if equal variance is assumed this is sd_pooled, otherwise sd_avg
df - the degrees of freedom used for correction and CI calculation
se - the standard error of the estimate; warning not totally sure about this yet
moe - margin of error; 1/2 length of the CI
d_biased - Cohen's d without correction applied
properties - a list of properties for the result
Properties
effect_size_name - if equal variance assumed d_s, otherwise d_avg
effect_size_name_html - html representation of d_name
denominator_name - if equal variance assumed sd_pooled otherwise sd_avg
denominator_name_html - html representation of denominator name
bias_corrected - TRUE/FALSE if bias correction was applied
message - a message explaining denominator and correction status
message_html - html representation of message
# example code res <- esci::CI_smd_one(24.5, 3.65, 40, 20)# example code res <- esci::CI_smd_one(24.5, 3.65, 40, 20)
Happiness may not be important just for the person feeling it; happiness may also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014) examined this idea by collecting data on U.S. states. A Gallup poll in 2010 was used to measure each state's well-being index, a measure of mean happiness for the state's residents on a scale from 0 to 100. Next, a kidney donation database for 1999-2010 was used to figure out each state's rate (number of donations per 1 million people) of non-directed kidney donations-that's giving one kidney to a stranger, an extremely generous and altruistic thing to do!
data_altruism_happinessdata_altruism_happiness
data_altruism_happinessA data frame with 50 rows and 6 columns:
factor - State where data was collected
factor - State where data was collected
numeric - State where data was collected
numeric - State where data was collected
numeric - State where data was collected
numeric - State where data was collected
To what extent does the wording of a question influence one's judgment? In their classic study, Jacowitz and Kahneman (1995) asked participants to estimate how many babies are born each day in the United States. Participants were given either a low anchor (more than 100 babies/day) or a high anchor (less than 50,000 babies/day). Those who saw the low anchor estimated many fewer births/day than those who saw the high anchor, which suggests that the wording can have a profound influence. The correct answer, as it happens, is ~11,000 births/day in 2014. To investigate the extent that these results are replicable, the Many Labs project repeated this classic study at many different labs around the world. You can find the summary data for 30 of these labs in the Anchor Estimate ma data file
data_anchor_estimate_madata_anchor_estimate_ma
data_anchor_estimate_maA data frame with 30 rows and 9 columns:
factor
numeric
numeric
integer
numeric
numeric
integer
factor
factor
Climate change? Vaccines? Fake news and conspiracy theories on these and numerous other issues can be highly damaging, but are thriving in this social media age. Trying to debunk a conspiracy theory by presenting facts and evidence often doesn't work, alas. Psychological inoculation, also similar to prebunking, presents a mild form of misinformation, preferably with explanation, in the hope of building resistance to real-life fake news-a sort of vaccine for fake news. The Bad News game is a spin-off from research on psychological inoculation. Basol et al. (2020) assessed the possible effectiveness of this game as a fake news vaccine. At getbadnews.com you can click 'About' for information, or just start playing the game- it's easy and maybe even fun. You encounter mock Twitter (now X) fake news messages that illustrate common strategies for making fake news memorable or believable. You make choices between messages and decide which ones to 'forward' as you try to spread fake news while building your credibility score and number of 'followers'-rather like real life for a conspiracy theorist wanting to spread the word. Compete with your friends for credibility and number of followers. Basol's online participants first saw 18 fictitious fake news tweets and rated each for reliability (accuracy, believability), and also rated their confidence in that reliability rating. Both ratings were on a 1 to 7 scale. Those in the BadNews group then played the game for 15 minutes, whereas those in the Control group played Tetris. Then all once again gave reliability and confidence ratings for the 18 tweets.
data_basol_badnewsdata_basol_badnews
data_basol_badnewsA data frame with 198 rows and 3 columns:
numeric
numeric
factor
Daryl Bem was an experienced mentalist and research psychologist, who, a decade earlier, had been one of several outside experts invited to scrutinize the laboratory and experimental procedures of parapsychology researcher Charles Honorton. Bem not only judged them adequate, but joined the research effort and became a coauthor with Honorton. Bem and Honorton (1994) first reviewed early ganzfeld studies and described how the experimental procedure had been improved to reduce the chance that results could be influenced by various possible biases, or leakages of information from sender to receiver. For example, the randomization procedure was carried out automatically by computer, and all stimuli were presented under computer control. Bem and Honorton then presented data from studies conducted with the improved procedure. Table 13.1 presents basic data from 10 studies reported by Bem and Honorton (1994). Participants each made a single judgment, so in Pilot 1, for example, 22 participants responded, with 8 of them giving a correct response. Three pilot studies helped refine the procedures, then four studies used novice receivers. Study 5 used 20 students of music, drama, or dance as receivers, in response to suggestions that creative people might be more likely to show telepathy. Studies 6 and 7 used receivers who had participated in an earlier study. The proportion of hits expected by chance is .25, and Table 13.1 shows that all but Study 1 found proportions higher than .25.
data_bem_psychicdata_bem_psychic
data_bem_psychicA data frame with 10 rows and 5 columns:
factor
factor
integer
integer
numeric
doi:10.1037/0033-2909.115.1.25
A subset of data_bodywell_fm, reports only for those participants who identified as female. Data is Subjective Wellbeing and Body Satisfaction.
data_bodywellfdata_bodywellf
data_bodywellfA data frame with 59 rows and 2 columns:
numeric
numeric
Survey data from a convenience sample Dominican University students. Reported are measures of Subjective Wellbeing abd Body Satisfaction.
data_bodywellfmdata_bodywellfm
data_bodywellfmA data frame with 106 rows and 2 columns:
numeric
numeric
A subset of data_bodywell_fm, reports only for those participants who identified as male. Data is Subjective Wellbeing abd Body Satisfaction.
data_bodywellmdata_bodywellm
data_bodywellmA data frame with 47 rows and 2 columns:
numeric
numeric
Clinton conducted a survey of college students to determine the extent to which subjective well-being is related to campus involvement (Campus Involvement data set on the book website). Participants completed a measure of subjective well-being (scale from 1 to 5) and a measure of campus involvement (scale from 1 to 5). Participants also reported gender (male or female) and commuter status (resident or commuter). Synthetic data simulated to mimic survey data from a class project.
data_campus_involvementdata_campus_involvement
data_campus_involvementA data frame with 113 rows and 6 columns:
integer
factor
numeric
factor
numeric
numeric
Fictitious data from an unrealistically small HEAT study comparing scores for a single group of students before and after a workshop on climate change.
data_chap_8_paired_ex_8.18data_chap_8_paired_ex_8.18
data_chap_8_paired_ex_8.18A data frame with 8 rows and 2 columns:
numeric
numeric
Some researchers claim that moral judgments are based not only on rational considerations but also on one's current emotional state. To what extent can recent emotional experiences influence moral judgments? Schnall et al. (2008) examined this question by manipulating feelings of cleanliness and purity and then observing the extent that this changes how harshly participants judge the morality of others. Inscho Study 1, Schnall et al. asked participants to complete a word scramble task with either neutral words (neutral prime) or words related to cleanliness (cleanliness prime). All students then completed a set of moral judgments about controversial scenarios: Moral judgment is the average of six items, each rated on a scale from 0 to 9, with high meaning harsh. The data from this study are in the Clean moral file, which also contains data from a replication by Johnson et al. (2014)
data_clean_moraldata_clean_moral
data_clean_moralA data frame with 208 rows and 4 columns:
factor
numeric
factor
numeric
Data from an additional survey of Dominican University students; reports various psychological and behavioral measures.
data_college_survey_1data_college_survey_1
data_college_survey_1A data frame with 243 rows and 23 columns:
integer
factor
factor
integer
factor
factor
factor
logical
factor
logical
numeric
numeric
integer
numeric
numeric
numeric
numeric
numeric
numeric
numeric
numeric
numeric
numeric
College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4
data_college_survey_2data_college_survey_2
data_college_survey_2A data frame with 138 rows and 17 columns:
integer
factor
factor
numeric
numeric
factor
factor
factor
logical
numeric
numeric
numeric
numeric
numeric
numeric
numeric
factor
DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014)
data_damischrcjdata_damischrcj
data_damischrcjA data frame with 8 rows and 5 columns:
factor
numeric
integer
integer
factor
Synthetic data meant to represent Experiment 1 of Effron & Raj, 2020. 138 U.S. adults, recruited in August 2018 on Prolific Academic, worked online. First, they saw six fake headlines four times, each time being asked to rate how interesting/engaging/ funny/well-written the headline was. This rating task simply ensured that the participants paid some attention to each headline. The stimuli were 12 actual fake-news headlines about American politics, with accompanying photographs. Half appealed to Republicans and half to Democrats. Later, 12 fake headlines were presented one at a time, a random mix of the six Old headlines-those seen before-and six New headlines not seen previously. It was stated very clearly that independent, non-partisan fact-checking had established that all the headlines were not true. Participants first rated, on a 0 (not at all) to 100 (extremely) scale, the degree to which to which they judged it unethical to publish that headline. That's the Unethicality DV. They also rated how likely they would be to share the headline if they saw it posted by an acquaintance on social media; there were three further similar ratings. Finally, they rated how accurate they believed the headline to be.
data_effronraj_fakenewsdata_effronraj_fakenews
data_effronraj_fakenewsA data frame with 138 rows and 5 columns:
factor
numeric
numeric
numeric
numeric
v1.1 – Participant 46 had an invalid negative value for UnethOld; replaced with 0.
Anger is a powerful emotion. To what extent can feeling angry actually change your heart rate? To investigate, Lakens (2013) asked students to record their heart rate (in beats per minute) at rest before (baseline) and then while recalling a time of intense anger. This is a conceptual replication of a classic study by Ekman et al. (1983). Load the Emotion heartrate data set from the book website.
data_emotion_heartratedata_emotion_heartrate
data_emotion_heartrateA data frame with 68 rows and 3 columns:
integer
numeric
numeric
To what extent does initial performance in a class relate to performance on a final exam? First exam and final exam scores for nine students enrolled in an introductory psychology course. Exam scores are percentages, where 0 = no answers correct and 100 = all answers correct. Data is synthetic to represent patterns found in a previous psych stats course.
data_exam_scoresdata_exam_scores
data_exam_scoresA data frame with 9 rows and 3 columns:
factor
numeric
numeric
To what extent does being exposed to the American flag influence political attitudes? One seminal study (Carter et al., 2011) explored this issue by subtly exposing participants either to images of the American flag or to control images. Next, participants were asked about their political attitudes, using a 1-7 rating scale where high scores indicate conservative attitudes. Participants exposed to the flag were found to express substantially more conservative attitudes. The Many Labs project replicated this finding at 25 different locations in the United States.
data_flag_priming_madata_flag_priming_ma
data_flag_priming_maA data frame with 25 rows and 7 columns:
factor
numeric
numeric
integer
numeric
numeric
integer
To what extent do men and women differ in their attitudes towards mathematics? To investigate, Nosek et al. (2002) asked male and female students to complete an Implicit Association Test (IAT)-this is a task designed to measure a participant's implicit (non-conscious) feelings towards a topic. (If you've never heard of the IAT, try it out here: tiny.cc/harvardiat) On this IAT, students were tested for negative feelings towards mathematics and art. Scores reflect the degree to which a student had more negative implicit attitudes about mathematics than art (positive score: more negative feelings about mathematics; 0: same level of negativity to both; negative score: more negative feelings about art). data_gender_math_iat has data from two labs that participated in a large-scale replication of the original study (Klein et al., 2014a, 2014b)
data_gender_math_iatdata_gender_math_iat
data_gender_math_iatA data frame with 155 rows and 4 columns:
factor
numeric
factor
numeric
In EOC Exercise 4 in Chapter 7 we encountered the classic study of Nosek et al. (2002), in which male and female participants completed an Implicit Association Test (IAT) that measured the extent of negative attitudes towards mathematics, compared with art. The study found that women, compared with men, tended to have more negative implicit attitudes towards mathematics. The Many Labs project repeated this study at locations around the world (Klein et al., 2014a, 2014b). Summary data for 30 of these labs are available in Gender math IAT ma. Higher scores indicate more implicit bias against mathematics. See also data_gender_math_iat for raw data from two specific sites from this replication effort.
data_gender_math_iat_madata_gender_math_iat_ma
data_gender_math_iat_maA data frame with 30 rows and 9 columns:
factor
numeric
numeric
integer
numeric
numeric
integer
factor
factor
Could eating much less delay Alzheimer's? If so, that would be great news. Halagappa et al. (2007) investigated the possibility by using a mouse model, meaning they used Alzheimer-prone mice, which were genetically predisposed to develop neural degeneration typical of Alzheimer's. The researchers used six independent groups of mice, three tested in mouse middle age when 10 months old, and three in mouse old age when 17 months. At each age there was a control group of normal mice that ate freely (the NFree10 and NFree17 groups), a group of Alzheimer-prone mice that also ate freely (the AFree10 and AFree17 groups), and another Alzheimer-prone group restricted to 40% less food than normal (the ADiet10 and ADiet17 groups). Table 14.2 lists the factors that define the groups, and group labels. I'll discuss one measure of mouse cognition: the percent time spent near the target of a water maze, with higher values indicating better learning and memory. Table 14.2 reports the means and standard deviations for this measure, and group sizes.
data_halagappadata_halagappa
data_halagappaA data frame with 6 rows and 4 columns:
factor
numeric
numeric
integer
Maybe you're thinking about buying a house after college? Regression can help you hunt for a bargain. Download the Home Prices data set. This file contains real estate listings from 1997 to 2003 in a city in California. Let's explore the extent to which the size of the home (in square meters) predicts the sale price.
data_home_pricesdata_home_prices
data_home_pricesA data frame with 300 rows and 8 columns:
integer
factor
integer
integer
integer
numeric
factor
logical
Suppose you want to change the battery in your phone, cook the perfect souffle, or perform a three-ball juggle. Just as numerous people do every day, you might search online to find a video that shows what to do. Suppose you watch such a video just once. First question: How well would you then predict you could perform the task? Second question: How well would you actually perform the task, the first time you tried? Now suppose you watch the video many times: Again consider the two questions. These questions were investigated in a series of studies by Kardas and O'Brien (2018). Let's first do some quick analyses of Kardas Experiments 3 and 4-let's call them Expt 3 and Expt 4-focusing on the effect of watching a video many times rather than once. In Expt 3, participants first watched a brief video of a person performing the moonwalk. The Low Exposure group watched the video once, the High Exposure group 20 times. Then participants predicted, on a 1 to 10 scale, how well they felt they would be able to perform the moonwalk themselves. Finally, they attempted a single performance of the moonwalk, which was videoed. These videos were rated, on the same 1 to 10 scale, by independent raters.
data_kardas_expt_3data_kardas_expt_3
data_kardas_expt_3A data frame with 100 rows and 3 columns:
factor
numeric
numeric
Suppose you want to change the battery in your phone, cook the perfect souffle, or perform a three-ball juggle. Just as numerous people do every day, you might search online to find a video that shows what to do. Suppose you watch such a video just once. First question: How well would you then predict you could perform the task? Second question: How well would you actually perform the task, the first time you tried? Now suppose you watch the video many times: Again consider the two questions. These questions were investigated in a series of studies by Kardas and O'Brien (2018). Let's first do some quick analyses of Kardas Experiments 3 and 4-let's call them Expt 3 and Expt 4-focusing on the effect of watching a video many times rather than once. Expt 4 was conducted online with participants recruited from Amazon's Mechanical Turk, who are typically more diverse than students. The online task was based on a mirror-drawing game developed by Bob and students (Cusack et al., 2015, tiny.cc/bobmirrortrace). Participants first read a description of the game and the scoring procedure. To play, you use your computer trackpad to trace a target line, as accurately and quickly as you can. The task is tricky because you can see only a mirror image of the path you are tracing with a finger on the trackpad. A running score is displayed. The final score is the percentage match between the target line and the path you traced, so scores can range from 0 to 100
data_kardas_expt_4data_kardas_expt_4
data_kardas_expt_4A data frame with 270 rows and 4 columns:
factor
integer
integer
numeric
To what extent do brand labels influence perceptions of a product? To investigate, participants were asked to participate in a taste test. All participants were actually given the same grape juice, but one glass was poured from a bottle labeled 'Organic' and the other glass from a bottle labeled 'Generic'. After each tasting (in counterbalanced order), participants were asked to rate how much they enjoyed the juice on a scale from 1 (not at all) to 10 (very much). Participants were also asked to say how much they'd be willing to pay for a large container of that juice on a scale from $1 to $10. Load the Labels flavor data set from the book website. These data were collected as part of a class project by Floretta-Schiller et al. (2015), whose work was inspired by a very clever study looking at the effects of fast-food wrappers on children's enjoyment of food (Robinson et al., 2007).
data_labels_flavordata_labels_flavor
data_labels_flavorA data frame with 51 rows and 6 columns:
integer
numeric
numeric
numeric
numeric
logical
The researchers were interested in how different study approaches might impact learning. Working in France, they created three independent groups, each comprising 95 adults. Participants worked online through seven learning modules about DNA. The Reread group worked through a module, then worked through it a second time before going on to the next module. The Quiz group worked through a module, then had to complete a quiz before going on to the next module. The Prequiz group had to work through the quiz before seeing the presentation of a module, then went on to the quiz and presentation of the next module. Participants received feedback and a brief explanation after answering each question in a quiz, and could take as long as they wished to work through each module and quiz. Seven days later, participants completed a final test. data_latimier_3groups is the full data set. To facilitate different student exercises, there are also separate data entities for each group (data_latimier_prequiz, data_latimier_reread, etc.), and for every pair of groups (data_latimier_quiz_prequiz, etc.).
data_latimier_3groupsdata_latimier_3groups
data_latimier_3groupsA data frame with 285 rows and 3 columns:
factor
numeric
numeric
Just the Prequiz group from Latimier et al., 2019 See full details in data_latimier_3_groups
data_latimier_prequizdata_latimier_prequiz
data_latimier_prequizA data frame with 95 rows and 3 columns:
factor
numeric
numeric
Just the Quiz group from Latimier et al., 2019 See full details in data_latimier_3_groups
data_latimier_quizdata_latimier_quiz
data_latimier_quizA data frame with 95 rows and 3 columns:
factor
numeric
numeric
Just the Quiz (RQ) an Prequiz (QR) groups from Latimier et al., 2019 See full details in data_latimier_3_groups
data_latimier_quiz_prequizdata_latimier_quiz_prequiz
data_latimier_quiz_prequizA data frame with 190 rows and 3 columns:
factor
numeric
numeric
Just the Reread group from Latimier et al., 2019 See full details in data_latimier_3_groups
data_latimier_rereaddata_latimier_reread
data_latimier_rereadA data frame with 95 rows and 3 columns:
factor
numeric
numeric
Just the Reread (RR) an Prequiz (QR) groups from Latimier et al., 2019 See full details in data_latimier_3_groups
data_latimier_reread_prequizdata_latimier_reread_prequiz
data_latimier_reread_prequizA data frame with 190 rows and 3 columns:
factor
numeric
numeric
Just the Reread Quiz groups from Latimier et al., 2019 See full details in data_latimier_3_groups
data_latimier_reread_quizdata_latimier_reread_quiz
data_latimier_reread_quizA data frame with 190 rows and 3 columns:
factor
numeric
numeric
Is genius born or made? Could any of us be Michael Jordan or Mozart if we worked sufficiently hard to develop the requisite skills? Meta-analysis of correlations can help answer such questions. The issue here is the extent that practice and effort may be sufficient for achieving the highest levels of expertise. Ericsson et al. (1993) argued that years of effort is what matters most: 'Many characteristics once believed to reflect innate talent are actually the result of intense practice extended for a minimum of 10 years' (p. 363). This view was enormously popularized by Malcolm Gladwell (2008), who argued in his book Outliers that 10,000 hours of focused practice is the key to achieving expertise. However, this view is now being challenged, with one important contribution being a large meta-analysis of correlations between amount of intense practice and level of achievement: Macnamara et al. (2014) combined 157 correlations reported in a wide range of fields, from sports to music and education, and found correlation of only r = .35 (.30, .39). Table 11.1 shows the 16 main correlations for music, from Macnamara et al. (2014).
data_macnamara_r_madata_macnamara_r_ma
data_macnamara_r_maA data frame with 16 rows and 4 columns:
factor
numeric
integer
factor
You've probably seen cross sections of the brain with brightly colored areas indicating which brain regions are most active during a particular type of cognition or emotion. Search online for fMRI (functional magnetic resonance imaging) brain scans to see such pictures and learn how they are made. They can be fascinating-are we at last able to see how thinking works? In 2008, McCabe and Castel published studies that investigated how adding a brain picture might alter judgments of the credibility of a scientific article. For one group of participants, an article was accompanied by a brain image that was irrelevant to the article. For a second, independent group, there was no image. Participants read the article, then gave a rating of the statement 'The scientific reasoning in the article made sense'. The response options were 1 (strongly disagree), 2 (disagree), 3 (agree), and 4 (strongly agree). The researchers reported that mean ratings were higher with a brain picture than without, but that the difference was small. It seemed that an irrelevant brain picture may have some, but only a small influence. The authors drew appropriately cautious conclusions, but the result quickly attracted attention and there were many media reports that greatly overstated it. At least according to the popular media, it seemed that adding a brain picture made any story convincing. Search on 'McCabe seeing is believing', or similar, to find media reports and blog posts. Some warned readers to watch out for brain pictures, which, they said, can trick you into believing things that aren't true. The result intrigued some New Zealander colleagues of mine who discovered that, despite its wide recognition, the finding hadn't been replicated. They ran replication studies using the materials used by the original researchers, and found generally small ESs. I joined the team at the data analysis stage and the research was published (Michael et al., 2013). I'll discuss here a meta-analysis of two of the original studies and eight replications by our team. The studies were sufficiently similar for meta-analysis, especially considering that all the Michael studies were designed to have many features that matched the original studies. This data set does not include two additional critique studies run by the Michael team. See also data_mccabemichael_brain2
data_mccabemichael_braindata_mccabemichael_brain
data_mccabemichael_brainA data frame with 10 rows and 9 columns:
factor
numeric
numeric
numeric
numeric
numeric
numeric
factor
factor
Same as data_mccabemichael_brain but includes two additional critique studies run by the Michael team.
data_mccabemichael_brain2data_mccabemichael_brain2
data_mccabemichael_brain2A data frame with 12 rows and 9 columns:
factor
numeric
numeric
numeric
numeric
numeric
numeric
factor
factor
My example is a well-known study of mindfulness meditation by Holzel et al. (2011). People who wanted to reduce stress, and were not experienced meditators, were assigned to a Meditation (n = 16) or a Control (n = 17) group. The Meditation group participated in 8 weeks of intensive training and practice of mindfulness meditation. The researchers used a questionnaire to assess a range of emotional and cognitive variables both before (Pretest) and after (Posttest) the 8-week period. All assessment was conducted while the participants were not meditating. The study is notable for including brain imaging to assess possible changes in participants' brains from Pretest to Posttest. The researchers measured gray matter concentration, which increases in brain regions that experience higher and more frequent activation. The researchers expected that the hippocampus may be especially responsive to meditation because it has been implicated in the regulation of emotion, arousal, and general responsiveness. They therefore included in their planned analysis the assessment of any changes to gray matter concentration in the hippocampus.
data_meditationbraindata_meditationbrain
data_meditationbrainA data frame with 33 rows and 7 columns:
numeric
numeric
factor
numeric
numeric
numeric
numeric
doi:10.1007/978-94-007-2079-4_9
To what extent might choosing organic foods make us morally smug? To investigate, Eskine (2013) asked participants to rate images of organic food, neutral (control) food, or comfort food. Next, under the guise of a different study, all participants completed a moral judgment scale in which they read different controversial scenarios and rated how morally wrong they judged them to be (scale of 1-7, high judgments mean more wrong). Table 14.7 shows summary data, which are also available in the first four variables in the OrganicMoral file. In that file you can see two further variables, which report full data-we'll come to these shortly. Here we use the summary data. After the results of Eskine (2013) were published, Moery and Calin-Jageman (2016) conducted a series of close replications. We obtained original materials from Eskine, piloted the procedure, and preregistered our sampling and analysis plan. The OSF page, osf.io/atkn7, has all the details. The data from one of these close replications are in the last two variables of the OrganicMoral file. For this replication study, group names are in the variable ReplicationGroup and moral judgments in MoralJudgment. (You may need to scroll right to see these variables.)
data_organicmoraldata_organicmoral
data_organicmoralA data frame with 106 rows and 6 columns:
factor
numeric
numeric
integer
factor
numeric
% transcription scores from pen and laptop group of Meuller et al., 2014
data_penlaptop1data_penlaptop1
data_penlaptop1A data frame with 65 rows and 2 columns:
factor
numeric
To what extent could feeling powerful affect your performance at motor skills? To investigate, Burgmer and Englich (2012) assigned German participants to either power or control conditions and then asked them to play golf (Experiment 1) or darts (Experiment 2). They found that participants manipulated to feel powerful performed substantially better than those in the control condition. To study this finding further, Cusack et al. (2015) conducted five replications in the United States. Across these replications they tried different ways of manipulating power, different types of tasks (golf, mirror tracing, and a cognitive task), different levels of difficulty, and different types of participant pools (undergraduates and online). Summary data from all seven studies are available in PowerPerformance ma.
data_powerperformance_madata_powerperformance_ma
data_powerperformance_maA data frame with 8 rows and 12 columns:
factor
factor
factor
factor
factor
numeric
numeric
numeric
numeric
numeric
integer
integer
doi:10.1371/journal.pone.0140806
How do you think you would react to feedback that gave encouragement and reassurance, or, instead, encouragement and challenge? Carol Dweck and her colleagues have investigated many such questions about how people respond to different types of feedback. My next example comes from Dweck's research group and illustrates data analysis that starts with the full data, rather than only summary statistics. Rattan et al. (2012) asked their college student participants to imagine they were undertaking a mathematics course and had just received a low score (65%) on the first test of the year. Participants were assigned randomly into three groups, which received different feedback along with the low score. The Comfort group received positive encouragement and also reassurance, the Challenge group received positive encouragement and also challenge, and the Control group received just the positive encouragement. Participants then responded to a range of questions about how they felt about the course and their professor. I'll discuss data for their ratings of their own motivation toward mathematics, made after they had received the feedback.
data_rattanmotivationdata_rattanmotivation
data_rattanmotivationA data frame with 54 rows and 2 columns:
factor
numeric
doi:10.1016/j.jesp.2011.12.012
To what extent is a religious upbringing related to prosocial behavior in childhood? To investigate, a large international sample of children was asked to play a game in which they were given 10 stickers but then asked if they would give some of these stickers away to another child who had not been able to be tested that day. The number of stickers donated was considered a measure of altruistic sharing. In addition, the parents of each child reported the family's religion. Summary data provided. THIS STUDY HAS BEEN RETRACTED DUE TO AN ERRONEOUS ANALYSIS - THE DATASET WILL BE REMOVED FROM FUTURE VERSIONS OF ESCI AND THE BOOK
data_religionsharingdata_religionsharing
data_religionsharingA data frame with 3 rows and 4 columns:
factor
numeric
numeric
integer
Let's look at some data about religious beliefs. The Religious belief file has data from a large online survey in which participants were asked to report, on a scale from 0 to 100, their belief in the existence of God. Age was also reported.
data_religious_beliefdata_religious_belief
data_religious_beliefA data frame with 213 rows and 3 columns:
character
factor
integer
Self-explaining is a learning strategy where students write or say their own explanations of the material they are studying. Self-explaining has generally been found to be more effective than standard studying, but it may also take more time. This raises the question of whether it's the study strategy or the extra time that benefits learning. To explore this issue, grade school children took a pretest of mathematics conceptual knowledge, studied mathematics problems, and then took a similar posttest (McEldoon et al., 2013). Participants were randomly assigned to one of two study conditions: normal study + more practice (More Practice group), or self-explaining (Self-Explain group). The first condition was intended to make time spent learning to be similar for the two groups. You can find part of the data from this study in SelfExplain, the scores being percent correct.
data_selfexplaindata_selfexplain
data_selfexplainA data frame with 52 rows and 4 columns:
factor
factor
numeric
numeric
doi:10.1111/j.2044-8279.2012.02083.x
You're excited! Your company has developed a wonderful new weight-loss program, and now it's your job to develop the ad campaign. Should you choose a BeforeAfter pair of pictures, as in Figure 14.1, top panel? Or might a Progressive sequence of pictures of the same person, as in the bottom panel, be more effective? Pause, think, and discuss. Which would you choose, and why? You might think that BeforeAfter is simpler and more dramatic. On the other hand, Progressive highlights the steady improvement that you claim the program will deliver. You're probably not surprised to learn that BeforeAfter is used often and has long been a favorite of the advertising industry, whereas Progressive is used only rarely. Luca Cian and colleagues (Cian et al., 2020) were curious to know the extent to which BeforeAfter is actually more effective, appealing, and credible than Progressive, or, indeed, whether Progressive might score more highly. They reported seven studies of various aspects of that question. I'll focus on their Study 2, in which they used three independent groups to compare all three conditions illustrated in Figure 14.1. The BeforeAfterInfo condition, in the middle panel, comprises three BeforeAfter pairs, thus providing extra information about the before and after endpoints. The researchers included this condition in case any advantage of Progressive might stem simply from having more images, rather than because it illustrates a clear progressive sequence. They randomly assigned 213 participants from MTurk to one of the three groups. Participants were asked to 'imagine that you have decided to lose some weight', then saw one of the three ads for a weight loss program called MRMDiets. They then answered the question 'How would you evaluate MRMDiets?' by choosing a 1-7 response on several scales, including Unlikeable-Likable, Ineffective-Effective, and Not credible-Credible. The researchers averaged six such scores to give an overall Credibility score, on the 1-7 scale, with 7 being the most credible. Simmons and Nelson (2020) were sufficiently intrigued to carry out two substantial very close replications. With the cooperation of the original researchers, they used the same materials and procedure. They used much larger groups and preregistered their research plan, including data analysis plan. I'll focus on their first replication, in which 761 participants from MTurk were randomized to the three groups.
data_simmonscredibilitydata_simmonscredibility
data_simmonscredibilityA data frame with 3 rows and 4 columns:
factor
numeric
numeric
integer
Is there really such a thing as beauty sleep? To investigate, researchers decided to examine the extent to which sleep relates to attractiveness. Each of 70 college students self-reported the amount of sleep they had the night before. In addition, a photograph was taken of each participant and rated for attractiveness on a scale from 1 to 10 by two judges of the opposite gender. The average rating score was used. You can download this data set (Sleep Beauty) from the book website.
data_sleep_beautydata_sleep_beauty
data_sleep_beautyA data frame with 70 rows and 2 columns:
numeric
numeric
SmithRecall - Ch15 - from Smith et al. (2016)
data_smithrecalldata_smithrecall
data_smithrecallA data frame with 120 rows and 6 columns:
integer
factor
factor
numeric
numeric
factor
Stickgold et al. (2000) found that, remarkably, performance on a visual discrimination task actually improved over the 48-96 hours after initial training, even without practice during that time. However, what if participants were sleep deprived during that period? They trained 11 participants in that new skill, then all were sleep deprived. The data were (-14.7, -10.7, -10.7, 2.2, 2.4, 4.5, 7.2, 9.6, 10, 21.3, 21.8)-or download the Stickgold data set from the book website. The data are the changes in performance scores from immediately after training to after the night without sleep: 0 represents no change, positive scores represent improvement, and negative scores represent decline. Data set courtesy of DataCrunch (tiny.cc/Stickgold)
data_stickgolddata_stickgold
data_stickgoldA data frame with 11 rows and 3 columns:
numeric
factor
factor
https://www.statcrunch.com/app/index.html?dataid=1053539
To what extent does study strategy influence learning? To investigate, psychology students were randomly assigned to three groups and asked to learn biology facts using one of three different strategies: a) Self-Explain (explaining for each fact what new knowledge is gained and how it relates to what is already known), b) Elab Interrogation (elaborative interrogation: stating for each fact why it makes sense), or c) Repetition Control (stating each fact over and over). After studying, students took a 25-point fill-the-blank test (O'Reilly et al., 1998)
data_studystrategiesdata_studystrategies
data_studystrategiesA data frame with 3 rows and 10 columns:
factor
numeric
numeric
integer
numeric
numeric
integer
numeric
numeric
integer
Summary data from an unpublished study by Neil Thomason and colleagues, who were interested in ways to enhance students' critical thinking. They were investigating argument mapping, which is a promising way to use diagrams to represent the structure of arguments. Students in their study completed an established test of critical thinking (the Pretest), then a critical thinking course based on argument mapping, then a second version of the test (the Posttest).
data_thomason_1data_thomason_1
data_thomason_1A data frame with 12 rows and 3 columns:
factor
numeric
numeric
Video games can be violent and they can also be challenging. To what extent might these factors cause aggressive behavior? To explore, Hilgard (2015) asked male participants to play one of four versions of a video game for 15 minutes. The game was customized so that it could vary in violence (shooting zombies or helping aliens) and difficulty (targets controlled by tough AI or dumb AI). After the game, players were provoked by being given an insulting evaluation by a confederate. Participants then got to decide how long the confederate should hold their hand in painfully cold ice water (between 0 and 80 seconds), and this was taken as a measure of aggressive behavior. You can find the materials and analysis plan for this study on the Open Science Framework: osf. io/cwenz. This is a simplified version of the full data set.
data_videogameaggressiondata_videogameaggression
data_videogameaggressionA data frame with 223 rows and 3 columns:
factor
factor
numeric
esci_plot_difference_axis_x can be used to redraw the
difference axis from a forest plot created with plot_meta.
You must pass the plot returned from plot_meta and the effect size
table containing the estimated difference.
esci_plot_difference_axis_x( myplot, difference_table, dlim = c(NA, NA), d_n.breaks = NULL, d_lab = NULL )esci_plot_difference_axis_x( myplot, difference_table, dlim = c(NA, NA), d_n.breaks = NULL, d_lab = NULL )
myplot |
required ggplot2 plot returned from a plot_meta function |
difference_table |
required data frame from an esci-estimate that has a difference-based effect size |
dlim |
Optional 2-item vector to provide the lower and upper boundaries of the difference axis. Defaults to c(NA, NA) which is to auto-set both boundaries. |
d_n.breaks |
Optional numeric > 2 to suggest number of breaks for the difference axis; defaults to NULL in which case number of breaks is handled automatically by ggplot |
d_lab |
Optional character to serve as the label for the difference axis; defaults to NULL |
estimate_magnitude is suitable for a single group design with a
continuous outcome variable. It estimates the population mean and
population median (raw data only) with confidence intervals. You can
pass raw data or summary data.
estimate_magnitude( data = NULL, outcome_variable = NULL, mean = NULL, sd = NULL, n = NULL, outcome_variable_name = "My outcome variable", conf_level = 0.95, save_raw_data = TRUE )estimate_magnitude( data = NULL, outcome_variable = NULL, mean = NULL, sd = NULL, n = NULL, outcome_variable_name = "My outcome variable", conf_level = 0.95, save_raw_data = TRUE )
data |
For raw data - A data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, or a vector of numeric data |
mean |
For summary data - A numeric representing the mean of the outcome variable |
sd |
For summary data - A numeric > 0, standard deviation of the outcome variable |
n |
For summary data - An integer > 0, sample size of the outcome variable |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function in place of a one-sample t-test or z-test.
Once you generate an estimate with this function, you can visualize
it with plot_magnitude().
If you want to compare your sample to a known value or reference, then
use estimate_mdiff_one().
The estimated mean is from statpsych::ci.mean1() (renamed ci.mean as of
statpsych 1.6).
The estimated median is from statpsych::ci.median1() (renamed ci.median
as of statpsych 1.6)
Returns an object of class esci_estimate
overview
outcome_variable_name -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
es_mean
outcome_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
es_median
outcome_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
raw_data
grouping_variable -
outcome_variable -
# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == "Pen", ], outcome_variable = transcription ) # To visualize the estimate myplot_from_raw <- esci::plot_magnitude( estimate_from_raw, effect_size = "median" ) # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate_from_summary <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) # To visualize the estimate myplot_from_summary <- esci::plot_magnitude( estimate_from_summary, effect_size = "mean" )# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == "Pen", ], outcome_variable = transcription ) # To visualize the estimate myplot_from_raw <- esci::plot_magnitude( estimate_from_raw, effect_size = "median" ) # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate_from_summary <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) # To visualize the estimate myplot_from_summary <- esci::plot_magnitude( estimate_from_summary, effect_size = "mean" )
Returns object
estimate_mdiff_2x2_between is suitable for a 2x2 between-subjects design
with a continuous outcome variable. It estimates each main effect, the
simple effects for the first factor, and the interaction. It can express
these estimates as mean differences, standardized mean differences (Cohen's
d), and as median differences (raw data only). You can pass raw data or
or summary data (summary data does not return medians).
estimate_mdiff_2x2_between( data = NULL, outcome_variable = NULL, grouping_variable_A = NULL, grouping_variable_B = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_A_levels = NULL, grouping_variable_B_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_A_name = "A", grouping_variable_B_name = "A", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )estimate_mdiff_2x2_between( data = NULL, outcome_variable = NULL, grouping_variable_A = NULL, grouping_variable_B = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_A_levels = NULL, grouping_variable_B_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_A_name = "A", grouping_variable_B_name = "A", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )
data |
For raw data - a data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, or a vector of numeric data |
grouping_variable_A |
For raw data - The column name of the grouping variable, or a vector of group names, only 2 levels allowed |
grouping_variable_B |
For raw data - The column name of the grouping variable, or a vector of group names, only 2 levels allowed |
means |
For summary data - A vector of 4 means: A1B1, A1B2, A2B1, A2B2 |
sds |
For summary data - A vector of 4 standard deviations, same order |
ns |
For summary data - A vector of 4 sample sizes |
grouping_variable_A_levels |
For summary data - An optional vector of 2 group labels |
grouping_variable_B_levels |
For summary data - An optional vector of 2 group labels |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
grouping_variable_A_name |
Optional friendly name for the grouping variable. Defaults to 'A' or the grouping variable column name if a data.frame is passed. |
grouping_variable_B_name |
Optional friendly name for the grouping variable. Defaults to 'A' or the grouping variable column name if a data.frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
assume_equal_variance |
Defaults to FALSE |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function in place of a 2x2 between-subjects ANOVA.
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can visualize the interaction
specifically with plot_interaction(). You can test hypotheses
with test_mdiff().
The estimated mean differences are from statpsych::ci.2x2.mean.bs().
The estimated SMDs are from statpsych::ci.2x2.stdmean.bs().
The estimated median differences are from statpsych::ci.2x2.median.bs()
Returns object of class esci_estimate
es_mean_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
effect_type -
effects_complex -
es_median_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
ta_LL -
ta_UL -
effect_type -
effects_complex -
es_smd
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
numerator -
denominator -
SE -
df -
d_biased -
effect_type -
effects_complex -
overview
outcome_variable_name -
grouping_variable_name -
grouping_variable_level -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
raw_data
grouping_variable -
outcome_variable -
grouping_variable_A -
grouping_variable_B -
data("data_videogameaggression") estimates_from_raw <- esci::estimate_mdiff_2x2_between( esci::data_videogameaggression, Agression, Violence, Difficulty ) # To visualize the estimated mean difference for the interaction myplot_from_raw <- esci::plot_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c("Evening", "Morning") grouping_variable_B_levels <- c("Sleep", "No Sleep") estimates_from_summary <- esci::estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = "Testing Time", grouping_variable_B_name = "Rest", outcome_variable_name = "False Memory Score", assume_equal_variance = TRUE ) # To visualize the estimated mean difference for the interaction plot_mdiff_interaction <- esci::plot_mdiff( estimates_from_summary$interaction, effect_size = "mean" ) # To visualize the interaction as a line plot plot_interaction_line <- esci::plot_interaction(estimates_from_summary) # Same but with fan effect representing each simple-effect CI plot_interaction_line_CI <- esci::plot_interaction( estimates_from_summary, show_CI = TRUE ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_summary$interaction, effect_size = "mean" )data("data_videogameaggression") estimates_from_raw <- esci::estimate_mdiff_2x2_between( esci::data_videogameaggression, Agression, Violence, Difficulty ) # To visualize the estimated mean difference for the interaction myplot_from_raw <- esci::plot_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c("Evening", "Morning") grouping_variable_B_levels <- c("Sleep", "No Sleep") estimates_from_summary <- esci::estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = "Testing Time", grouping_variable_B_name = "Rest", outcome_variable_name = "False Memory Score", assume_equal_variance = TRUE ) # To visualize the estimated mean difference for the interaction plot_mdiff_interaction <- esci::plot_mdiff( estimates_from_summary$interaction, effect_size = "mean" ) # To visualize the interaction as a line plot plot_interaction_line <- esci::plot_interaction(estimates_from_summary) # Same but with fan effect representing each simple-effect CI plot_interaction_line_CI <- esci::plot_interaction( estimates_from_summary, show_CI = TRUE ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_summary$interaction, effect_size = "mean" )
Returns object
estimate_mdiff_2x2_mixed is suitable for a 2x2 mixed-factorial design
with a continuous outcome variable. It estimates each main effect, the
simple effects for the repeated-measures factor, and the interaction.
It can express these estimates as mean differences, median difference,
or standardized mean differences. This function accepts raw data only.
estimate_mdiff_2x2_mixed( data, outcome_variable_level1, outcome_variable_level2, grouping_variable, outcome_variable_name = "My outcome variable", repeated_measures_name = "Time", conf_level = 0.95, save_raw_data = TRUE )estimate_mdiff_2x2_mixed( data, outcome_variable_level1, outcome_variable_level2, grouping_variable, outcome_variable_name = "My outcome variable", repeated_measures_name = "Time", conf_level = 0.95, save_raw_data = TRUE )
data |
For raw data - a dataframe or tibble |
outcome_variable_level1 |
The column name of the outcome variable for level 1 of the repeated-measures factor |
outcome_variable_level2 |
The column name of the outcome variable for level 2 of the repeated-measures factor |
grouping_variable |
The column name of the grouping variable; only 2 levels allowed; must be a factor |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
repeated_measures_name |
Optional friendly name for the repeated measures factor. Defaults to 'Time' |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function in place of a 2x2 mixed-factorial ANOVA.
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can visualize the interaction
specifically with plot_interaction(). You can test hypotheses
with test_mdiff().
The estimated mean differences are from statpsych::ci.2x2.mean.mixed().
Returns object of class esci_estimate
es_mean_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
effect_type -
effects_complex -
t -
p -
es_smd
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
numerator -
denominator -
SE -
df -
d_biased -
effect_type -
effects_complex -
overview
outcome_variable_name -
grouping_variable_name -
grouping_variable_level -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
raw_data
grouping_variable -
outcome_variable -
grouping_variable_A -
grouping_variable_B -
paired -
# From raw data (summary data mode not available for this function) example_data <- data.frame( pretest = c( 19, 18, 19, 20, 17, 16, 16, 10, 12, 9, 13, 15 ), posttest = c( 18, 19, 20, 17, 20, 16, 19, 16, 16, 14, 16, 18 ), condition = as.factor( c( rep("Control", times = 6), rep("Treated", times = 6) ) ) ) estimates <- esci::estimate_mdiff_2x2_mixed( data = example_data, outcome_variable_level1 = pretest, outcome_variable_level2 = posttest, grouping_variable = condition, repeated_measures_name = "Time" ) # To visualize the estimated mean difference for the interaction myplot <- esci::plot_mdiff(estimates$interaction, effect_size = "mean") # Line-plot of the interaction with fan effect representing each simple-effect CI plot_interaction_line_CI <- esci::plot_interaction( estimates, show_CI = TRUE ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimates$interaction, effect_size = "mean" )# From raw data (summary data mode not available for this function) example_data <- data.frame( pretest = c( 19, 18, 19, 20, 17, 16, 16, 10, 12, 9, 13, 15 ), posttest = c( 18, 19, 20, 17, 20, 16, 19, 16, 16, 14, 16, 18 ), condition = as.factor( c( rep("Control", times = 6), rep("Treated", times = 6) ) ) ) estimates <- esci::estimate_mdiff_2x2_mixed( data = example_data, outcome_variable_level1 = pretest, outcome_variable_level2 = posttest, grouping_variable = condition, repeated_measures_name = "Time" ) # To visualize the estimated mean difference for the interaction myplot <- esci::plot_mdiff(estimates$interaction, effect_size = "mean") # Line-plot of the interaction with fan effect representing each simple-effect CI plot_interaction_line_CI <- esci::plot_interaction( estimates, show_CI = TRUE ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimates$interaction, effect_size = "mean" )
Returns object
estimate_mdiff_ind_contrast is suitable for a multi-group design
(between subjects) with a continuous outcome variable. It accepts
a user-defined set of contrast weights that allows estimation of any
1-df contrast. It can express estimates as mean differences, standardized
mean differences (Cohen's d) or median differences (raw data only). You can
pass raw data or summary data.
estimate_mdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, contrast = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )estimate_mdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, contrast = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )
data |
For raw data - a data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, or a vector of numeric data |
grouping_variable |
For raw data - The column name of the grouping variable, or a vector of group names |
means |
For summary data - A vector of 2 or more means |
sds |
For summary data - A vector of standard deviations, same length as means |
ns |
For summary data - A vector of sample sizes, same length as means |
contrast |
A vector of group weights, same length as number of groups. |
grouping_variable_levels |
For summary data - An optional vector of group labels, same length as means |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
grouping_variable_name |
Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
assume_equal_variance |
Defaults to FALSE |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function in place of a one-way ANOVA.
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can test hypotheses with
test_mdiff().
The estimated mean differences are from statpsych::ci.lc.mean.bs().
The estimated SMDs are from CI_smd_ind_contrast() which relies
on statpsych::ci.lc.stdmean.bs() unless there are only 2 groups.
The estimated median differences are from statpsych::ci.lc.median.bs()
Returns object of class esci_estimate
es_mean_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
es_median_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
ta_LL -
ta_UL -
es_smd
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
numerator -
denominator -
SE -
df -
d_biased -
overview
outcome_variable_name -
grouping_variable_name -
grouping_variable_level -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
raw_data
grouping_variable -
outcome_variable -
# From raw data data("data_rattanmotivation") estimate_from_raw <- esci::estimate_mdiff_ind_contrast( esci::data_rattanmotivation, Motivation, Group, contrast = c("Challenge" = 1, "Control" = -1/2, "Comfort" = -1/2) ) # To visualize the estimate myplot_from_raw <- esci::plot_mdiff( estimate_from_raw, effect_size = "median" ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median" ) # From summary data data("data_halagappa") estimate_from_summary <- estimate_mdiff_ind_contrast( means = data_halagappa$Mean, sds = data_halagappa$SD, ns = data_halagappa$n, grouping_variable_levels = as.character(data_halagappa$Groups), assume_equal_variance = TRUE, contrast = c( "NFree10" = 1/3, "AFree10" = 1/3, "ADiet10" = -1/3, "NFree17" = -1/3, "AFree17" = 1/3, "ADiet17" = -1/3 ), grouping_variable_name = "Diet", outcome_variable_name = "% time near target" ) # To visualize the estimate myplot <- esci::plot_mdiff(estimate_from_summary, effect_size = "mean") # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_summary, effect_size = "mean" )# From raw data data("data_rattanmotivation") estimate_from_raw <- esci::estimate_mdiff_ind_contrast( esci::data_rattanmotivation, Motivation, Group, contrast = c("Challenge" = 1, "Control" = -1/2, "Comfort" = -1/2) ) # To visualize the estimate myplot_from_raw <- esci::plot_mdiff( estimate_from_raw, effect_size = "median" ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median" ) # From summary data data("data_halagappa") estimate_from_summary <- estimate_mdiff_ind_contrast( means = data_halagappa$Mean, sds = data_halagappa$SD, ns = data_halagappa$n, grouping_variable_levels = as.character(data_halagappa$Groups), assume_equal_variance = TRUE, contrast = c( "NFree10" = 1/3, "AFree10" = 1/3, "ADiet10" = -1/3, "NFree17" = -1/3, "AFree17" = 1/3, "ADiet17" = -1/3 ), grouping_variable_name = "Diet", outcome_variable_name = "% time near target" ) # To visualize the estimate myplot <- esci::plot_mdiff(estimate_from_summary, effect_size = "mean") # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_summary, effect_size = "mean" )
Returns object
estimate_mdiff_one is suitable for a single-group design
with a continuous outcome variable that is compared to a reference
or population value. It can express estimates as mean differences,
standardized mean differences (Cohen's d) or median differences
(raw data only). You can pass raw data or summary data.
estimate_mdiff_one( data = NULL, outcome_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = 0, outcome_variable_name = "My outcome variable", conf_level = 0.95, save_raw_data = TRUE )estimate_mdiff_one( data = NULL, outcome_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = 0, outcome_variable_name = "My outcome variable", conf_level = 0.95, save_raw_data = TRUE )
data |
For raw data - a data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, or a vector of numeric data |
comparison_mean |
For summary data, a numeric |
comparison_sd |
For summary data, numeric > 0 |
comparison_n |
For summary data, a numeric integer > 0 |
reference_mean |
Reference value, defaults to 0 |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function in place of a z-test or one-sample t-test.
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can test hypotheses with
test_mdiff().
The estimated mean differences are from statpsych::ci.mean1() (renamed
ci.mean as of statpsych 1.6).
The estimated SMDs are from CI_smd_one().
The estimated median differences are from statpsych::ci.median1() (renamed
ci.median as of statpsych 1.6)
Returns object of class esci_estimate
overview
outcome_variable_name -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
es_mean
outcome_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
es_median
outcome_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
raw_data
grouping_variable -
outcome_variable -
es_mean_difference
outcome_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
type -
es_median_difference
outcome_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
type -
es_smd
outcome_variable_name -
effect -
effect_size -
LL -
UL -
numerator -
denominator -
SE -
df -
d_biased -
# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_mdiff_one( data = data_penlaptop1[data_penlaptop1$condition == "Pen", ], outcome_variable = transcription, reference_mean = 10 ) # To visualize the mean difference estimate myplot_from_raw <- esci::plot_mdiff(estimate_from_raw, effect_size = "mean") # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "mean", rope = c(-2, 2) ) # From summary data mymean <- 12.09 mysd <- 5.52 myn <- 103 estimate_from_summary <- esci::estimate_mdiff_one( comparison_mean = mymean, comparison_sd = mysd, comparison_n = myn, reference_mean = 12 ) # To visualize the estimate myplot_from_sumary <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_mdiff_one( data = data_penlaptop1[data_penlaptop1$condition == "Pen", ], outcome_variable = transcription, reference_mean = 10 ) # To visualize the mean difference estimate myplot_from_raw <- esci::plot_mdiff(estimate_from_raw, effect_size = "mean") # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "mean", rope = c(-2, 2) ) # From summary data mymean <- 12.09 mysd <- 5.52 myn <- 103 estimate_from_summary <- esci::estimate_mdiff_one( comparison_mean = mymean, comparison_sd = mysd, comparison_n = myn, reference_mean = 12 ) # To visualize the estimate myplot_from_sumary <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )
Returns object
estimate_mdiff_paired is suitable for a simple paired design
with a continuous outcome variable. It provides estimates and CIs for the
population mean difference between the repeated measures, the standardized
mean difference (SMD; Cohen's d) between the repeated measures, and the
median difference between the repeated measures (raw data only). You can
pass raw data or summary data.
estimate_mdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, comparison_mean = NULL, comparison_sd = NULL, reference_mean = NULL, reference_sd = NULL, n = NULL, correlation = NULL, comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 0.95, save_raw_data = TRUE )estimate_mdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, comparison_mean = NULL, comparison_sd = NULL, reference_mean = NULL, reference_sd = NULL, n = NULL, correlation = NULL, comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 0.95, save_raw_data = TRUE )
data |
For raw data - a data frame or tibble |
comparison_measure |
For raw data - The column name of comparison measure of the outcome variable, or a vector of numeric data |
reference_measure |
For raw data - The column name of the reference measure of the outcome variable, or a vector of numeric data |
comparison_mean |
For summary data, a numeric |
comparison_sd |
For summary data, numeric > 0 |
reference_mean |
For summary data, a numeric |
reference_sd |
For summary data, numeric > 0 |
n |
For summary data, a numeric integer > 0 |
correlation |
For summary data, correlation between measures, a numeric that is > -1 and < 1 |
comparison_measure_name |
For summary data - An optional character label for the comparison measure. Defaults to 'Comparison measure' |
reference_measure_name |
For summary data - An optional character label for the reference measure. Defaults to 'Reference measure' |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function in place of a paired-samples t-test.
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can test hypotheses with
test_mdiff().
The estimated mean differences are from statpsych::ci.mean.ps().
The estimated SMDs are from CI_smd_ind_contrast().
The estimated median differences are from statpsych::ci.median.ps().
Returns object of class esci_estimate
overview
outcome_variable_name -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
es_mean_difference
type -
comparison_measure_name -
reference_measure_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
es_smd
comparison_measure_name -
reference_measure_name -
effect -
effect_size -
LL -
UL -
numerator -
denominator -
SE -
d_biased -
df -
es_r
x_variable_name -
y_variable_name -
effect -
effect_size -
LL -
UL -
SE -
n -
df -
ta_LL -
ta_UL -
es_median_difference
type -
comparison_measure_name -
reference_measure_name -
effect -
effect_size -
LL -
UL -
SE -
ta_LL -
ta_UL -
es_mean_ratio
comparison_measure_name -
reference_measure_name -
effect -
effect_size -
LL -
UL -
comparison_mean -
reference_mean -
es_median_ratio
comparison_measure_name -
reference_measure_name -
effect -
effect_size -
LL -
UL -
comparison_median -
reference_median -
raw_data
comparison_measure -
reference_measure -
# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_mdiff_paired( data = esci::data_thomason_1, comparison_measure = Posttest, reference_measure = Pretest ) # To visualize the estimated median difference (raw data only) myplot_from_raw <- esci::plot_mdiff(estimate_from_raw, effect_size = "median") # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median", rope = c(-2, 2) ) sd1 <- 4.28 sd2 <- 3.4 sdiff <- 2.13 cor <- (sd1^2 + sd2^2 - sdiff^2) / (2*sd1*sd2) estimate_from_summary <- esci::estimate_mdiff_paired( comparison_mean = 14.25, comparison_sd = 4.28, reference_mean = 12.88, reference_sd = 3.4, n = 16, correlation = 0.87072223749, comparison_measure_name = "After", reference_measure_name = "Before" ) # To visualize the estimated mean difference myplot_from_summary <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_mdiff_paired( data = esci::data_thomason_1, comparison_measure = Posttest, reference_measure = Pretest ) # To visualize the estimated median difference (raw data only) myplot_from_raw <- esci::plot_mdiff(estimate_from_raw, effect_size = "median") # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median", rope = c(-2, 2) ) sd1 <- 4.28 sd2 <- 3.4 sdiff <- 2.13 cor <- (sd1^2 + sd2^2 - sdiff^2) / (2*sd1*sd2) estimate_from_summary <- esci::estimate_mdiff_paired( comparison_mean = 14.25, comparison_sd = 4.28, reference_mean = 12.88, reference_sd = 3.4, n = 16, correlation = 0.87072223749, comparison_measure_name = "After", reference_measure_name = "Before" ) # To visualize the estimated mean difference myplot_from_summary <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )
Returns object
estimate_mdiff_two is suitable for a simple two-group design
with a continuous outcome variable. It provides estimates and CIs for the
population mean difference between the repeated measures, the standardized
mean difference (SMD; Cohen's d) between the repeated measures, and the
median difference between the repeated measures (raw data only). You can
pass raw data or summary data.
estimate_mdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = NULL, reference_sd = NULL, reference_n = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE, switch_comparison_order = FALSE )estimate_mdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = NULL, reference_sd = NULL, reference_n = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE, switch_comparison_order = FALSE )
data |
For raw data - a data.frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, or a vector of numeric data |
grouping_variable |
For raw data - The column name of the grouping variable, or a vector of group names |
comparison_mean |
For summary data, a numeric |
comparison_sd |
For summary data, numeric > 0 |
comparison_n |
For summary data, a numeric integer > 0 |
reference_mean |
For summary data, a numeric |
reference_sd |
For summary data, numeric > 0 |
reference_n |
For summary data, a numeric integer > 0 |
grouping_variable_levels |
For summary data - An optional vector of 2 group labels |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
grouping_variable_name |
Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
assume_equal_variance |
Defaults to FALSE |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
switch_comparison_order |
Defaults to FALSE |
Reach for this function in place of an independent-samples t-test.
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can test hypotheses with
test_mdiff().
The estimated mean differences are from statpsych::ci.mean2().
The estimated SMDs are from CI_smd_ind_contrast().
The estimated median differences are from statpsych::ci.median2().
Returns object of class esci_estimate
es_mean_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
df -
ta_LL -
ta_UL -
es_median_difference
type -
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
ta_LL -
ta_UL -
es_smd
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
numerator -
denominator -
SE -
df -
d_biased -
es_mean_ratio
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
comparison_mean -
reference_mean -
es_median_ratio
outcome_variable_name -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
comparison_median -
reference_median -
overview
outcome_variable_name -
grouping_variable_name -
grouping_variable_level -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
raw_data
grouping_variable -
outcome_variable -
# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # To visualize the estimated median difference (raw data only) myplot_from_raw <- esci::plot_mdiff( estimate_from_raw, effect_size = "median" ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median", rope = c(-2, 2) ) # From summary data estimate_from_summary <- esci::estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c("Ref-Laptop", "Comp-Pen"), outcome_variable_name = "% Transcription", grouping_variable_name = "Note-taking type", assume_equal_variance = TRUE ) # To visualize the estimated mean difference myplot <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # To visualize the estimated median difference (raw data only) myplot_from_raw <- esci::plot_mdiff( estimate_from_raw, effect_size = "median" ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median", rope = c(-2, 2) ) # From summary data estimate_from_summary <- esci::estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c("Ref-Laptop", "Comp-Pen"), outcome_variable_name = "% Transcription", grouping_variable_name = "Note-taking type", assume_equal_variance = TRUE ) # To visualize the estimated mean difference myplot <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )
Returns object
estimate_pdiff_ind_contrast is suitable for a multi-group design
(between subjects) with a categorical outcome variable. It accepts
a user-defined set of contrast weights that allows estimation of any
1-df contrast. It can express estimates as a difference in proportions
and as an odds ratio (2-group designs only). You can pass raw data or
summary data.
estimate_pdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, ns = NULL, contrast = NULL, case_label = 1, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, count_NA = FALSE )estimate_pdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, ns = NULL, contrast = NULL, case_label = 1, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, count_NA = FALSE )
data |
For raw data - a data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable which is a factor, or a vector that is a factor |
grouping_variable |
For raw data - The column name of the grouping variable which is a factor, or a vector that is a factor |
cases |
For summary data - A numeric vector of 2 or more event counts, each an integer >= 0 |
ns |
For summary data - A numeric vector of sample sizes, same length as counts, each an integer >= corresponding event count |
contrast |
A vector of group weights, same length as number of groups. |
case_label |
An optional numeric or character label For summary data, used as the label and defaults to 'Affected'. For raw data, used to specify the level used for the proportion. |
grouping_variable_levels |
For summary data - An optional vector of group labels, same length as cases |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
grouping_variable_name |
Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
count_NA |
Logical to count NAs (TRUE) in total N or not (FALSE) |
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can test hypotheses with
test_mdiff().
The estimated proportion differences are from statpsych::ci.lc.prop.bs().
The estimated odds ratios (if returned) are from statpsych::ci.oddsratio().
Returns object of class esci_estimate
es_proportion_difference
type -
outcome_variable_name -
case_label -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
effect_size_adjusted -
ta_LL -
ta_UL -
es_odds_ratio
outcome_variable_name -
case_label -
grouping_variable_name -
effect -
effect_size -
SE -
LL -
UL -
ta_LL -
ta_UL -
overview
grouping_variable_name -
grouping_variable_level -
outcome_variable_name -
outcome_variable_level -
cases -
n -
P -
P_LL -
P_UL -
P_SE -
P_adjusted -
ta_LL -
ta_UL -
es_phi
grouping_variable_name -
outcome_variable_name -
effect -
effect_size -
SE -
LL -
UL -
# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_ind_contrast( esci::data_campus_involvement, CommuterStatus, Gender, contrast = c("Male" = -1, "Female" = 1) ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_pdiff_ind_contrast( cases = c(78, 10), ns = c(252, 20), case_label = "egocentric", grouping_variable_levels = c("Original", "Replication"), contrast = c(-1, 1), conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_ind_contrast( esci::data_campus_involvement, CommuterStatus, Gender, contrast = c("Male" = -1, "Female" = 1) ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_pdiff_ind_contrast( cases = c(78, 10), ns = c(252, 20), case_label = "egocentric", grouping_variable_levels = c("Original", "Replication"), contrast = c(-1, 1), conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)
Returns object
estimate_pdiff_one is suitable for a single-group design
(between subjects) with a categorical outcome variable. It calculates
the effect sizes with respect to a reference or population proportion
(default value of 0). It returns the estimated difference between the
in proportion from the reference/population value. You can pass raw data or
summary data.
estimate_pdiff_one( data = NULL, outcome_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_p = 0, case_label = 1, outcome_variable_name = "My outcome variable", conf_level = 0.95, count_NA = FALSE )estimate_pdiff_one( data = NULL, outcome_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_p = 0, case_label = 1, outcome_variable_name = "My outcome variable", conf_level = 0.95, count_NA = FALSE )
data |
For raw data - a dataframe or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, which must be a factor, or a vector that is a factor |
comparison_cases |
For summary data, a numeric integer > 0 |
comparison_n |
For summary data, a numeric integer >= count |
reference_p |
Reference proportion, numeric >=0 and <=1 |
case_label |
An optional numeric or character label for the count level. |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
count_NA |
Logical to count NAs (TRUE) in total N or not (FALSE) |
Once you generate an estimate with this function, you can visualize
it with plot_pdiff() and you can test hypotheses with
test_pdiff().
The estimated proportion differences are from statpsych::ci.prop1() (renamed
ci.prop as of statpsych 1.6).
Returns an object of class esci_estimate
overview
outcome_variable_name -
outcome_variable_level -
cases -
n -
P -
P_LL -
P_UL -
P_SE -
P_adjusted -
ta_LL -
ta_UL -
es_proportion_difference
outcome_variable_name -
case_label -
effect -
effect_size -
LL -
UL -
SE -
effect_size_adjusted -
ta_LL -
ta_UL -
cases -
n -
type -
# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_one( esci::data_campus_involvement, CommuterStatus, reference_p = 0.50 ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_pdiff_one( comparison_cases = 8, comparison_n = 22, reference_p = 0.5 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_one( esci::data_campus_involvement, CommuterStatus, reference_p = 0.50 ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_pdiff_one( comparison_cases = 8, comparison_n = 22, reference_p = 0.5 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)
Returns object
estimate_pdiff_paired is suitable for a simple paired design
with a categorical outcome variable. It provides estimates and CIs for the
population proportion difference between the repeated measures. You can
pass raw data or summary data.
estimate_pdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, cases_consistent = NULL, cases_inconsistent = NULL, not_cases_consistent = NULL, not_cases_inconsistent = NULL, case_label = 1, not_case_label = NULL, comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 0.95, count_NA = FALSE )estimate_pdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, cases_consistent = NULL, cases_inconsistent = NULL, not_cases_consistent = NULL, not_cases_inconsistent = NULL, case_label = 1, not_case_label = NULL, comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 0.95, count_NA = FALSE )
data |
For raw data - a data.frame or tibble |
comparison_measure |
For raw data - The comparison measure, a factor. Can be the column name of a data frame of a vector. |
reference_measure |
For raw data - The reference measure, a factor. Can be the column name of a data frame of a vector. |
cases_consistent |
Count of cases in measure 1 that are also cases at measure 2; measure 1 = 0, measure 2 = 0; cell 0_0 |
cases_inconsistent |
Count of cases in measure 1 that are not cases at measure 2; measure 1 = 0, measure 2 = 1; cell 0_1 |
not_cases_consistent |
Count of not cases in measure 1 that are also not cases at measure 2; measure 1 = 1, measure 2 = 1, cell 1_1 |
not_cases_inconsistent |
Count of not cases in measure 1 that are not cases at measure 2; measure 1 = 1, measure 2 = 0, cell 1_0 |
case_label |
An optional numeric or character label for the case level. |
not_case_label |
An optional numeric or character label for the not case level. |
comparison_measure_name |
For summary data - An optional character label for the comparison measure. Defaults to 'Comparison measure' |
reference_measure_name |
For summary data - An optional character label for the reference measure. Defaults to 'Reference measure' |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
count_NA |
Logical to count NAs (TRUE) in total N or not (FALSE) |
Once you generate an estimate with this function, you can visualize
it with plot_pdiff() and you can test hypotheses with
test_pdiff().
The estimated proportion differences are from statpsych::ci.prop.ps().
Returns object of class esci_estimate
# From summary data # Example 1 from Bonett & Price, 2012 estimate_from_summary <- esci::estimate_pdiff_paired( cases_consistent = 60, cases_inconsistent = 50, not_cases_inconsistent = 22, not_cases_consistent = 68, case_label = "Answered True", not_case_label = "Answered False", reference_measure_name = "9th grade", comparison_measure_name = "12th grade", conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)# From summary data # Example 1 from Bonett & Price, 2012 estimate_from_summary <- esci::estimate_pdiff_paired( cases_consistent = 60, cases_inconsistent = 50, not_cases_inconsistent = 22, not_cases_consistent = 68, case_label = "Answered True", not_case_label = "Answered False", reference_measure_name = "9th grade", comparison_measure_name = "12th grade", conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)
Returns object
estimate_pdiff_two is suitable for a simple two-group design
with a categorical outcome variable. It provides estimates and CIs for the
difference in proportions between the two groups, the odds ratio, and phi.
You can pass raw data or summary data.
estimate_pdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_cases = NULL, reference_n = NULL, case_label = 1, not_case_label = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, count_NA = FALSE )estimate_pdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_cases = NULL, reference_n = NULL, case_label = 1, not_case_label = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My outcome variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, count_NA = FALSE )
data |
For raw data - a data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable which is a factor, or a vector that is a factor |
grouping_variable |
For raw data - The column name of the grouping variable which is a factor, or a vector that is a factor |
comparison_cases |
For summary data, a numeric integer >= 0 |
comparison_n |
For summary data, a numeric integer >= comparison_events |
reference_cases |
For summary data, a numeric integer >= 0 |
reference_n |
For summary data, a numeric integer >= reference_events |
case_label |
An optional numeric or character label for the case level. |
not_case_label |
An optional numeric or character label for the not case level. |
grouping_variable_levels |
For summary data - An optional vector of 2 group labels |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
grouping_variable_name |
Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
count_NA |
Logical to count NAs (TRUE) in total N or not (FALSE) |
Once you generate an estimate with this function, you can visualize
it with plot_mdiff() and you can test hypotheses with
test_mdiff().
The estimated mean differences are from statpsych::ci.prop2().
The estimated odds ratio is from statpsych::ci.oddsratio().
The estimated correlation (phi) is from statpsych::ci.phi().
Returns object of class esci_estimate
es_proportion_difference
type -
outcome_variable_name -
case_label -
grouping_variable_name -
effect -
effect_size -
LL -
UL -
SE -
effect_size_adjusted -
ta_LL -
ta_UL -
es_odds_ratio
outcome_variable_name -
case_label -
grouping_variable_name -
effect -
effect_size -
SE -
LL -
UL -
ta_LL -
ta_UL -
overview
grouping_variable_name -
grouping_variable_level -
outcome_variable_name -
outcome_variable_level -
cases -
n -
P -
P_LL -
P_UL -
P_SE -
P_adjusted -
ta_LL -
ta_UL -
es_phi
grouping_variable_name -
outcome_variable_name -
effect -
effect_size -
SE -
LL -
UL -
data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_two( esci::data_campus_involvement, CommuterStatus, Gender ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary_data estimate_from_summary <- esci::estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c("Original", "Replication"), conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) #' # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_two( esci::data_campus_involvement, CommuterStatus, Gender ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary_data estimate_from_summary <- esci::estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c("Original", "Replication"), conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) #' # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)
estimate_proportion is suitable for a single group design with a
categorical outcome variable. It estimates the population proportion
for the frequency of each level of the outcome variable, with confidence
intervals. You can pass raw data or summary data.
estimate_proportion( data = NULL, outcome_variable = NULL, cases = NULL, case_label = 1, outcome_variable_levels = NULL, outcome_variable_name = "My outcome variable", conf_level = 0.95, count_NA = FALSE )estimate_proportion( data = NULL, outcome_variable = NULL, cases = NULL, case_label = 1, outcome_variable_levels = NULL, outcome_variable_name = "My outcome variable", conf_level = 0.95, count_NA = FALSE )
data |
For raw data - a data frame or tibble |
outcome_variable |
For raw data - The column name of the outcome variable, which must be a factor, or a vector that is a factor |
cases |
For summary data - A vector of cases |
case_label |
A numeric or string indicating which level of the factor to estimate. Defaults to 1, meaning first level is analyzed |
outcome_variable_levels |
For summary data - optional vector of 2 characters indicating name of the count level and name of the not count level. Defaults to "Affected" and "Not Affected" |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My outcome variable' or the outcome variable column name if a data frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
count_NA |
Logical to count NAs (TRUE) in total N or not (FALSE) |
Once you generate an estimate with this function, you can visualize
it with plot_proportion().
If you want to compare your estimate to a known value or reference, then
use estimate_pdiff_one().
The estimated proportions are from statpsych::ci.prop1() (renamed
ci.prop as of statpsych 1.6).
Returns an object of class esci_estimate
overview
outcome_variable_name -
outcome_variable_level -
cases -
n -
P -
P_LL -
P_UL -
P_SE -
P_adjusted -
ta_LL -
ta_UL -
es_proportion
outcome_variable_name -
case_label -
effect -
effect_size -
LL -
UL -
SE -
effect_size_adjusted -
ta_LL -
ta_UL -
cases -
n -
# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_proportion( esci::data_campus_involvement, CommuterStatus ) # To visualize the estimate myplot_from_raw <- esci::plot_proportion(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c("Affected", "Not Affected") ) # To visualize the estimate myplot_from_summary<- esci::plot_proportion(estimate_from_summary)# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_proportion( esci::data_campus_involvement, CommuterStatus ) # To visualize the estimate myplot_from_raw <- esci::plot_proportion(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c("Affected", "Not Affected") ) # To visualize the estimate myplot_from_summary<- esci::plot_proportion(estimate_from_summary)
estimate_r is suitable for a design with two continuous
variables. It estimates the linear correlation between two variables
(Pearson's r) with a confidence interval. You can pass raw data or
summary data.
estimate_r( data = NULL, x = NULL, y = NULL, r = NULL, n = NULL, x_variable_name = "My x variable", y_variable_name = "My y variable", conf_level = 0.95, save_raw_data = TRUE )estimate_r( data = NULL, x = NULL, y = NULL, r = NULL, n = NULL, x_variable_name = "My x variable", y_variable_name = "My y variable", conf_level = 0.95, save_raw_data = TRUE )
data |
For raw data - A data frame or tibble |
x |
For raw data - The column name of the outcome variable, or a vector of numeric data |
y |
For raw data - The column name of the outcome variable, or a vector of numeric data |
r |
For summary data - A pearson's r correlation coefficient |
n |
For summary data - Sample size, an integer > 0 |
x_variable_name |
Optional friendly name for the x variable. Defaults to 'My x variable' or the outcome variable column name if a data frame is passed. |
y_variable_name |
Optional friendly name for the y variable. Defaults to 'My y variable' or the outcome variable column name if a data frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Reach for this function to conduct simple linear correlation or simple linear regression.
Once you generate an estimate with this function, you can visualize
it with plot_correlation() and you can test hypotheses with
test_correlation(). In addition, you can use plot_scatter()
to visualize the raw data and to conduct a regression analysis that r
returns predicted Y' values from a given X value.
The estimated correlation is from statpsych::ci.cor(), which uses the
Fisher r-to-z approach.
Returns object of class esci_estimate
overview
outcome_variable_name -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
es_r
x_variable_name -
y_variable_name -
effect -
effect_size -
LL -
UL -
SE -
n -
df -
ta_LL -
ta_UL -
regression
component -
values -
LL -
UL -
raw_data
x -
y -
fit -
lwr -
upr -
# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_r( esci::data_thomason_1, Pretest, Posttest ) # To visualize the value of r myplot_correlation <- esci::plot_correlation(estimate_from_raw) # To visualize the data (scatterplot) and use regression to obtain Y' from X myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_raw <- esci::test_correlation( estimate_from_raw, rope = c(-0.1, 0.1) ) # From summary data estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50) # To visualize the value of r myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_correlation( estimate_from_summary, rope = c(-0.1, 0.1) )# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_r( esci::data_thomason_1, Pretest, Posttest ) # To visualize the value of r myplot_correlation <- esci::plot_correlation(estimate_from_raw) # To visualize the data (scatterplot) and use regression to obtain Y' from X myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_raw <- esci::test_correlation( estimate_from_raw, rope = c(-0.1, 0.1) ) # From summary data estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50) # To visualize the value of r myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_correlation( estimate_from_summary, rope = c(-0.1, 0.1) )
Returns object
estimate_rdiff_two is suitable for a simple two-group design
with two continuous outcome variables where you want to estimate the
difference in the strength of the relationship between the two groups.
It estimate the linear correlation (Pearson's r) for each group and the
difference in r, along with confidence intervals. You can
pass raw data or summary data.
Returns effect sizes appropriate for estimating the linear relationship between two quantitative variables
estimate_rdiff_two( data = NULL, x = NULL, y = NULL, grouping_variable = NULL, comparison_r = NULL, comparison_n = NULL, reference_r = NULL, reference_n = NULL, grouping_variable_levels = NULL, x_variable_name = "My x variable", y_variable_name = "My y variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, save_raw_data = TRUE )estimate_rdiff_two( data = NULL, x = NULL, y = NULL, grouping_variable = NULL, comparison_r = NULL, comparison_n = NULL, reference_r = NULL, reference_n = NULL, grouping_variable_levels = NULL, x_variable_name = "My x variable", y_variable_name = "My y variable", grouping_variable_name = "My grouping variable", conf_level = 0.95, save_raw_data = TRUE )
data |
For raw data - a dataframe or tibble |
x |
For raw data - The column name of the outcome variable, or a vector of numeric data |
y |
For raw data - The column name of the outcome variable, or a vector of numeric data |
grouping_variable |
For raw data, a vector that is a factor or the name of a factor column from data |
comparison_r |
For summary data, a pearson's r correlation coefficient |
comparison_n |
For summary data - An integer > 0 |
reference_r |
For summary data, a pearson's r correlation coefficient |
reference_n |
For summary data - An integer > 0 |
grouping_variable_levels |
For summary data - An optional vector of 2 group labels |
x_variable_name |
Optional friendly name for the x variable. Defaults to 'My x variable' or the outcome variable column name if a data frame is passed. |
y_variable_name |
Optional friendly name for the y variable. Defaults to 'My y variable' or the outcome variable column name if a data frame is passed. |
grouping_variable_name |
Optional friendly name for the grouping variable. Defaults to 'My grouping variable' or the grouping variable column name if a data.frame is passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
save_raw_data |
For raw data; defaults to TRUE; set to FALSE to save memory by not returning raw data in estimate object |
Once you generate an estimate with this function, you can visualize
it with plot_rdiff() and you can test hypotheses with
test_rdiff(). In addition, you can use plot_scatter()
to visualize the raw data.
The estimated single-group r values are from statpsych::ci.cor().
The difference in r values is from statpsych::ci.cor2().
Returns object of class esci_estimate
overview
outcome_variable_name -
grouping_variable_name -
grouping_variable_level -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
es_r_difference
type -
grouping_variable_name -
grouping_variable_level -
x_variable_name -
y_variable_name -
effect -
effect_size -
LL -
UL -
SE -
n -
df -
ta_LL -
ta_UL -
rz -
sem -
z -
p -
es_r
grouping_variable_name -
grouping_variable_level -
x_variable_name -
y_variable_name -
effect -
effect_size -
LL -
UL -
SE -
n -
df -
ta_LL -
ta_UL -
raw_data
x -
y -
grouping_variable -
# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_rdiff_two( esci::data_campus_involvement, GPA, SWB, Gender ) # To visualize the difference in r myplot_from_raw <- esci::plot_rdiff(estimate_from_raw) # To visualize the data (scatterplot) by group myplot_scatter <- esci::plot_scatter(estimate_from_raw) # To evaluate a hypothesis (by default: point null of exaclty 0): res_htest_from_raw <- esci::test_rdiff( estimate_from_raw ) # From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c("Females", "Males"), x_variable_name = "Satisfaction with life", y_variable_name = "Body satisfaction", grouping_variable_name = "Gender", conf_level = .95 ) myplot_from_summary <- esci::plot_rdiff(estimate) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_rdiff( estimate, rope = c(-0.1, 0.1) )# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_rdiff_two( esci::data_campus_involvement, GPA, SWB, Gender ) # To visualize the difference in r myplot_from_raw <- esci::plot_rdiff(estimate_from_raw) # To visualize the data (scatterplot) by group myplot_scatter <- esci::plot_scatter(estimate_from_raw) # To evaluate a hypothesis (by default: point null of exaclty 0): res_htest_from_raw <- esci::test_rdiff( estimate_from_raw ) # From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c("Females", "Males"), x_variable_name = "Satisfaction with life", y_variable_name = "Body satisfaction", grouping_variable_name = "Gender", conf_level = .95 ) myplot_from_summary <- esci::plot_rdiff(estimate) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_rdiff( estimate, rope = c(-0.1, 0.1) )
geom_meta_diamond_h creates a horizontal meta-analytic diamond
defined by an x value (horizontal center of diamond), xmin and xmax
values (for the horizontal ends of the diamond), a y value (for the
vertical placement of the diamond) and a height (for the vertical height
of the diamond). Note the use of xmin and xmax allows for representation
of asymmetric confidence intervals with this geom.
geom_meta_diamond_h( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )geom_meta_diamond_h( mapping = NULL, data = NULL, stat = "identity", position = "identity", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
stat |
The statistical transformation to use on the data for this layer.
When using a
|
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
... |
Other arguments passed to the geom. These are often aesthetics,
used to set an aesthetic to a fixed value, like ##Aesthetics ##
|
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
# example code library(ggplot2) my_effects <- data.frame( effect_size = c(1, 2, 1, 0), UL = c(2, 3, 2, 1), LL = c(0, 1, 0, -1), y = c(1, 2, 3, 4) ) myplot <- ggplot2::ggplot() myplot <- myplot + geom_meta_diamond_h( data = my_effects, ggplot2::aes( x = effect_size, xmin = LL, xmax = UL, y = y ), height = 0.25, color = "black", fill = "red", )# example code library(ggplot2) my_effects <- data.frame( effect_size = c(1, 2, 1, 0), UL = c(2, 3, 2, 1), LL = c(0, 1, 0, -1), y = c(1, 2, 3, 4) ) myplot <- ggplot2::ggplot() myplot <- myplot + geom_meta_diamond_h( data = my_effects, ggplot2::aes( x = effect_size, xmin = LL, xmax = UL, y = y ), height = 0.25, color = "black", fill = "red", )
Correlations: Single Group
jamovicorrelation( switch = "from_raw", data, x, y, r = " ", n = " ", x_variable_name = "X variable", y_variable_name = "Y variable", conf_level = 95, show_details = FALSE, do_regression = FALSE, show_line = FALSE, show_line_CI = FALSE, show_residuals = FALSE, show_PI = FALSE, show_mean_lines = FALSE, show_r = FALSE, plot_as_z = FALSE, predict_from_x = " ", evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "300", es_plot_height = "400", sp_plot_width = "650", sp_plot_height = "650", ymin = "-1", ymax = "1", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", error_layout = "none", sp_ymin = "auto", sp_ymax = "auto", sp_ybreaks = "auto", sp_xmin = "auto", sp_xmax = "auto", sp_xbreaks = "auto", sp_ylab = "auto", sp_xlab = "auto", sp_axis.text.y = "14", sp_axis.title.y = "15", sp_axis.text.x = "14", sp_axis.title.x = "15", shape_summary = "circle filled", color_summary = "#008DF9", fill_summary = "#008DF9", size_summary = "4", alpha_summary = "1", linetype_summary = "solid", color_interval = "black", size_interval = "3", alpha_interval = "1", alpha_error = "1", fill_error = "gray75", sp_shape_raw_reference = "circle filled", sp_color_raw_reference = "black", sp_fill_raw_reference = "#008DF9", sp_size_raw_reference = "3", sp_alpha_raw_reference = ".25", sp_linetype_summary_reference = "solid", sp_color_summary_reference = "#008DF9", sp_size_summary_reference = "3", sp_alpha_summary_reference = ".25", sp_linetype_PI_reference = "dotted", sp_color_PI_reference = "#E20134", sp_size_PI_reference = "2", sp_alpha_PI_reference = "1", sp_linetype_residual_reference = "solid", sp_color_residual_reference = "#E20134", sp_size_residual_reference = "1", sp_alpha_residual_reference = "1", sp_prediction_label = "5", sp_prediction_color = "#E20134", sp_linetype_CI = "solid", sp_color_CI = "#008DF9", sp_size_CI = "4", sp_alpha_CI = "1", sp_linetype_ref = "dotted", sp_color_ref = "gray60", sp_size_ref = "1", sp_alpha_ref = "1", sp_linetype_PI = "solid", sp_color_PI = "#E20134", sp_size_PI = "2", sp_alpha_PI = "1" )jamovicorrelation( switch = "from_raw", data, x, y, r = " ", n = " ", x_variable_name = "X variable", y_variable_name = "Y variable", conf_level = 95, show_details = FALSE, do_regression = FALSE, show_line = FALSE, show_line_CI = FALSE, show_residuals = FALSE, show_PI = FALSE, show_mean_lines = FALSE, show_r = FALSE, plot_as_z = FALSE, predict_from_x = " ", evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "300", es_plot_height = "400", sp_plot_width = "650", sp_plot_height = "650", ymin = "-1", ymax = "1", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", error_layout = "none", sp_ymin = "auto", sp_ymax = "auto", sp_ybreaks = "auto", sp_xmin = "auto", sp_xmax = "auto", sp_xbreaks = "auto", sp_ylab = "auto", sp_xlab = "auto", sp_axis.text.y = "14", sp_axis.title.y = "15", sp_axis.text.x = "14", sp_axis.title.x = "15", shape_summary = "circle filled", color_summary = "#008DF9", fill_summary = "#008DF9", size_summary = "4", alpha_summary = "1", linetype_summary = "solid", color_interval = "black", size_interval = "3", alpha_interval = "1", alpha_error = "1", fill_error = "gray75", sp_shape_raw_reference = "circle filled", sp_color_raw_reference = "black", sp_fill_raw_reference = "#008DF9", sp_size_raw_reference = "3", sp_alpha_raw_reference = ".25", sp_linetype_summary_reference = "solid", sp_color_summary_reference = "#008DF9", sp_size_summary_reference = "3", sp_alpha_summary_reference = ".25", sp_linetype_PI_reference = "dotted", sp_color_PI_reference = "#E20134", sp_size_PI_reference = "2", sp_alpha_PI_reference = "1", sp_linetype_residual_reference = "solid", sp_color_residual_reference = "#E20134", sp_size_residual_reference = "1", sp_alpha_residual_reference = "1", sp_prediction_label = "5", sp_prediction_color = "#E20134", sp_linetype_CI = "solid", sp_color_CI = "#008DF9", sp_size_CI = "4", sp_alpha_CI = "1", sp_linetype_ref = "dotted", sp_color_ref = "gray60", sp_size_ref = "1", sp_alpha_ref = "1", sp_linetype_PI = "solid", sp_color_PI = "#E20134", sp_size_PI = "2", sp_alpha_PI = "1" )
switch |
. |
data |
. |
x |
. |
y |
. |
r |
. |
n |
. |
x_variable_name |
. |
y_variable_name |
. |
conf_level |
. |
show_details |
. |
do_regression |
. |
show_line |
. |
show_line_CI |
. |
show_residuals |
. |
show_PI |
. |
show_mean_lines |
. |
show_r |
. |
plot_as_z |
. |
predict_from_x |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
sp_plot_width |
. |
sp_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
error_layout |
. |
sp_ymin |
. |
sp_ymax |
. |
sp_ybreaks |
. |
sp_xmin |
. |
sp_xmax |
. |
sp_xbreaks |
. |
sp_ylab |
. |
sp_xlab |
. |
sp_axis.text.y |
. |
sp_axis.title.y |
. |
sp_axis.text.x |
. |
sp_axis.title.x |
. |
shape_summary |
. |
color_summary |
. |
fill_summary |
. |
size_summary |
. |
alpha_summary |
. |
linetype_summary |
. |
color_interval |
. |
size_interval |
. |
alpha_interval |
. |
alpha_error |
. |
fill_error |
. |
sp_shape_raw_reference |
. |
sp_color_raw_reference |
. |
sp_fill_raw_reference |
. |
sp_size_raw_reference |
. |
sp_alpha_raw_reference |
. |
sp_linetype_summary_reference |
. |
sp_color_summary_reference |
. |
sp_size_summary_reference |
. |
sp_alpha_summary_reference |
. |
sp_linetype_PI_reference |
. |
sp_color_PI_reference |
. |
sp_size_PI_reference |
. |
sp_alpha_PI_reference |
. |
sp_linetype_residual_reference |
. |
sp_color_residual_reference |
. |
sp_size_residual_reference |
. |
sp_alpha_residual_reference |
. |
sp_prediction_label |
. |
sp_prediction_color |
. |
sp_linetype_CI |
. |
sp_color_CI |
. |
sp_size_CI |
. |
sp_alpha_CI |
. |
sp_linetype_ref |
. |
sp_color_ref |
. |
sp_size_ref |
. |
sp_alpha_ref |
. |
sp_linetype_PI |
. |
sp_color_PI |
. |
sp_size_PI |
. |
sp_alpha_PI |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_r |
a table | ||||
results$regression |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$scatter_plot_warnings |
a html | ||||
results$scatter_plots |
an image | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Describe
jamovidescribe( data, outcome_variable, show_details = FALSE, mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = "0", histogram_bins = "12", es_plot_width = "500", es_plot_height = "400", ymin = "auto", ymax = "auto", breaks = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", fill_regular = "#008DF9", fill_highlighted = "#E20134", color = "black" )jamovidescribe( data, outcome_variable, show_details = FALSE, mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = "0", histogram_bins = "12", es_plot_width = "500", es_plot_height = "400", ymin = "auto", ymax = "auto", breaks = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", fill_regular = "#008DF9", fill_highlighted = "#E20134", color = "black" )
data |
. |
outcome_variable |
. |
show_details |
. |
mark_mean |
. |
mark_median |
. |
mark_sd |
. |
mark_quartiles |
. |
mark_z_lines |
. |
mark_percentile |
. |
histogram_bins |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
breaks |
. |
xmin |
. |
xmax |
. |
xbreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
fill_regular |
. |
fill_highlighted |
. |
color |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$describe_plot_warnings |
a html | ||||
results$describe_plot |
an image | ||||
results$describe_dotplot |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Means and Medians: Single Group
jamovimagnitude( switch = "from_raw", data, outcome_variable, mean = "", sd = "", n = "", outcome_variable_name = "Outcome variable", conf_level = 95, effect_size = "mean", show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "300", es_plot_height = "400", ymin = "auto", ymax = "auto", breaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", error_layout = "halfeye", error_scale = "0.20", error_nudge = "0.3", data_layout = "random", data_spread = "0.25", shape_raw = "circle filled", shape_summary = "circle filled", color_raw = "#008DF9", color_summary = "#008DF9", fill_raw = "NA", fill_summary = "#008DF9", size_raw = "2", size_summary = "4", alpha_raw = "1", alpha_summary = "1", linetype_summary = "solid", color_interval = "black", size_interval = "3", alpha_interval = "1", alpha_error = "1", fill_error = "gray75" )jamovimagnitude( switch = "from_raw", data, outcome_variable, mean = "", sd = "", n = "", outcome_variable_name = "Outcome variable", conf_level = 95, effect_size = "mean", show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "300", es_plot_height = "400", ymin = "auto", ymax = "auto", breaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", error_layout = "halfeye", error_scale = "0.20", error_nudge = "0.3", data_layout = "random", data_spread = "0.25", shape_raw = "circle filled", shape_summary = "circle filled", color_raw = "#008DF9", color_summary = "#008DF9", fill_raw = "NA", fill_summary = "#008DF9", size_raw = "2", size_summary = "4", alpha_raw = "1", alpha_summary = "1", linetype_summary = "solid", color_interval = "black", size_interval = "3", alpha_interval = "1", alpha_error = "1", fill_error = "gray75" )
switch |
. |
data |
. |
outcome_variable |
. |
mean |
. |
sd |
. |
n |
. |
outcome_variable_name |
. |
conf_level |
. |
effect_size |
. |
show_details |
. |
show_calculations |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
breaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
error_layout |
. |
error_scale |
. |
error_nudge |
. |
data_layout |
. |
data_spread |
. |
shape_raw |
. |
shape_summary |
. |
color_raw |
. |
color_summary |
. |
fill_raw |
. |
fill_summary |
. |
size_raw |
. |
size_summary |
. |
alpha_raw |
. |
alpha_summary |
. |
linetype_summary |
. |
color_interval |
. |
size_interval |
. |
alpha_interval |
. |
alpha_error |
. |
fill_error |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_smd |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$magnitude_plot_warnings |
a html | ||||
results$magnitude_plot |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Means and Medians: 2x2 Factorial
jamovimdiff2x2( design = "fully_between", switch = "from_raw", data, outcome_variable, grouping_variable_A, grouping_variable_B, outcome_variable_level1, outcome_variable_level2, outcome_variable_name_bs = "My outcome variable", grouping_variable, repeated_measures_name = "Time", outcome_variable_name = "My outcome variable", A1_label = "A1 level", A2_label = "A2 level", B1_label = "B1 level", B2_label = "B2 level", A_label = "Variable A", B_label = "Variable B", A1B1_mean = " ", A1B1_sd = " ", A1B1_n = " ", A1B2_mean = " ", A1B2_sd = " ", A1B2_n = " ", A2B1_mean = " ", A2B1_sd = " ", A2B1_n = " ", A2B2_mean = " ", A2B2_sd = " ", A2B2_n = " ", conf_level = 95, effect_size = "mean_difference", assume_equal_variance = TRUE, show_details = FALSE, show_interaction_plot = FALSE, show_CI = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "700", es_plot_height = "400", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = FALSE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_raw_unused = "circle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_unused = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray65", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_raw_unused = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray65", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_raw_unused = "1", size_summary_reference = "4", size_summary_comparison = "4", size_summary_unused = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_unused = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_unused = "gray65", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_unused = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_unused = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_unused = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_unused = "gray75", fill_error_difference = "gray75" )jamovimdiff2x2( design = "fully_between", switch = "from_raw", data, outcome_variable, grouping_variable_A, grouping_variable_B, outcome_variable_level1, outcome_variable_level2, outcome_variable_name_bs = "My outcome variable", grouping_variable, repeated_measures_name = "Time", outcome_variable_name = "My outcome variable", A1_label = "A1 level", A2_label = "A2 level", B1_label = "B1 level", B2_label = "B2 level", A_label = "Variable A", B_label = "Variable B", A1B1_mean = " ", A1B1_sd = " ", A1B1_n = " ", A1B2_mean = " ", A1B2_sd = " ", A1B2_n = " ", A2B1_mean = " ", A2B1_sd = " ", A2B1_n = " ", A2B2_mean = " ", A2B2_sd = " ", A2B2_n = " ", conf_level = 95, effect_size = "mean_difference", assume_equal_variance = TRUE, show_details = FALSE, show_interaction_plot = FALSE, show_CI = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "700", es_plot_height = "400", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = FALSE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_raw_unused = "circle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_unused = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray65", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_raw_unused = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray65", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_raw_unused = "1", size_summary_reference = "4", size_summary_comparison = "4", size_summary_unused = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_unused = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_unused = "gray65", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_unused = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_unused = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_unused = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_unused = "gray75", fill_error_difference = "gray75" )
design |
. |
switch |
. |
data |
. |
outcome_variable |
. |
grouping_variable_A |
. |
grouping_variable_B |
. |
outcome_variable_level1 |
. |
outcome_variable_level2 |
. |
outcome_variable_name_bs |
. |
grouping_variable |
. |
repeated_measures_name |
. |
outcome_variable_name |
. |
A1_label |
. |
A2_label |
. |
B1_label |
. |
B2_label |
. |
A_label |
. |
B_label |
. |
A1B1_mean |
. |
A1B1_sd |
. |
A1B1_n |
. |
A1B2_mean |
. |
A1B2_sd |
. |
A1B2_n |
. |
A2B1_mean |
. |
A2B1_sd |
. |
A2B1_n |
. |
A2B2_mean |
. |
A2B2_sd |
. |
A2B2_n |
. |
conf_level |
. |
effect_size |
. |
assume_equal_variance |
. |
show_details |
. |
show_interaction_plot |
. |
show_CI |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
rope_units |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
simple_contrast_labels |
. |
error_layout |
. |
error_scale |
. |
error_nudge |
. |
data_layout |
. |
data_spread |
. |
difference_axis_units |
. |
difference_axis_breaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_raw_unused |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_unused |
. |
shape_summary_difference |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
size_raw_reference |
. |
size_raw_comparison |
. |
size_raw_unused |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_unused |
. |
size_summary_difference |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_unused |
. |
linetype_summary_difference |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_unused |
. |
color_interval_difference |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_unused |
. |
size_interval_difference |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_unused |
. |
alpha_interval_difference |
. |
alpha_error_reference |
. |
alpha_error_comparison |
. |
alpha_error_unused |
. |
alpha_error_difference |
. |
fill_error_reference |
. |
fill_error_comparison |
. |
fill_error_unused |
. |
fill_error_difference |
. |
A results object containing:
results$analysis_type |
a html | ||||
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_median_difference |
a table | ||||
results$es_mean_difference |
a table | ||||
results$es_smd |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$main_effect_A |
an image | ||||
results$main_effect_B |
an image | ||||
results$interaction |
an image | ||||
results$interaction_plot |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Means and Medians: Independent Groups Contrast
jamovimdiffindcontrast( switch = "from_raw", data, outcome_variable, grouping_variable, means, sds, ns, grouping_variable_levels, outcome_variable_name = "Outcome variable", grouping_variable_name = "Grouping variable", comparison_labels = " ", reference_labels = " ", conf_level = 95, effect_size = "mean_difference", assume_equal_variance = TRUE, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "550", es_plot_height = "450", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_raw_unused = "circle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_unused = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray65", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_raw_unused = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray65", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_raw_unused = "1", size_summary_reference = "4", size_summary_comparison = "4", size_summary_unused = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_unused = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_unused = "gray65", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_unused = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_unused = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_unused = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_unused = "gray75", fill_error_difference = "gray75" )jamovimdiffindcontrast( switch = "from_raw", data, outcome_variable, grouping_variable, means, sds, ns, grouping_variable_levels, outcome_variable_name = "Outcome variable", grouping_variable_name = "Grouping variable", comparison_labels = " ", reference_labels = " ", conf_level = 95, effect_size = "mean_difference", assume_equal_variance = TRUE, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "550", es_plot_height = "450", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_raw_unused = "circle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_unused = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray65", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_raw_unused = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray65", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_raw_unused = "1", size_summary_reference = "4", size_summary_comparison = "4", size_summary_unused = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_unused = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_unused = "gray65", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_unused = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_unused = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_unused = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_unused = "gray75", fill_error_difference = "gray75" )
switch |
. |
data |
. |
outcome_variable |
. |
grouping_variable |
. |
means |
. |
sds |
. |
ns |
. |
grouping_variable_levels |
. |
outcome_variable_name |
. |
grouping_variable_name |
. |
comparison_labels |
. |
reference_labels |
. |
conf_level |
. |
effect_size |
. |
assume_equal_variance |
. |
show_details |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
rope_units |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
simple_contrast_labels |
. |
error_layout |
. |
error_scale |
. |
error_nudge |
. |
data_layout |
. |
data_spread |
. |
difference_axis_units |
. |
difference_axis_breaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_raw_unused |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_unused |
. |
shape_summary_difference |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
size_raw_reference |
. |
size_raw_comparison |
. |
size_raw_unused |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_unused |
. |
size_summary_difference |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_unused |
. |
linetype_summary_difference |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_unused |
. |
color_interval_difference |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_unused |
. |
size_interval_difference |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_unused |
. |
alpha_interval_difference |
. |
alpha_error_reference |
. |
alpha_error_comparison |
. |
alpha_error_unused |
. |
alpha_error_difference |
. |
fill_error_reference |
. |
fill_error_comparison |
. |
fill_error_unused |
. |
fill_error_difference |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_median_difference |
a table | ||||
results$es_mean_difference |
a table | ||||
results$es_smd |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an array of images | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Means and Medians: Paired
jamovimdiffpaired( switch = "from_raw", data, reference_measure, comparison_measure, comparison_mean = " ", comparison_sd = " ", reference_mean = " ", reference_sd = " ", n = " ", enter_r_or_sdiff = "enter_r", correlation = " ", sdiff = " ", comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 95, effect_size = "mean_difference", show_ratio = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "600", es_plot_height = "400", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_raw_difference = "triangle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#008DF9", color_raw_difference = "#E20134", color_summary_reference = "#008DF9", color_summary_comparison = "#008DF9", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_raw_difference = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#008DF9", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_raw_difference = "2", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_difference = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_difference = "gray75" )jamovimdiffpaired( switch = "from_raw", data, reference_measure, comparison_measure, comparison_mean = " ", comparison_sd = " ", reference_mean = " ", reference_sd = " ", n = " ", enter_r_or_sdiff = "enter_r", correlation = " ", sdiff = " ", comparison_measure_name = "Comparison measure", reference_measure_name = "Reference measure", conf_level = 95, effect_size = "mean_difference", show_ratio = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "600", es_plot_height = "400", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_raw_difference = "triangle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#008DF9", color_raw_difference = "#E20134", color_summary_reference = "#008DF9", color_summary_comparison = "#008DF9", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_raw_difference = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#008DF9", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_raw_difference = "2", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_difference = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_difference = "gray75" )
switch |
. |
data |
. |
reference_measure |
. |
comparison_measure |
. |
comparison_mean |
. |
comparison_sd |
. |
reference_mean |
. |
reference_sd |
. |
n |
. |
enter_r_or_sdiff |
. |
correlation |
. |
sdiff |
. |
comparison_measure_name |
. |
reference_measure_name |
. |
conf_level |
. |
effect_size |
. |
show_ratio |
. |
show_details |
. |
show_calculations |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
rope_units |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
simple_contrast_labels |
. |
error_layout |
. |
error_scale |
. |
error_nudge |
. |
data_layout |
. |
data_spread |
. |
difference_axis_units |
. |
difference_axis_breaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_raw_difference |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_difference |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_difference |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_difference |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_difference |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_difference |
. |
size_raw_reference |
. |
size_raw_comparison |
. |
size_raw_difference |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_difference |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_difference |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_difference |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_error_reference |
. |
alpha_error_comparison |
. |
alpha_error_difference |
. |
fill_error_reference |
. |
fill_error_comparison |
. |
fill_error_difference |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_r |
a table | ||||
results$es_mean_difference |
a table | ||||
results$es_smd |
a table | ||||
results$es_mean_ratio |
a table | ||||
results$es_median_difference |
a table | ||||
results$es_median_ratio |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Means and Medians: Two Groups
jamovimdifftwo( switch = "from_raw", data, outcome_variable, grouping_variable, reference_level_name = "Reference group", reference_mean = " ", reference_sd = " ", reference_n = " ", comparison_level_name = "Comparison group", comparison_mean = " ", comparison_sd = " ", comparison_n = " ", outcome_variable_name = "Outcome variable", grouping_variable_name = "Grouping variable", conf_level = 95, assume_equal_variance = TRUE, effect_size = "mean_difference", show_ratio = FALSE, switch_comparison_order = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "600", es_plot_height = "400", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_difference = "gray75" )jamovimdifftwo( switch = "from_raw", data, outcome_variable, grouping_variable, reference_level_name = "Reference group", reference_mean = " ", reference_sd = " ", reference_n = " ", comparison_level_name = "Comparison group", comparison_mean = " ", comparison_sd = " ", comparison_n = " ", outcome_variable_name = "Outcome variable", grouping_variable_name = "Grouping variable", conf_level = 95, assume_equal_variance = TRUE, effect_size = "mean_difference", show_ratio = FALSE, switch_comparison_order = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", rope_units = "raw", alpha = 0.05, null_color = "#A40122", es_plot_width = "600", es_plot_height = "400", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "halfeye", error_scale = "0.25", error_nudge = "0.5", data_layout = "random", data_spread = "0.20", difference_axis_units = "raw", difference_axis_breaks = "auto", shape_raw_reference = "circle filled", shape_raw_comparison = "circle filled", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_raw_reference = "NA", fill_raw_comparison = "NA", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_raw_reference = "2", size_raw_comparison = "2", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", size_interval_reference = "3", size_interval_comparison = "3", size_interval_difference = "3", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_error_reference = "1", alpha_error_comparison = "1", alpha_error_difference = "1", fill_error_reference = "gray75", fill_error_comparison = "gray75", fill_error_difference = "gray75" )
switch |
. |
data |
. |
outcome_variable |
. |
grouping_variable |
. |
reference_level_name |
. |
reference_mean |
. |
reference_sd |
. |
reference_n |
. |
comparison_level_name |
. |
comparison_mean |
. |
comparison_sd |
. |
comparison_n |
. |
outcome_variable_name |
. |
grouping_variable_name |
. |
conf_level |
. |
assume_equal_variance |
. |
effect_size |
. |
show_ratio |
. |
switch_comparison_order |
. |
show_details |
. |
show_calculations |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
rope_units |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
simple_contrast_labels |
. |
error_layout |
. |
error_scale |
. |
error_nudge |
. |
data_layout |
. |
data_spread |
. |
difference_axis_units |
. |
difference_axis_breaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_difference |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_difference |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_difference |
. |
size_raw_reference |
. |
size_raw_comparison |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_difference |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_difference |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_error_reference |
. |
alpha_error_comparison |
. |
alpha_error_difference |
. |
fill_error_reference |
. |
fill_error_comparison |
. |
fill_error_difference |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_mean_difference |
a table | ||||
results$es_smd |
a table | ||||
results$es_mean_ratio |
a table | ||||
results$es_median_difference |
a table | ||||
results$es_median_ratio |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an array of images | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Meta-Analysis: Difference in Means
jamovimetamdiff( switch = "from_raw", data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r, labels, moderator, d, dcomparison_ns, dreference_ns, dr, dlabels, dmoderator, conf_level = 95, effect_label = "My effect", reported_effect_size = "mean_difference", assume_equal_variance = TRUE, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = TRUE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )jamovimetamdiff( switch = "from_raw", data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r, labels, moderator, d, dcomparison_ns, dreference_ns, dr, dlabels, dmoderator, conf_level = 95, effect_label = "My effect", reported_effect_size = "mean_difference", assume_equal_variance = TRUE, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = TRUE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )
switch |
. |
data |
. |
comparison_means |
. |
comparison_sds |
. |
comparison_ns |
. |
reference_means |
. |
reference_sds |
. |
reference_ns |
. |
r |
. |
labels |
. |
moderator |
. |
d |
. |
dcomparison_ns |
. |
dreference_ns |
. |
dr |
. |
dlabels |
. |
dmoderator |
. |
conf_level |
. |
effect_label |
. |
reported_effect_size |
. |
assume_equal_variance |
. |
random_effects |
. |
include_PIs |
. |
show_details |
. |
es_plot_width |
. |
es_plot_height |
. |
size_base |
. |
size_multiplier |
. |
axis.text.y |
. |
report_CIs |
. |
meta_diamond_height |
. |
xlab |
. |
xmin |
. |
xmax |
. |
xbreaks |
. |
mark_zero |
. |
axis.text.x |
. |
axis.title.x |
. |
dlab |
. |
dmin |
. |
dmax |
. |
dbreaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_summary_difference |
. |
shape_raw_unused |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
color_summary_overall |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
fill_summary_overall |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
alpha_summary_overall |
. |
linetype_raw_reference |
. |
linetype_raw_comparison |
. |
linetype_summary_difference |
. |
linetype_raw_unused |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
color_interval_unused |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
size_interval_unused |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_interval_unused |
. |
A results object containing:
results$debug |
a html | ||||
results$help |
a html | ||||
results$raw_data |
a table | ||||
results$es_meta |
a table | ||||
results$es_heterogeneity |
a table | ||||
results$es_meta_difference |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$raw_data$asDF
as.data.frame(results$raw_data)
Meta-Analysis: Means
jamovimetamean( switch = "from_raw", data, means, sds, ns, labels, moderator, ds, dns, dlabels, dmoderator, conf_level = 95, effect_label = "My effect", reference_mean = "", reported_effect_size = "mean_difference", random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = TRUE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )jamovimetamean( switch = "from_raw", data, means, sds, ns, labels, moderator, ds, dns, dlabels, dmoderator, conf_level = 95, effect_label = "My effect", reference_mean = "", reported_effect_size = "mean_difference", random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = TRUE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )
switch |
. |
data |
. |
means |
. |
sds |
. |
ns |
. |
labels |
. |
moderator |
. |
ds |
. |
dns |
. |
dlabels |
. |
dmoderator |
. |
conf_level |
. |
effect_label |
. |
reference_mean |
. |
reported_effect_size |
. |
random_effects |
. |
include_PIs |
. |
show_details |
. |
es_plot_width |
. |
es_plot_height |
. |
size_base |
. |
size_multiplier |
. |
axis.text.y |
. |
report_CIs |
. |
meta_diamond_height |
. |
xlab |
. |
xmin |
. |
xmax |
. |
xbreaks |
. |
mark_zero |
. |
axis.text.x |
. |
axis.title.x |
. |
dlab |
. |
dmin |
. |
dmax |
. |
dbreaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_summary_difference |
. |
shape_raw_unused |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
color_summary_overall |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
fill_summary_overall |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
alpha_summary_overall |
. |
linetype_raw_reference |
. |
linetype_raw_comparison |
. |
linetype_summary_difference |
. |
linetype_raw_unused |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
color_interval_unused |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
size_interval_unused |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_interval_unused |
. |
A results object containing:
results$debug |
a html | ||||
results$help |
a html | ||||
results$raw_data |
a table | ||||
results$es_meta |
a table | ||||
results$es_heterogeneity |
a table | ||||
results$es_meta_difference |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$raw_data$asDF
as.data.frame(results$raw_data)
Meta-Analysis: Difference in Proportions
jamovimetapdiff( data, reference_cases, reference_ns, comparison_cases, comparison_ns, labels, moderator, effect_label = "My effect", reported_effect_size = "RD", conf_level = 95, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = FALSE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )jamovimetapdiff( data, reference_cases, reference_ns, comparison_cases, comparison_ns, labels, moderator, effect_label = "My effect", reported_effect_size = "RD", conf_level = 95, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = FALSE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )
data |
. |
reference_cases |
. |
reference_ns |
. |
comparison_cases |
. |
comparison_ns |
. |
labels |
. |
moderator |
. |
effect_label |
. |
reported_effect_size |
. |
conf_level |
. |
random_effects |
. |
include_PIs |
. |
show_details |
. |
es_plot_width |
. |
es_plot_height |
. |
size_base |
. |
size_multiplier |
. |
axis.text.y |
. |
report_CIs |
. |
meta_diamond_height |
. |
xlab |
. |
xmin |
. |
xmax |
. |
xbreaks |
. |
mark_zero |
. |
axis.text.x |
. |
axis.title.x |
. |
dlab |
. |
dmin |
. |
dmax |
. |
dbreaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_summary_difference |
. |
shape_raw_unused |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
color_summary_overall |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
fill_summary_overall |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
alpha_summary_overall |
. |
linetype_raw_reference |
. |
linetype_raw_comparison |
. |
linetype_summary_difference |
. |
linetype_raw_unused |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
color_interval_unused |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
size_interval_unused |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_interval_unused |
. |
A results object containing:
results$debug |
a html | ||||
results$help |
a html | ||||
results$raw_data |
a table | ||||
results$es_meta |
a table | ||||
results$es_heterogeneity |
a table | ||||
results$es_meta_difference |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$raw_data$asDF
as.data.frame(results$raw_data)
Meta-Analysis: Proportions
jamovimetaproportion( data, cases, ns, labels, moderator, effect_label = "My effect", conf_level = 95, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = FALSE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )jamovimetaproportion( data, cases, ns, labels, moderator, effect_label = "My effect", conf_level = 95, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "auto", xmax = "auto", xbreaks = "auto", mark_zero = FALSE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )
data |
. |
cases |
. |
ns |
. |
labels |
. |
moderator |
. |
effect_label |
. |
conf_level |
. |
random_effects |
. |
include_PIs |
. |
show_details |
. |
es_plot_width |
. |
es_plot_height |
. |
size_base |
. |
size_multiplier |
. |
axis.text.y |
. |
report_CIs |
. |
meta_diamond_height |
. |
xlab |
. |
xmin |
. |
xmax |
. |
xbreaks |
. |
mark_zero |
. |
axis.text.x |
. |
axis.title.x |
. |
dlab |
. |
dmin |
. |
dmax |
. |
dbreaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_summary_difference |
. |
shape_raw_unused |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
color_summary_overall |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
fill_summary_overall |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
alpha_summary_overall |
. |
linetype_raw_reference |
. |
linetype_raw_comparison |
. |
linetype_summary_difference |
. |
linetype_raw_unused |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
color_interval_unused |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
size_interval_unused |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_interval_unused |
. |
A results object containing:
results$debug |
a html | ||||
results$help |
a html | ||||
results$raw_data |
a table | ||||
results$es_meta |
a table | ||||
results$es_heterogeneity |
a table | ||||
results$es_meta_difference |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$raw_data$asDF
as.data.frame(results$raw_data)
Meta-Analysis: Correlations
jamovimetar( data, rs, ns, labels, moderator, effect_label = "My effect", conf_level = 95, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "-1", xmax = "1", xbreaks = "auto", mark_zero = TRUE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )jamovimetar( data, rs, ns, labels, moderator, effect_label = "My effect", conf_level = 95, random_effects = "random_effects", include_PIs = FALSE, show_details = FALSE, es_plot_width = "600", es_plot_height = "750", size_base = "2", size_multiplier = "3", axis.text.y = "14", report_CIs = FALSE, meta_diamond_height = ".25", xlab = "auto", xmin = "-1", xmax = "1", xbreaks = "auto", mark_zero = TRUE, axis.text.x = "14", axis.title.x = "15", dlab = "auto", dmin = "auto", dmax = "auto", dbreaks = "auto", shape_raw_reference = "square filled", shape_raw_comparison = "square filled", shape_summary_difference = "triangle filled", shape_raw_unused = "square filled", color_raw_reference = "#008DF9", color_raw_comparison = "#009F81", color_raw_unused = "gray65", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_unused = "gray75", color_summary_difference = "black", color_summary_overall = "black", fill_raw_reference = "#008DF9", fill_raw_comparison = "#009F81", fill_raw_unused = "gray65", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_unused = "gray75", fill_summary_difference = "black", fill_summary_overall = "black", alpha_raw_reference = "1", alpha_raw_comparison = "1", alpha_raw_unused = "1", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_unused = "1", alpha_summary_difference = "1", alpha_summary_overall = "1", linetype_raw_reference = "solid", linetype_raw_comparison = "solid", linetype_summary_difference = "solid", linetype_raw_unused = "solid", color_interval_reference = "black", color_interval_comparison = "black", color_interval_difference = "black", color_interval_unused = "black", size_interval_reference = ".50", size_interval_comparison = ".50", size_interval_difference = ".50", size_interval_unused = ".50", alpha_interval_reference = "1", alpha_interval_comparison = "1", alpha_interval_difference = "1", alpha_interval_unused = "1" )
data |
. |
rs |
. |
ns |
. |
labels |
. |
moderator |
. |
effect_label |
. |
conf_level |
. |
random_effects |
. |
include_PIs |
. |
show_details |
. |
es_plot_width |
. |
es_plot_height |
. |
size_base |
. |
size_multiplier |
. |
axis.text.y |
. |
report_CIs |
. |
meta_diamond_height |
. |
xlab |
. |
xmin |
. |
xmax |
. |
xbreaks |
. |
mark_zero |
. |
axis.text.x |
. |
axis.title.x |
. |
dlab |
. |
dmin |
. |
dmax |
. |
dbreaks |
. |
shape_raw_reference |
. |
shape_raw_comparison |
. |
shape_summary_difference |
. |
shape_raw_unused |
. |
color_raw_reference |
. |
color_raw_comparison |
. |
color_raw_unused |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_unused |
. |
color_summary_difference |
. |
color_summary_overall |
. |
fill_raw_reference |
. |
fill_raw_comparison |
. |
fill_raw_unused |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_unused |
. |
fill_summary_difference |
. |
fill_summary_overall |
. |
alpha_raw_reference |
. |
alpha_raw_comparison |
. |
alpha_raw_unused |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_unused |
. |
alpha_summary_difference |
. |
alpha_summary_overall |
. |
linetype_raw_reference |
. |
linetype_raw_comparison |
. |
linetype_summary_difference |
. |
linetype_raw_unused |
. |
color_interval_reference |
. |
color_interval_comparison |
. |
color_interval_difference |
. |
color_interval_unused |
. |
size_interval_reference |
. |
size_interval_comparison |
. |
size_interval_difference |
. |
size_interval_unused |
. |
alpha_interval_reference |
. |
alpha_interval_comparison |
. |
alpha_interval_difference |
. |
alpha_interval_unused |
. |
A results object containing:
results$debug |
a html | ||||
results$help |
a html | ||||
results$raw_data |
a table | ||||
results$es_meta |
a table | ||||
results$es_heterogeneity |
a table | ||||
results$es_meta_difference |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$raw_data$asDF
as.data.frame(results$raw_data)
Proportions: Paired
jamovipdiffpaired( switch = "from_raw", data, reference_measure, comparison_measure, cases_consistent = " ", cases_inconsistent = " ", not_cases_consistent = " ", not_cases_inconsistent = " ", case_label = "Sick", not_case_label = "Well", comparison_measure_name = "Post-test", reference_measure_name = "Pre-test", conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "400", es_plot_height = "450", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "none", difference_axis_breaks = "auto", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid" )jamovipdiffpaired( switch = "from_raw", data, reference_measure, comparison_measure, cases_consistent = " ", cases_inconsistent = " ", not_cases_consistent = " ", not_cases_inconsistent = " ", case_label = "Sick", not_case_label = "Well", comparison_measure_name = "Post-test", reference_measure_name = "Pre-test", conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "400", es_plot_height = "450", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "none", difference_axis_breaks = "auto", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid" )
switch |
. |
data |
. |
reference_measure |
. |
comparison_measure |
. |
cases_consistent |
. |
cases_inconsistent |
. |
not_cases_consistent |
. |
not_cases_inconsistent |
. |
case_label |
. |
not_case_label |
. |
comparison_measure_name |
. |
reference_measure_name |
. |
conf_level |
. |
show_details |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
simple_contrast_labels |
. |
error_layout |
. |
difference_axis_breaks |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_difference |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_difference |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_difference |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_difference |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_difference |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_proportion_difference |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Proportions: Two Groups
jamovipdifftwo( switch = "from_raw", data, outcome_variable, grouping_variable, comparison_cases = " ", comparison_not_cases = " ", reference_cases = " ", reference_not_cases = " ", case_label = "Sick", not_case_label = "Well", grouping_variable_level1 = "Treated", grouping_variable_level2 = "Control", outcome_variable_name = "Outcome variable", grouping_variable_name = "Grouping variable", count_NA = FALSE, show_ratio = FALSE, show_chi_square = FALSE, chi_table_option = "both", show_phi = FALSE, conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "400", es_plot_height = "450", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "none", difference_axis_breaks = "auto", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid" )jamovipdifftwo( switch = "from_raw", data, outcome_variable, grouping_variable, comparison_cases = " ", comparison_not_cases = " ", reference_cases = " ", reference_not_cases = " ", case_label = "Sick", not_case_label = "Well", grouping_variable_level1 = "Treated", grouping_variable_level2 = "Control", outcome_variable_name = "Outcome variable", grouping_variable_name = "Grouping variable", count_NA = FALSE, show_ratio = FALSE, show_chi_square = FALSE, chi_table_option = "both", show_phi = FALSE, conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "400", es_plot_height = "450", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", simple_contrast_labels = TRUE, error_layout = "none", difference_axis_breaks = "auto", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid" )
switch |
. |
data |
. |
outcome_variable |
. |
grouping_variable |
. |
comparison_cases |
. |
comparison_not_cases |
. |
reference_cases |
. |
reference_not_cases |
. |
case_label |
. |
not_case_label |
. |
grouping_variable_level1 |
. |
grouping_variable_level2 |
. |
outcome_variable_name |
. |
grouping_variable_name |
. |
count_NA |
. |
show_ratio |
. |
show_chi_square |
. |
chi_table_option |
. |
show_phi |
. |
conf_level |
. |
show_details |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
simple_contrast_labels |
. |
error_layout |
. |
difference_axis_breaks |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_difference |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_difference |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_difference |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_difference |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_difference |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_proportion_difference |
a table | ||||
results$contingency_table |
a table | ||||
results$es_phi |
a table | ||||
results$es_odds_ratio |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an array of images | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Proportions: Single Group
jamoviproportion( switch = "from_raw", data, outcome_variable, cases = " ", not_cases = " ", case_label = "Affected", not_case_label = "Not Affected", outcome_variable_name = "My outcome variable", count_NA = FALSE, conf_level = 95, show_details = FALSE, plot_possible = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "300", es_plot_height = "400", ymin = "0", ymax = "1", breaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", error_layout = "none", shape_summary = "circle filled", color_summary = "#008DF9", fill_summary = "#008DF9", size_summary = "4", alpha_summary = "1", linetype_summary = "solid" )jamoviproportion( switch = "from_raw", data, outcome_variable, cases = " ", not_cases = " ", case_label = "Affected", not_case_label = "Not Affected", outcome_variable_name = "My outcome variable", count_NA = FALSE, conf_level = 95, show_details = FALSE, plot_possible = FALSE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "300", es_plot_height = "400", ymin = "0", ymax = "1", breaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", error_layout = "none", shape_summary = "circle filled", color_summary = "#008DF9", fill_summary = "#008DF9", size_summary = "4", alpha_summary = "1", linetype_summary = "solid" )
switch |
. |
data |
. |
outcome_variable |
. |
cases |
. |
not_cases |
. |
case_label |
. |
not_case_label |
. |
outcome_variable_name |
. |
count_NA |
. |
conf_level |
. |
show_details |
. |
plot_possible |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
ymin |
. |
ymax |
. |
breaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
error_layout |
. |
shape_summary |
. |
color_summary |
. |
fill_summary |
. |
size_summary |
. |
alpha_summary |
. |
linetype_summary |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$magnitude_plot_warnings |
a html | ||||
results$estimation_plots |
an array of images | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
Correlations: Two Groups
jamovirdifftwo( switch = "from_raw", data, x, y, grouping_variable, comparison_r = " ", comparison_n = " ", reference_r = " ", reference_n = " ", x_variable_name = "X variable", y_variable_name = "Y variable", comparison_level_name = "Comparison level", reference_level_name = "Reference level", grouping_variable_name = "Grouping variable", conf_level = 95, show_details = FALSE, show_line = TRUE, show_line_CI = TRUE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "600", es_plot_height = "400", sp_plot_width = "650", sp_plot_height = "650", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", sp_ymin = "auto", sp_ymax = "auto", sp_ybreaks = "auto", sp_xmin = "auto", sp_xmax = "auto", sp_xbreaks = "auto", sp_ylab = "auto", sp_xlab = "auto", sp_axis.text.y = "14", sp_axis.title.y = "15", sp_axis.text.x = "14", sp_axis.title.x = "15", difference_axis_breaks = "auto", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid", sp_shape_raw_reference = "circle filled", sp_shape_raw_comparison = "circle filled", sp_shape_raw_unused = "circle filled", sp_color_raw_reference = "black", sp_color_raw_comparison = "black", sp_color_raw_unused = "black", sp_fill_raw_reference = "#008DF9", sp_fill_raw_comparison = "#009F81", sp_fill_raw_unused = "NA", sp_size_raw_reference = "3", sp_size_raw_comparison = "3", sp_size_raw_unused = "2", sp_alpha_raw_reference = ".25", sp_alpha_raw_comparison = ".25", sp_alpha_raw_unused = ".25", sp_linetype_summary_reference = "solid", sp_linetype_summary_comparison = "solid", sp_color_summary_reference = "#008DF9", sp_color_summary_comparison = "#009F81", sp_size_summary_reference = "2", sp_size_summary_comparison = "2", sp_alpha_summary_reference = ".25", sp_alpha_summary_comparison = ".25" )jamovirdifftwo( switch = "from_raw", data, x, y, grouping_variable, comparison_r = " ", comparison_n = " ", reference_r = " ", reference_n = " ", x_variable_name = "X variable", y_variable_name = "Y variable", comparison_level_name = "Comparison level", reference_level_name = "Reference level", grouping_variable_name = "Grouping variable", conf_level = 95, show_details = FALSE, show_line = TRUE, show_line_CI = TRUE, evaluate_hypotheses = FALSE, null_value = "0", null_boundary = "0", alpha = 0.05, null_color = "#A40122", es_plot_width = "600", es_plot_height = "400", sp_plot_width = "650", sp_plot_height = "650", ymin = "auto", ymax = "auto", ybreaks = "auto", ylab = "auto", xlab = "auto", axis.text.y = "14", axis.title.y = "15", axis.text.x = "14", axis.title.x = "15", sp_ymin = "auto", sp_ymax = "auto", sp_ybreaks = "auto", sp_xmin = "auto", sp_xmax = "auto", sp_xbreaks = "auto", sp_ylab = "auto", sp_xlab = "auto", sp_axis.text.y = "14", sp_axis.title.y = "15", sp_axis.text.x = "14", sp_axis.title.x = "15", difference_axis_breaks = "auto", shape_summary_reference = "circle filled", shape_summary_comparison = "circle filled", shape_summary_difference = "triangle filled", color_summary_reference = "#008DF9", color_summary_comparison = "#009F81", color_summary_difference = "black", fill_summary_reference = "#008DF9", fill_summary_comparison = "#009F81", fill_summary_difference = "black", size_summary_reference = "4", size_summary_comparison = "4", size_summary_difference = "4", alpha_summary_reference = "1", alpha_summary_comparison = "1", alpha_summary_difference = "1", linetype_summary_reference = "solid", linetype_summary_comparison = "solid", linetype_summary_difference = "solid", sp_shape_raw_reference = "circle filled", sp_shape_raw_comparison = "circle filled", sp_shape_raw_unused = "circle filled", sp_color_raw_reference = "black", sp_color_raw_comparison = "black", sp_color_raw_unused = "black", sp_fill_raw_reference = "#008DF9", sp_fill_raw_comparison = "#009F81", sp_fill_raw_unused = "NA", sp_size_raw_reference = "3", sp_size_raw_comparison = "3", sp_size_raw_unused = "2", sp_alpha_raw_reference = ".25", sp_alpha_raw_comparison = ".25", sp_alpha_raw_unused = ".25", sp_linetype_summary_reference = "solid", sp_linetype_summary_comparison = "solid", sp_color_summary_reference = "#008DF9", sp_color_summary_comparison = "#009F81", sp_size_summary_reference = "2", sp_size_summary_comparison = "2", sp_alpha_summary_reference = ".25", sp_alpha_summary_comparison = ".25" )
switch |
. |
data |
. |
x |
. |
y |
. |
grouping_variable |
. |
comparison_r |
. |
comparison_n |
. |
reference_r |
. |
reference_n |
. |
x_variable_name |
. |
y_variable_name |
. |
comparison_level_name |
. |
reference_level_name |
. |
grouping_variable_name |
. |
conf_level |
. |
show_details |
. |
show_line |
. |
show_line_CI |
. |
evaluate_hypotheses |
. |
null_value |
. |
null_boundary |
. |
alpha |
. |
null_color |
. |
es_plot_width |
. |
es_plot_height |
. |
sp_plot_width |
. |
sp_plot_height |
. |
ymin |
. |
ymax |
. |
ybreaks |
. |
ylab |
. |
xlab |
. |
axis.text.y |
. |
axis.title.y |
. |
axis.text.x |
. |
axis.title.x |
. |
sp_ymin |
. |
sp_ymax |
. |
sp_ybreaks |
. |
sp_xmin |
. |
sp_xmax |
. |
sp_xbreaks |
. |
sp_ylab |
. |
sp_xlab |
. |
sp_axis.text.y |
. |
sp_axis.title.y |
. |
sp_axis.text.x |
. |
sp_axis.title.x |
. |
difference_axis_breaks |
. |
shape_summary_reference |
. |
shape_summary_comparison |
. |
shape_summary_difference |
. |
color_summary_reference |
. |
color_summary_comparison |
. |
color_summary_difference |
. |
fill_summary_reference |
. |
fill_summary_comparison |
. |
fill_summary_difference |
. |
size_summary_reference |
. |
size_summary_comparison |
. |
size_summary_difference |
. |
alpha_summary_reference |
. |
alpha_summary_comparison |
. |
alpha_summary_difference |
. |
linetype_summary_reference |
. |
linetype_summary_comparison |
. |
linetype_summary_difference |
. |
sp_shape_raw_reference |
. |
sp_shape_raw_comparison |
. |
sp_shape_raw_unused |
. |
sp_color_raw_reference |
. |
sp_color_raw_comparison |
. |
sp_color_raw_unused |
. |
sp_fill_raw_reference |
. |
sp_fill_raw_comparison |
. |
sp_fill_raw_unused |
. |
sp_size_raw_reference |
. |
sp_size_raw_comparison |
. |
sp_size_raw_unused |
. |
sp_alpha_raw_reference |
. |
sp_alpha_raw_comparison |
. |
sp_alpha_raw_unused |
. |
sp_linetype_summary_reference |
. |
sp_linetype_summary_comparison |
. |
sp_color_summary_reference |
. |
sp_color_summary_comparison |
. |
sp_size_summary_reference |
. |
sp_size_summary_comparison |
. |
sp_alpha_summary_reference |
. |
sp_alpha_summary_comparison |
. |
A results object containing:
results$debug |
a preformatted | ||||
results$help |
a html | ||||
results$overview |
a table | ||||
results$es_r |
a table | ||||
results$es_r_difference |
a table | ||||
results$point_null |
a table | ||||
results$interval_null |
a table | ||||
results$scatter_plot_warnings |
a html | ||||
results$scatter_plots |
an image | ||||
results$estimation_plot_warnings |
a html | ||||
results$estimation_plots |
an image | ||||
Tables can be converted to data frames with asDF or as.data.frame. For example:
results$overview$asDF
as.data.frame(results$overview)
meta_any is suitable for synthesizing any effect size across
multiple studies. You must provide the effect size for each study and the
predicted sampling variance for each study.
meta_any( data, yi, vi, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", effect_size_name = "Effect size", moderator_variable_name = "My moderator", random_effects = TRUE, conf_level = 0.95 )meta_any( data, yi, vi, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", effect_size_name = "Effect size", moderator_variable_name = "My moderator", random_effects = TRUE, conf_level = 0.95 )
data |
A data frame or tibble with columns |
yi |
Name a column in data containing the effect size for each study |
vi |
Name of a column in data containing the expected sampling variance for each study |
labels |
Name of a column in data containing a label for each study |
moderator |
Optional name of a column in data containing a factor as a categorical moderator |
contrast |
Optional vector specifying a contrast analysis for the categorical moderator. Only define if a moderator is defined; vector length should match number of levels in the moderator |
effect_label |
Optional human-friendly name for the effect being synthesized; defaults to 'My effect' |
effect_size_name |
Optional human-friendly name of the effect size being synthesized; defaults to 'Effect size' |
moderator_variable_name |
Optional human-friendly name of the moderator, if defined; If not passed but a moderator is defined, will be set to the quoted name of the moderator column or 'My moderator' |
random_effects |
Use TRUE to obtain a random effect meta-analysis (usually recommended); FALSE for fixed effect. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
#' Once you generate an estimate with this function, you can visualize
it with plot_meta().
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
The diamond ratio and its confidence interval come from
CI_diamond_ratio().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
#' # Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Fixed effect, 95% CI esizes <- esci::meta_mean( data = esci::data_mccabemichael_brain, means = "M Brain", sds = "s Brain", ns = "n Brain", labels = "Study name", random_effects = FALSE )$raw_data estimate <- esci::meta_any( data = esizes, yi = effect_size, vi = sample_variance, labels = label, effect_size_name = "Mean", random_effects = FALSE ) myplot_forest <- esci::plot_meta(estimate)#' # Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Fixed effect, 95% CI esizes <- esci::meta_mean( data = esci::data_mccabemichael_brain, means = "M Brain", sds = "s Brain", ns = "n Brain", labels = "Study name", random_effects = FALSE )$raw_data estimate <- esci::meta_any( data = esizes, yi = effect_size, vi = sample_variance, labels = label, effect_size_name = "Mean", random_effects = FALSE ) myplot_forest <- esci::plot_meta(estimate)
meta_d1 is suitable for synthesizing across multiple
single-group studies with a continuous outcome variable, but where the
outcome is not measured on the same scale in all studies
meta_d1( data, ds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", random_effects = TRUE, conf_level = 0.95 )meta_d1( data, ds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", random_effects = TRUE, conf_level = 0.95 )
data |
A data frame or tibble |
ds |
Set of bias-adjusted cohen's d1 values, 1 for each study |
ns |
Set of sample sizes, positive integers, 1 for each study |
labels |
Optional set of labels, 1 for each study |
moderator |
Optional factor as a categorical moderator; should have k > 2 per group |
contrast |
Optional vector specifying a contrast between moderator levels |
effect_label |
Optional character providing a human-friendly label for the effect |
random_effects |
Boolean; TRUE for a random effects model; otherwise fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Once you generate an estimate with this function, you can visualize
it with plot_meta().
Each study's effect size should be expressed as Cohen's d1: (mean - reference) / sd.
And the d1 values should all be corrected for bias.
The function CI_smd_one() can assist with converting raw data from
each study to d1_unbiased.
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
The diamond ratio and its confidence interval come from
CI_diamond_ratio().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# example code original_7 <- data.frame( study_name = c( "Aden (1993)" , "Buggs (1995)" , "Crazed (1999)" , "Dudley (2003)" , "Evers (2005)" , "Fox (2009)", "Mine (2011)" ), rt_mean = c( 454 , 317 , 430 , 525 , 479 , 387, 531 ), rt_sd = c( 142 , 158 , 137 , 260 , 144 , 165, 233 ), rt_n = c( 24 , 7 , 20 , 8 , 14 , 13, 18 ), subset = as.factor( c( "90s", "90s", "90s", "00s", "00s", "00s", "00s" ) ), d1_unbiased = c( 3.091587, 1.742751, 3.012857, 1.793487, 3.130074, 2.195209, 2.17667 ) ) # Fixed effect, 95% CI estimate <- esci::meta_d1( original_7, d1_unbiased, rt_n, study_name, random_effects = FALSE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Add a moderator estimate_moderator <- esci::meta_d1( data = original_7, ds = d1_unbiased, ns = rt_n, moderator = subset, labels = study_name, random_effects = FALSE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)# example code original_7 <- data.frame( study_name = c( "Aden (1993)" , "Buggs (1995)" , "Crazed (1999)" , "Dudley (2003)" , "Evers (2005)" , "Fox (2009)", "Mine (2011)" ), rt_mean = c( 454 , 317 , 430 , 525 , 479 , 387, 531 ), rt_sd = c( 142 , 158 , 137 , 260 , 144 , 165, 233 ), rt_n = c( 24 , 7 , 20 , 8 , 14 , 13, 18 ), subset = as.factor( c( "90s", "90s", "90s", "00s", "00s", "00s", "00s" ) ), d1_unbiased = c( 3.091587, 1.742751, 3.012857, 1.793487, 3.130074, 2.195209, 2.17667 ) ) # Fixed effect, 95% CI estimate <- esci::meta_d1( original_7, d1_unbiased, rt_n, study_name, random_effects = FALSE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Add a moderator estimate_moderator <- esci::meta_d1( data = original_7, ds = d1_unbiased, ns = rt_n, moderator = subset, labels = study_name, random_effects = FALSE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)
meta_d2 is suitable for synthesizing across multiple two-group studies
(paired or independent) with a continuous outcome measure but where not
all studies are measured on the same scale, and instead the magnitude of
difference for each study is expressed as d_s or d_avg.
meta_d2( data, ds, comparison_ns, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )meta_d2( data, ds, comparison_ns, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )
data |
A data frame or tibble |
ds |
Set of bias-adjusted cohen's d_s or d_avg values, 1 for each study |
comparison_ns |
Set of comparison_group sample sizes, positive integers, 1 for each study |
reference_ns |
Set of reference_groups sample sizes, positive integers, 1 for each study |
r |
optional correlation between measures for w-s studies, NA otherwise |
labels |
Optional set of labels, 1 for each study |
moderator |
Optional factor as a categorical moderator; should have k > 2 per group |
contrast |
Optional vector specifying a contrast between moderator levels |
effect_label |
Optional character providing a human-friendly label for the effect |
assume_equal_variance |
Defaults to FALSE |
random_effects |
Boolean; TRUE for a random effects model; otherwise fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Once you generate an estimate with this function, you can visualize
it with plot_meta().
Each study's effect size should be expressed as: Cohen's d_s: (comparison_mean - reference_mean) / sd_pooled or Cohen_'s d_avg: (comparison_mean - reference_mean) / sd_avg
To enter d_s, set assume_equal_variance to TRUE To enter d_avg, set assume_equal_variance to FALSE
And the d values should all be corrected for bias.
The function CI_smd_ind_contrast() can assist with converting
raw data from each study to d_s or d_avg with bias correction. It also
has more details on calculation of these forms of d and their CIs.
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
The diamond ratio and its confidence interval come from
CI_diamond_ratio().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# Data set -- see Introduction to the New Statistics, 1st edition data("data_damischrcj") # Meta-analysis, random effects, assuming equal variance, no moderator estimate <- esci::meta_d2( data = esci::data_damischrcj, ds = "Cohen's d unbiased", comparison_ns = "n Control", reference_ns = "n Lucky", labels = Study, assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Add a categorical moderator estimate_moderator <- esci::meta_d2( data = esci::data_damischrcj, ds = "Cohen's d unbiased", comparison_ns = "n Control", reference_ns = "n Lucky", labels = "Study", moderator = "Research Group", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)# Data set -- see Introduction to the New Statistics, 1st edition data("data_damischrcj") # Meta-analysis, random effects, assuming equal variance, no moderator estimate <- esci::meta_d2( data = esci::data_damischrcj, ds = "Cohen's d unbiased", comparison_ns = "n Control", reference_ns = "n Lucky", labels = Study, assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Add a categorical moderator estimate_moderator <- esci::meta_d2( data = esci::data_damischrcj, ds = "Cohen's d unbiased", comparison_ns = "n Control", reference_ns = "n Lucky", labels = "Study", moderator = "Research Group", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)
meta_mdiff_two is suitable for synthesizing across multiple two-group
studies (paired or independent) with a continuous outcome measure. It takes
in raw data from each study. If all studies used the same measurement scale,
a meta-analytic raw-score difference can be returned. If studies used
different scales, a standardized mean difference can be returned.
Studies can be all paired, all independent, or a mix. Equal variance can
be assumed, or not. If standardized mean difference is the output, it is
d_s when equal variance is assumed and d_avg when equal variance is not
assumed.
meta_mdiff_two( data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", reported_effect_size = c("mean_difference", "smd_unbiased", "smd"), assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )meta_mdiff_two( data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", reported_effect_size = c("mean_difference", "smd_unbiased", "smd"), assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )
data |
A data frame or tibble |
comparison_means |
Set of comparison_group means, 1 per study |
comparison_sds |
Set of comparison_group standard deviations, 1 per study, all > 0 |
comparison_ns |
Set of comparison_group sample sizes, positive integers, 1 for each study |
reference_means |
Set of reference_group means, 1 per study |
reference_sds |
Set of comparison_group standard deviations, 1 per study, all > 0 |
reference_ns |
Set of reference_group sample sizes, positive integers, 1 for each study |
r |
Optional correlation between measures for w-s studies, NA otherwise |
labels |
An optional collection of study labels |
moderator |
An optional factor to analyze as a categorical moderator, must have k > 2 per groups |
contrast |
An optional contrast to estimate between moderator levels; express as a vector of contrast weights with 1 weight per moderator level. |
effect_label |
Optional character giving a human-friendly name of the effect being synthesized |
reported_effect_size |
Character specifying effect size to return: Must be one of 'mean_difference', 'smd_unbiased' (to return an unbiased Cohen's d_s or d_avg) or 'smd' (to return d_s or d_avg without correction for bias). Defaults to mean_difference. |
assume_equal_variance |
Defaults to FALSE |
random_effects |
TRUE for random effect model; FALSE for fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Once you generate an estimate with this function, you can visualize
it with plot_meta().
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
The diamond ratio and its confidence interval come from
CI_diamond_ratio().
If reported_effect_size is smd_unbiased or smd the conversion to Cohen's d
is handled by CI_smd_ind_contrast().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis: random effects, moderator estimate_moderator <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator) # Meta-analysis: random effects, moderator, output d_s estimate_moderator_d <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator_d <- esci::plot_meta(estimate_moderator_d)# Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis: random effects, moderator estimate_moderator <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator) # Meta-analysis: random effects, moderator, output d_s estimate_moderator_d <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator_d <- esci::plot_meta(estimate_moderator_d)
meta_mean is suitable for synthesizing across multiple single-group studies
with a continuous outcome variable when all studies are measured on the
same scale.
meta_mean( data, means, sds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", reference_mean = 0, reported_effect_size = c("mean_difference", "smd_unbiased", "smd"), random_effects = TRUE, conf_level = 0.95 )meta_mean( data, means, sds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", reference_mean = 0, reported_effect_size = c("mean_difference", "smd_unbiased", "smd"), random_effects = TRUE, conf_level = 0.95 )
data |
A dataframe or tibble |
means |
A collection of study means, 1 per study |
sds |
A collection of study standard deviations, 1 per study, all >0 |
ns |
A collection of sample sizes, 1 per study, all integers > 2 |
labels |
An optional collection of study labels |
moderator |
An optional factor to analyze as a categorical moderator, must have k > 2 per groups |
contrast |
An optional contrast to estimate between moderator levels; express as a vector of contrast weights with 1 weight per moderator level. |
effect_label |
Optional character giving a human-friendly name of the effect being synthesized |
reference_mean |
Optional reference mean, defaults to 0 |
reported_effect_size |
Character specifying effect size to return; Must be one of 'mean_difference', 'smd_unbiased' (to return an unbiased Cohen's d1) or 'smd' (to return Cohen's d1 without correction for bias) |
random_effects |
TRUE for random effect model; FALSE for fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
The diamond ratio and its confidence interval come from
CI_diamond_ratio().
If reported_effect_size is smd_unbiased or smd the conversion to d1
is handled by CI_smd_one().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Fixed effect, 95% CI estimate <- esci::meta_mean( data = esci::data_mccabemichael_brain, means = "M Brain", sds = "s Brain", ns = "n Brain", labels = "Study name", random_effects = FALSE ) myplot_forest <- esci::plot_meta(estimate) # Add a moderator, report cohen's d1 estimate_moderator_d <- esci::meta_mean( data = esci::data_mccabemichael_brain, means = "M Brain", sds = "s Brain", ns = "n Brain", labels = "Study name", moderator = "Research group", reported_effect_size = "smd_unbiased", random_effects = FALSE ) # Forest plot myplot_forest_moderator_d <- esci::plot_meta(estimate_moderator_d)# Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Fixed effect, 95% CI estimate <- esci::meta_mean( data = esci::data_mccabemichael_brain, means = "M Brain", sds = "s Brain", ns = "n Brain", labels = "Study name", random_effects = FALSE ) myplot_forest <- esci::plot_meta(estimate) # Add a moderator, report cohen's d1 estimate_moderator_d <- esci::meta_mean( data = esci::data_mccabemichael_brain, means = "M Brain", sds = "s Brain", ns = "n Brain", labels = "Study name", moderator = "Research group", reported_effect_size = "smd_unbiased", random_effects = FALSE ) # Forest plot myplot_forest_moderator_d <- esci::plot_meta(estimate_moderator_d)
meta_pdiff_two is suitable for synthesizing across multiple two-group
studies with a categorical outcome variable. It takes as input the
the number of cases/events in the comparison and reference groups as
well as the total number of samples in the comparison and reference groups.
meta_pdiff_two( data, comparison_cases, comparison_ns, reference_cases, reference_ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", reported_effect_size = c("RD", "RR", "OR", "AS", "PETO"), random_effects = TRUE, conf_level = 0.95 )meta_pdiff_two( data, comparison_cases, comparison_ns, reference_cases, reference_ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", reported_effect_size = c("RD", "RR", "OR", "AS", "PETO"), random_effects = TRUE, conf_level = 0.95 )
data |
A dataframe or tibble |
comparison_cases |
A collection of case/event counts for the comparison groups, 1 per study, all integers >= 0 |
comparison_ns |
A collection of sample sizes for the comparison groups, 1 per study, all integers > 2 |
reference_cases |
A collection of case/event counts for the reference groups, 1 per study, all integers >= 0 |
reference_ns |
A collection of sample sizes for the reference groups, 1 per study, all integers > 2 |
labels |
An optional collection of study labels |
moderator |
An optional factor to analyze as a categorical moderator, must have k > 2 per groups |
contrast |
An optional contrast to estimate between moderator levels; express as a vector of contrast weights with 1 weight per moderator level. |
effect_label |
Optional character giving a human-friendly name of the effect being synthesized |
reported_effect_size |
Character specifying effect size to return:
Must be one of 'RD' (risk difference, default), 'RR' (log risk
ratio), 'OR' (log odds ratio), 'AS' (arcsine square root transformed
risk difference), or 'PETO' (log odds ratio estimated using Peto's
method). See |
random_effects |
TRUE for random effect model; FALSE for fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Once you generate an estimate with this function, you can visualize
it with plot_meta().
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
The conversion of events into suitable effect sizes is handled by
metafor::escalc()
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( "Online", "Original", "Online Pilot", "Exact replication" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( "Online", "In-Person", "Online", "In-Person" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, reported_effect_size = "RD" ) # Forest plot myplot_forst <- esci::plot_meta(estimate) # Add a categorical moderator (setting) estimate_moderator <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, moderator = setting, reported_effect_size = "RD" ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( "Online", "Original", "Online Pilot", "Exact replication" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( "Online", "In-Person", "Online", "In-Person" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, reported_effect_size = "RD" ) # Forest plot myplot_forst <- esci::plot_meta(estimate) # Add a categorical moderator (setting) estimate_moderator <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, moderator = setting, reported_effect_size = "RD" ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)
meta_proportion is suitable for synthesizing across multiple studies with
a categorical outcome variable. It takes as input the number of cases/events
and the number of samples in each study.
meta_proportion( data, cases, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", random_effects = TRUE, conf_level = 0.95 )meta_proportion( data, cases, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", random_effects = TRUE, conf_level = 0.95 )
data |
A dataframe or tibble |
cases |
A collection of cases/event counts, 1 per study, all integers, all > 0 |
ns |
A collection of sample sizes, 1 per study, all integers > 2 |
labels |
An optional collection of study labels |
moderator |
An optional factor to analyze as a categorical moderator, must have k > 2 per groups |
contrast |
An optional contrast to estimate between moderator levels; express as a vector of contrast weights with 1 weight per moderator level. |
effect_label |
Optional character giving a human-friendly name of the effect being synthesized |
random_effects |
TRUE for random effect model; FALSE for fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Once you generate an estimate with this function, you can visualize
it with plot_meta().
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( "Online", "Original", "Online Pilot", "Exact replication" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( "Online", "In-Person", "Online", "In-Person" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis, risk difference as effect size, moderator (setting) estimate_moderator <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies, moderator = setting ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( "Online", "Original", "Online Pilot", "Exact replication" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( "Online", "In-Person", "Online", "In-Person" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis, risk difference as effect size, moderator (setting) estimate_moderator <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies, moderator = setting ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)
meta_r is suitable for synthesizing across multiple studies that have
measured a linear correlation (Pearson's r) from two continuous variables.
meta_r( data, rs, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", random_effects = TRUE, conf_level = 0.95 )meta_r( data, rs, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = "My effect", random_effects = TRUE, conf_level = 0.95 )
data |
A dataframe or tibble |
rs |
A collection of Pearson's r values, 1 per study, all between -1 and 1, inclusive |
ns |
A collection of study sample sizes, all integers > 2 |
labels |
An optional collection of study labels |
moderator |
An optional factor to analyze as a categorical moderator, must have k > 2 per groups |
contrast |
An optional contrast to estimate between moderator levels; express as a vector of contrast weights with 1 weight per moderator level. |
effect_label |
Optional character giving a human-friendly name of the effect being synthesized |
random_effects |
TRUE for random effect model; FALSE for fixed effects |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
Once you generate an estimate with this function, you can visualize
it with plot_meta().
The meta-analytic effect size, confidence interval and heterogeneity
estimates all come from metafor::rma().
An esci-estimate object; a list of data frames and properties. Returned tables include:
es_meta - A data frame of meta-analytic effect sizes. If a moderator was defined, there is an additional row for each level of the moderator.
effect_label - Study label
effect_size - Effect size
LL - Lower bound of conf_level% confidence interval
UL - Upper bound of conf_level% confidence interval
SE - Expected standard error
k - Number of studies
diamond_ratio - ratio of random to fixed effects meta-analytic effect sizes
diamond_ratio_LL - lower bound of conf_level% confidence interval for diamond ratio
diamond_ratio_UL - upper bound of conf_level% confidence interval for diamond ratio
I2 - I2 measure of heterogeneity
I2_LL - Lower bound of conf_level% confidence interval for I2
I2_UL - upper bound of conf_level% confidence interval for I2
PI_LL - lower bound of conf_level% of prediction interval
PI_UL - upper bound of conf_level% of prediction interval
p - p value for the meta-analytic effect size, based on null of exactly 0
*width - width of the effect-size confidence interval
FE_effect_size - effect size of the fixed-effects model (regardless of if fixed effects was selected
RE_effect_size - effect size of the random-effects model (regardless of if random effects was selected
FE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
RE_CI_width - width of the fixed-effects confidence interval, used to calculate diamond ratio
es_heterogeneity - A data frame of of heterogeneity values and conf_level% CIs for the meta-analytic effect size. If a moderator was defined also reports heterogeneity estimates for each level of the moderator.
effect_label - study label
moderator_variable_name - if moderator passed, gives name of the moderator
moderator_level - 'Overall' and each level of moderator, if passed
measure - Name of the measure of heterogeneity
estimate - Value of the heterogeneity estimate
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
raw_data - A data from with one row for each study that was passed
label - study label
effect_size - effect size
weight - study weight in the meta analysis
sample_variance - expected level of sampling variation
SE - expected standard error
LL - lower bound of conf_level% confidence interval
UL - upper bound of conf_level% confidence interval
mean - used to calculate study p value; this is the d value entered for the study
sd - use to calculate study p value; set to 1 for each study
n - study sample size
p - p value for the study, based on null of exactly 0
# Data: See Introduction to the New Statistics, first edition esci_single_r <- data.frame( studies = c( 'Violin, viola' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'All' , 'Piano' , 'Piano' , 'Band' , 'Music majors' , 'Music majors' , 'All' ), rvalues = c( .67, .51, .4, .46, .47, .228, -.224, .104, .322, .231, .67, .41, .34, .31, .54, .583 ), sample_size = c( 109, 55, 19, 30, 19, 52, 24, 52, 16, 97, 57, 107, 178, 64, 19, 135 ), subsets = as.factor( c( 'Strings' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Strings' , 'Strings' , 'Strings' , 'Strings' ) ) ) # Meta-analysis, random effects, no moderator estimate <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis, random effects, moderator (subsets) estimate_moderator <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, subsets, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)# Data: See Introduction to the New Statistics, first edition esci_single_r <- data.frame( studies = c( 'Violin, viola' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'All' , 'Piano' , 'Piano' , 'Band' , 'Music majors' , 'Music majors' , 'All' ), rvalues = c( .67, .51, .4, .46, .47, .228, -.224, .104, .322, .231, .67, .41, .34, .31, .54, .583 ), sample_size = c( 109, 55, 19, 30, 19, 52, 24, 52, 16, 97, 57, 107, 178, 64, 19, 135 ), subsets = as.factor( c( 'Strings' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Strings' , 'Strings' , 'Strings' , 'Strings' ) ) ) # Meta-analysis, random effects, no moderator estimate <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis, random effects, moderator (subsets) estimate_moderator <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, subsets, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator)
This function calculates basic descriptive statistics for a numerical variable. It can calculate an overall summary, or broken down by the levels of a grouping variable. Inputs can be summary data, vectors, or a data frame.
overview( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My Outcome Variable", grouping_variable_name = NULL, conf_level = 0.95, assume_equal_variance = FALSE )overview( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_levels = NULL, outcome_variable_name = "My Outcome Variable", grouping_variable_name = NULL, conf_level = 0.95, assume_equal_variance = FALSE )
data |
|
outcome_variable |
|
grouping_variable |
|
means |
For summary data - A vector of 1 or more numerical means |
sds |
For summary data - A vector of standard deviations, same length as means |
ns |
For summary data - A vector of sample sizes, same length as means |
grouping_variable_levels |
For summary data - An optional vector of group labels, same length as means. If not passed, auto-generated. |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My Outcome Variable'. Ignored if a data-frame is passed, this argument is ignored. |
grouping_variable_name |
Optional friendly name for the grouping variable. If a data frame is passed, this argument is ignored. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
assume_equal_variance |
Defaults to FALSE |
If equal variance is not assumed, each group is treated independently. In
that case, the estimated mean, CI, and SE is from statpsych::ci.mean1(),
and the estimated median, CI, and SE is from statpsych::ci.median1(). If
equal variance is assumed, each group CI is calculated as with respect to all
group data, using statpsych::ci.lc.mean.bs() and
statpsych::ci.lc.median.bs()
Returns a table of descriptive statistics
overview
outcome_variable_name -
grouping_variable_name -
grouping_variable_level -
mean -
mean_LL -
mean_UL -
median -
median_LL -
median_UL -
sd -
min -
max -
q1 -
q3 -
n -
missing -
df -
mean_SE -
median_SE -
# example code esci::overview(data_latimier_3groups, "Test%", "Group")# example code esci::overview(data_latimier_3groups, "Test%", "Group")
This function calculated basic descriptive statistics for a categorical/ nominal variable. Inputs can be summary data, vectors, or a data frame.
overview_nominal( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, outcome_variable_levels = NULL, outcome_variable_name = "My Outcome Variable", grouping_variable_name = "My Grouping Variable", conf_level = 0.95, count_NA = FALSE )overview_nominal( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, outcome_variable_levels = NULL, outcome_variable_name = "My Outcome Variable", grouping_variable_name = "My Grouping Variable", conf_level = 0.95, count_NA = FALSE )
data |
|
outcome_variable |
|
grouping_variable |
|
cases |
For summary data - A vector of 1 or more counts, integers>0 |
outcome_variable_levels |
For summary data - An optional vector of group labels, same length as cases. If not passed, auto-generated. |
outcome_variable_name |
Optional friendly name for the outcome variable. Defaults to 'My Outcome Variable'. Ignored if a data-frame is passed, this argument is ignored. |
grouping_variable_name |
Optional friendly name for the grouping variable. Defaults to 'My Grouping Variable'. Ignored for summary data and for data frames – only used if vectors of data are passed. |
conf_level |
The confidence level for the confidence interval. Given in decimal form. Defaults to 0.95. |
count_NA |
Logical to count NAs (TRUE) in total N or not (FALSE) |
Returns a table of descriptive statistics
overview_nominal
outcome_variable_name -
outcome_variable_level -
cases -
n -
P -
P_LL -
P_UL -
P_SE -
P_adjusted -
ta_LL -
ta_UL -
# example code esci::overview_nominal(esci::data_latimier_3groups, "Group")# example code esci::overview_nominal(esci::data_latimier_3groups, "Group")
plot_correlation creates a ggplot2 plot suitable for visualizing an
estimate correlation between two continuous variables (Pearson's r). This
function can be passed an esci_estimate object generated by
estimate_r()
plot_correlation( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), rope = c(NA, NA), ggtheme = NULL )plot_correlation( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), rope = c(NA, NA), ggtheme = NULL )
estimate |
|
error_layout |
|
error_scale |
|
error_normalize |
|
rope |
|
ggtheme |
|
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_r( esci::data_thomason_1, Pretest, Posttest ) # To visualize the value of r myplot_correlation <- esci::plot_correlation(estimate_from_raw) # To visualize the data (scatterplot) and use regression to obtain Y' from X myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_raw <- esci::test_correlation( estimate_from_raw, rope = c(-0.1, 0.1) ) # From summary data estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50) # To visualize the value of r myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_correlation( estimate_from_summary, rope = c(-0.1, 0.1) )# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_r( esci::data_thomason_1, Pretest, Posttest ) # To visualize the value of r myplot_correlation <- esci::plot_correlation(estimate_from_raw) # To visualize the data (scatterplot) and use regression to obtain Y' from X myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_raw <- esci::test_correlation( estimate_from_raw, rope = c(-0.1, 0.1) ) # From summary data estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50) # To visualize the value of r myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_correlation( estimate_from_summary, rope = c(-0.1, 0.1) )
plot_describe Takes an estimate produced from estimate_magnitude
and produces a dotplot or histogram. It can mark various descriptive
statistics on the plot, including mean, median, sd, quartiles, and z lines.
If a percentile is passed, it color-codes data based on if it is above
or below that percentile.
plot_describe( estimate, type = c("histogram", "dotplot"), mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = NULL, histogram_bins = 12, ylim = c(0, NA), ybreaks = NULL, xlim = c(NA, NA), xbreaks = NULL, fill_regular = "#008DF9", fill_highlighted = "#E20134", color = "black", marker_size = 5, ggtheme = NULL )plot_describe( estimate, type = c("histogram", "dotplot"), mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = NULL, histogram_bins = 12, ylim = c(0, NA), ybreaks = NULL, xlim = c(NA, NA), xbreaks = NULL, fill_regular = "#008DF9", fill_highlighted = "#E20134", color = "black", marker_size = 5, ggtheme = NULL )
estimate |
A esci_estimate object with raw data an es_mean |
type |
histogram or dotplot |
mark_mean |
should mean be marked? |
mark_median |
should median be marked? |
mark_sd |
should mean be marked? |
mark_quartiles |
should mean be marked? |
mark_z_lines |
should z lines be marked? |
mark_percentile |
a percentile (0 to 1) to be marked |
histogram_bins |
number of bins if a histogram |
ylim |
2-length numeric vector |
ybreaks |
numeric >= 1 |
xlim |
2-length numeric vector |
xbreaks |
numeric >= 1 |
fill_regular |
color for |
fill_highlighted |
color for |
color |
outline color |
marker_size |
Size of markers |
ggtheme |
theme to apply, if any |
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# example code # Generate an estimate on a single continuous variable estimate <- esci::estimate_magnitude(esci::data_latimier_3groups, `Test%`) # Now describe the result, with a histogram myplot_hist <- plot_describe(estimate) # Same, but as a dotplot and mark the mean myplot_dots <- plot_describe(estimate, type = "dotplot", mark_mean = TRUE)# example code # Generate an estimate on a single continuous variable estimate <- esci::estimate_magnitude(esci::data_latimier_3groups, `Test%`) # Now describe the result, with a histogram myplot_hist <- plot_describe(estimate) # Same, but as a dotplot and mark the mean myplot_dots <- plot_describe(estimate, type = "dotplot", mark_mean = TRUE)
plot_interaction helps visualize the interaction from a 2x2 design.
It plots the 2 simple effects for the first factor and can also
help visualize the CIs on those simple effects. It is the comparison
between those simple effects that represents an interaction (the difference
in the difference).
You can pass esci-estimate objects generated
estimate_mdiff_2x2_between() or estimate_mdiff_2x2_mixed().
This function returns a ggplot2 object.
plot_interaction( estimate, effect_size = c("mean", "median"), show_CI = FALSE, ggtheme = NULL, line_count = 100, line_alpha = 0.02 )plot_interaction( estimate, effect_size = c("mean", "median"), show_CI = FALSE, ggtheme = NULL, line_count = 100, line_alpha = 0.02 )
estimate |
A esci_estimate object with raw data an es_mdiff_2x2_ function |
effect_size |
Optional; one of 'mean' or 'median' to determine the measure of central tendency plotted. Note that median is only available if the estimate was generated from raw data. Defaults to 'mean' |
show_CI |
Optional logical; set to TRUE to visualize the confidence intervals on each simple effect; defaults to FALSE |
ggtheme |
Optional ggplot2 theme object to specify the visual style of the
plot. Defaults to |
line_count |
Optional integer > 0 to specify the number of lines used to visualize the simple-effect confidence intervals; defaults to 100 |
line_alpha |
Optional numeric between 0 and 1 to specify the alpha (transparency) of the confidence interval lines; defaults to 0.02 |
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
data("data_videogameaggression") estimates_from_raw <- esci::estimate_mdiff_2x2_between( esci::data_videogameaggression, Agression, Violence, Difficulty ) # To visualize the estimated mean difference for the interaction myplot_from_raw <- esci::plot_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c("Evening", "Morning") grouping_variable_B_levels <- c("Sleep", "No Sleep") estimates_from_summary <- esci::estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = "Testing Time", grouping_variable_B_name = "Rest", outcome_variable_name = "False Memory Score", assume_equal_variance = TRUE ) # To visualize the estimated mean difference for the interaction plot_mdiff_interaction <- esci::plot_mdiff( estimates_from_summary$interaction, effect_size = "mean" ) # To visualize the interaction as a line plot plot_interaction_line <- esci::plot_interaction(estimates_from_summary) # Same but with fan effect representing each simple-effect CI plot_interaction_line_CI <- esci::plot_interaction( estimates_from_summary, show_CI = TRUE ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_summary$interaction, effect_size = "mean" )data("data_videogameaggression") estimates_from_raw <- esci::estimate_mdiff_2x2_between( esci::data_videogameaggression, Agression, Violence, Difficulty ) # To visualize the estimated mean difference for the interaction myplot_from_raw <- esci::plot_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_raw$interaction, effect_size = "median" ) # From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c("Evening", "Morning") grouping_variable_B_levels <- c("Sleep", "No Sleep") estimates_from_summary <- esci::estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = "Testing Time", grouping_variable_B_name = "Rest", outcome_variable_name = "False Memory Score", assume_equal_variance = TRUE ) # To visualize the estimated mean difference for the interaction plot_mdiff_interaction <- esci::plot_mdiff( estimates_from_summary$interaction, effect_size = "mean" ) # To visualize the interaction as a line plot plot_interaction_line <- esci::plot_interaction(estimates_from_summary) # Same but with fan effect representing each simple-effect CI plot_interaction_line_CI <- esci::plot_interaction( estimates_from_summary, show_CI = TRUE ) # To conduct a hypothesis test on the mean difference res_htest_from_raw <- esci::test_mdiff( estimates_from_summary$interaction, effect_size = "mean" )
plot_magnitude creates a ggplot2 plot suitable for visualizing
the results of a study with one group and one or more continuous outcome
variables. It can highlight either the mean or median of each outcome
variable. This function can be passed an esci_estimate object generated by
estimate_magnitude()
plot_magnitude( estimate, effect_size = c("mean", "median"), data_layout = c("random", "swarm", "none"), data_spread = 0.25, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_nudge = 0.35, error_normalize = c("groups", "all", "panels"), rope = c(NA, NA), ggtheme = NULL )plot_magnitude( estimate, effect_size = c("mean", "median"), data_layout = c("random", "swarm", "none"), data_spread = 0.25, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_nudge = 0.35, error_normalize = c("groups", "all", "panels"), rope = c(NA, NA), ggtheme = NULL )
estimate |
|
effect_size |
|
data_layout |
|
data_spread |
|
error_layout |
|
error_scale |
|
error_nudge |
|
error_normalize |
|
rope |
|
ggtheme |
|
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == "Pen", ], outcome_variable = transcription ) # To visualize the estimate myplot_from_raw <- esci::plot_magnitude( estimate_from_raw, effect_size = "median" ) # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate_from_summary <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) # To visualize the estimate myplot_from_summary <- esci::plot_magnitude( estimate_from_summary, effect_size = "mean" )# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == "Pen", ], outcome_variable = transcription ) # To visualize the estimate myplot_from_raw <- esci::plot_magnitude( estimate_from_raw, effect_size = "median" ) # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate_from_summary <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) # To visualize the estimate myplot_from_summary <- esci::plot_magnitude( estimate_from_summary, effect_size = "mean" )
plot_mdiff helps visualize comparisons of a continuous outcome
variable between conditions. It can plot raw data (if available) for each
condition, the mean or median (raw data only) for each condition, and
it emphasizes a 1-df comparison among conditions, plotting the estimated
difference and its confidence interval with a difference axis.
You can pass esci-estimate objects generated
by estimate_mdiff_one(), estimate_mdiff_two(),
estimate_mdiff_paired(), estimate_mdiff_ind_contrast(),
estimate_mdiff_2x2_between(), and estimate_mdiff_2x2_mixed().
This function returns a ggplot2 object.
plot_mdiff( estimate, effect_size = c("mean", "median"), data_layout = c("random", "swarm", "none"), data_spread = 0.15, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_nudge = 0.4, error_normalize = c("groups", "all", "panels"), difference_axis_units = c("raw", "sd"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), rope_units = c("raw", "sd"), ggtheme = NULL )plot_mdiff( estimate, effect_size = c("mean", "median"), data_layout = c("random", "swarm", "none"), data_spread = 0.15, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_nudge = 0.4, error_normalize = c("groups", "all", "panels"), difference_axis_units = c("raw", "sd"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), rope_units = c("raw", "sd"), ggtheme = NULL )
estimate |
An esci-estimate object generated by an estimate_mdiff_ function |
effect_size |
Optional; one of 'mean' or 'median' to determine the measure of central tendency plotted. Note that median is only available if the estimate was generated from raw data. Defaults to 'mean' |
data_layout |
Optional; one of 'random', 'swarm', or 'none' to determine how raw data (if available) will be displayed. Defaults to 'random' |
data_spread |
Optional numeric determining width raw data will use in each condition. Defaults to 0.15 (relative to 1 unit per condition) |
error_layout |
Optional; one of 'halfeye', 'eye', 'gradient' or 'none' to determine how expected error distribution will be displayed for each estimated parameter. Defaults to 'halfeye'. Currently does not apply if 'median' is selected as effect size, in which case a simple error bar will be used |
error_scale |
Optional numeric determining width of the expected error distribution. Defaults to 0.3 |
error_nudge |
Optional numeric determining degree to which measures of central tendency will be shifted to the right of the raw data; defaults to 0.4 |
error_normalize |
Optional; one of 'groups', 'all', or 'panels' to determine how width of the expected error distributions will be normalized. Defaults to 'groups'. See documentation in ggdist |
difference_axis_units |
Optional; one of 'raw' or 'sd' to determine if markings on the difference axis will be in raw-score units or in standard-deviation units. For 'sd' the standard deviation of the mean difference is used, and this is true even if 'median' is selected as the effect size |
difference_axis_breaks |
Optional numeric > 1 of suggested number of breaks for the difference axis. Defaults to 5 |
difference_axis_space |
Optional numeric > 0 to indicate spacing to the difference axis. Defaults to 1 |
simple_contrast_labels |
Optional logical to determine if contrasts are given simple labels ('Reference', 'Comparison', 'Difference') or more descriptive labels based on the contrast specified. |
ylim |
Optional 2-item vector specifying y-axis limits. Defaults to c(NA NA); Use NA to specify auto-limit. |
ybreaks |
Optional numeric > 2 for suggested number of y-axis breaks; defaults to 5 |
rope |
Optional 2-item vector with item 2 >= item 1. Use to specify a range of values to use to visualize a hypothesis test. If both values are the same, a point-null hypothesis test will be visualized. If item2 > item1 an interval-null hypothesis test will be visualized. Defaults to c(NA, NA), which is to not visualize a hypothesis test |
rope_units |
Optional; one of 'raw' or 'sd' to indicate units of the rope passed. Defaults to 'raw' |
ggtheme |
Optional ggplot2 theme object to specify the visual style of the
plot. Defaults to |
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # To visualize the estimated median difference (raw data only) myplot_from_raw <- esci::plot_mdiff( estimate_from_raw, effect_size = "median" ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median", rope = c(-2, 2) ) # From summary data estimate_from_summary <- esci::estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c("Ref-Laptop", "Comp-Pen"), outcome_variable_name = "% Transcription", grouping_variable_name = "Note-taking type", assume_equal_variance = TRUE ) # To visualize the estimated mean difference myplot <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )# From raw data data("data_penlaptop1") estimate_from_raw <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # To visualize the estimated median difference (raw data only) myplot_from_raw <- esci::plot_mdiff( estimate_from_raw, effect_size = "median" ) # To conduct a hypothesis test res_htest_from_raw <- esci::test_mdiff( estimate_from_raw, effect_size = "median", rope = c(-2, 2) ) # From summary data estimate_from_summary <- esci::estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c("Ref-Laptop", "Comp-Pen"), outcome_variable_name = "% Transcription", grouping_variable_name = "Note-taking type", assume_equal_variance = TRUE ) # To visualize the estimated mean difference myplot <- esci::plot_mdiff( estimate_from_summary, effect_size = "mean" ) # To conduct a hypothesis test res_htest_from_summary <- esci::test_mdiff( estimate_from_summary, effect_size = "mean", rope = c(-2, 2) )
‘plot_meta’ returns a ggplot2 object visualizing the results of
a meta-analysis, showing each study effect size and CI, the overall effect
size and CI as a diamond, effect sizes estimated at each moderator level
(if defined), and (optionally) prediction intervals for subsequent studies.
This function requires as input an esci_estimate object generated by an
esci meta-analysis function: meta_any(), meta_d1(),
meta_d2(), meta_mdiff_two(), meta_mean(),
meta_pdiff_two(), meta_proportion(), and meta_r().
plot_meta( estimate, mark_zero = TRUE, include_PIs = FALSE, report_CIs = FALSE, explain_DR = FALSE, meta_diamond_height = 0.35, ggtheme = ggplot2::theme_classic() )plot_meta( estimate, mark_zero = TRUE, include_PIs = FALSE, report_CIs = FALSE, explain_DR = FALSE, meta_diamond_height = 0.35, ggtheme = ggplot2::theme_classic() )
estimate |
|
mark_zero |
|
include_PIs |
|
report_CIs |
|
explain_DR |
|
meta_diamond_height |
|
ggtheme |
|
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis: random effects, moderator estimate_moderator <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator) # Meta-analysis: random effects, moderator, output d_s estimate_moderator_d <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator_d <- esci::plot_meta(estimate_moderator_d)# Data set -- see Introduction to the New Statistics, 2nd edition data("data_mccabemichael_brain") # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest <- esci::plot_meta(estimate) # Meta-analysis: random effects, moderator estimate_moderator <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator <- esci::plot_meta(estimate_moderator) # Meta-analysis: random effects, moderator, output d_s estimate_moderator_d <- esci::meta_mdiff_two( data = esci::data_mccabemichael_brain, comparison_means = "M Brain", comparison_sds = "s Brain", comparison_ns = "n Brain", reference_means = "M No Brain", reference_sds = "s No Brain", reference_ns = "n No Brain", labels = "Study name", moderator = "Research group", effect_label = "Brain Photo Rating - No Brain Photo Rating", assume_equal_variance = TRUE, random_effects = TRUE ) # Forest plot myplot_forest_moderator_d <- esci::plot_meta(estimate_moderator_d)
plot_pdiff helps visualize comparisons of a categorical outcome
variable between conditions. It plots proportions of cases for each
level of grouping variable and emphasizes a 1-df comparison among
conditions, plotting the estimated difference and its confidence interval
with a difference axis. You can pass esci-estimate objects generated
by estimate_pdiff_one(), estimate_pdiff_two(),
estimate_pdiff_paired(), estimate_pdiff_ind_contrast()
This function returns a ggplot2 object.
plot_pdiff( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )plot_pdiff( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )
estimate |
An esci-estimate object generated by an estimate_pdiff_ function |
error_layout |
Optional; one of 'halfeye', 'eye', 'gradient' or 'none' to determine how expected error distribution will be displayed for each estimated parameter. Defaults to 'halfeye'. Currently does not apply if 'median' is selected as effect size, in which case a simple error bar will be used |
error_scale |
Optional numeric determining width of the expected error distribution. Defaults to 0.3 |
error_normalize |
Optional; one of 'groups', 'all', or 'panels' to determine how width of the expected error distributions will be normalized. Defaults to 'groups'. See documentation in ggdist |
difference_axis_breaks |
Optional numeric > 1 of suggested number of breaks for the difference axis. Defaults to 5 |
difference_axis_space |
Optional numeric > 0 to indicate spacing to the difference axis. Defaults to 1 |
simple_contrast_labels |
Optional logical to determine if contrasts are given simple labels ('Reference', 'Comparison', 'Difference') or more descriptive labels based on the contrast specified. |
ylim |
Optional 2-item vector specifying y-axis limits. Defaults to c(NA NA); Use NA to specify auto-limit. |
ybreaks |
Optional numeric > 2 for suggested number of y-axis breaks; defaults to 5 |
rope |
Optional 2-item vector with item 2 >= item 1. Use to specify a range of values to use to visualize a hypothesis test. If both values are the same, a point-null hypothesis test will be visualized. If item2 > item1 an interval-null hypothesis test will be visualized. Defaults to c(NA, NA), which is to not visualize a hypothesis test |
ggtheme |
Optional ggplot2 theme object to specify the visual style of the
plot. Defaults to |
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_two( esci::data_campus_involvement, CommuterStatus, Gender ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary_data estimate_from_summary <- esci::estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c("Original", "Replication"), conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) #' # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)data("data_campus_involvement") estimate_from_raw <- esci::estimate_pdiff_two( esci::data_campus_involvement, CommuterStatus, Gender ) # To visualize the estimate myplot_from_raw <- esci::plot_pdiff(estimate_from_raw) # To conduct a hypothesis test res_htest_from_raw <- esci::test_pdiff(estimate_from_raw) # From summary_data estimate_from_summary <- esci::estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c("Original", "Replication"), conf_level = 0.95 ) # To visualize the estimate myplot_from_summary <- esci::plot_pdiff(estimate_from_summary) #' # To conduct a hypothesis test res_htest_from_summary <- esci::test_pdiff(estimate_from_summary)
plot_proportion creates a ggplot2 plot suitable for visualizing an
estimated proportion from a categorical variable. This function can be passed
an esci_estimate object generated by estimate_proportion()
plot_proportion( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), rope = c(NA, NA), plot_possible = FALSE, ggtheme = NULL )plot_proportion( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), rope = c(NA, NA), plot_possible = FALSE, ggtheme = NULL )
estimate |
|
error_layout |
|
error_scale |
|
error_normalize |
|
rope |
|
plot_possible |
|
ggtheme |
|
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_proportion( esci::data_campus_involvement, CommuterStatus ) # To visualize the estimate myplot_from_raw <- esci::plot_proportion(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c("Affected", "Not Affected") ) # To visualize the estimate myplot_from_summary<- esci::plot_proportion(estimate_from_summary)# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_proportion( esci::data_campus_involvement, CommuterStatus ) # To visualize the estimate myplot_from_raw <- esci::plot_proportion(estimate_from_raw) # From summary data estimate_from_summary <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c("Affected", "Not Affected") ) # To visualize the estimate myplot_from_summary<- esci::plot_proportion(estimate_from_summary)
plot_rdiff helps visualize comparisons of Pearson's r
estimates between conditions. It plots the Pearson's r value for each
level of a grouping variable and emphasizes a 1-df comparison among
conditions, plotting the estimated difference and its confidence interval
with a difference axis. You can pass esci-estimate objects generated
by estimate_rdiff_two(). This function returns a ggplot2 object.
plot_rdiff( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), difference_axis_breaks = 5, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )plot_rdiff( estimate, error_layout = c("halfeye", "eye", "gradient", "none"), error_scale = 0.3, error_normalize = c("groups", "all", "panels"), difference_axis_breaks = 5, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )
estimate |
An esci-estimate object generated by an estimate_pdiff_ function |
error_layout |
Optional; one of 'halfeye', 'eye', 'gradient' or 'none' to determine how expected error distribution will be displayed for each estimated parameter. Defaults to 'halfeye'. Currently does not apply if 'median' is selected as effect size, in which case a simple error bar will be used |
error_scale |
Optional numeric determining width of the expected error distribution. Defaults to 0.3 |
error_normalize |
Optional; one of 'groups', 'all', or 'panels' to determine how width of the expected error distributions will be normalized. Defaults to 'groups'. See documentation in ggdist |
difference_axis_breaks |
Optional numeric > 1 of suggested number of breaks for the difference axis. Defaults to 5 |
simple_contrast_labels |
Optional logical to determine if contrasts are given simple labels ('Reference', 'Comparison', 'Difference') or more descriptive labels based on the contrast specified. |
ylim |
Optional 2-item vector specifying y-axis limits. Defaults to c(NA NA); Use NA to specify auto-limit. |
ybreaks |
Optional numeric > 2 for suggested number of y-axis breaks; defaults to 5 |
rope |
Optional 2-item vector with item 2 >= item 1. Use to specify a range of values to use to visualize a hypothesis test. If both values are the same, a point-null hypothesis test will be visualized. If item2 > item1 an interval-null hypothesis test will be visualized. Defaults to c(NA, NA), which is to not visualize a hypothesis test |
ggtheme |
Optional ggplot2 theme object to specify the visual style of the
plot. Defaults to |
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_rdiff_two( esci::data_campus_involvement, GPA, SWB, Gender ) # To visualize the difference in r myplot_from_raw <- esci::plot_rdiff(estimate_from_raw) # To visualize the data (scatterplot) by group myplot_scatter <- esci::plot_scatter(estimate_from_raw) # To evaluate a hypothesis (by default: point null of exaclty 0): res_htest_from_raw <- esci::test_rdiff( estimate_from_raw ) # From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c("Females", "Males"), x_variable_name = "Satisfaction with life", y_variable_name = "Body satisfaction", grouping_variable_name = "Gender", conf_level = .95 ) myplot_from_summary <- esci::plot_rdiff(estimate) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_rdiff( estimate, rope = c(-0.1, 0.1) )# From raw data data("data_campus_involvement") estimate_from_raw <- esci::estimate_rdiff_two( esci::data_campus_involvement, GPA, SWB, Gender ) # To visualize the difference in r myplot_from_raw <- esci::plot_rdiff(estimate_from_raw) # To visualize the data (scatterplot) by group myplot_scatter <- esci::plot_scatter(estimate_from_raw) # To evaluate a hypothesis (by default: point null of exaclty 0): res_htest_from_raw <- esci::test_rdiff( estimate_from_raw ) # From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c("Females", "Males"), x_variable_name = "Satisfaction with life", y_variable_name = "Body satisfaction", grouping_variable_name = "Gender", conf_level = .95 ) myplot_from_summary <- esci::plot_rdiff(estimate) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_rdiff( estimate, rope = c(-0.1, 0.1) )
plot_scatter returns a ggplot2 object of data from two continuous
variables. Can indicate regression line and its confidence
interval,prediction intervals regression residuals and more. This function
requires as input an esci_estimate object generated by estimate_r()
plot_scatter( estimate, show_line = FALSE, show_line_CI = FALSE, show_PI = FALSE, show_residuals = FALSE, show_mean_lines = FALSE, show_r = FALSE, predict_from_x = NULL, plot_as_z = FALSE, ggtheme = ggplot2::theme_classic() )plot_scatter( estimate, show_line = FALSE, show_line_CI = FALSE, show_PI = FALSE, show_residuals = FALSE, show_mean_lines = FALSE, show_r = FALSE, predict_from_x = NULL, plot_as_z = FALSE, ggtheme = ggplot2::theme_classic() )
estimate |
|
show_line |
|
show_line_CI |
|
show_PI |
|
show_residuals |
|
show_mean_lines |
|
show_r |
|
predict_from_x |
|
plot_as_z |
|
ggtheme |
|
This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).
Expect breaking changes as this function is improved for general use. Work still do be done includes:
Revise to avoid deprecated ggplot features
Revise for consistent ability to control aesthetics and consistent layer names
Returns a ggplot object
# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_r( esci::data_thomason_1, Pretest, Posttest ) # To visualize the value of r myplot_correlation <- esci::plot_correlation(estimate_from_raw) # To visualize the data (scatterplot) and use regression to obtain Y' from X myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_raw <- esci::test_correlation( estimate_from_raw, rope = c(-0.1, 0.1) ) # From summary data estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50) # To visualize the value of r myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_correlation( estimate_from_summary, rope = c(-0.1, 0.1) )# From raw data data("data_thomason_1") estimate_from_raw <- esci::estimate_r( esci::data_thomason_1, Pretest, Posttest ) # To visualize the value of r myplot_correlation <- esci::plot_correlation(estimate_from_raw) # To visualize the data (scatterplot) and use regression to obtain Y' from X myplot_scatter_from_raw <- esci::plot_scatter(estimate_from_raw, predict_from_x = 10) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_raw <- esci::test_correlation( estimate_from_raw, rope = c(-0.1, 0.1) ) # From summary data estimate_from_summary <- esci::estimate_r(r = 0.536, n = 50) # To visualize the value of r myplot_correlation_from_summary <- esci::plot_correlation(estimate_from_summary) # To evaluate a hypothesis (interval null from -0.1 to 0.1): res_htest_from_summary <- esci::test_correlation( estimate_from_summary, rope = c(-0.1, 0.1) )
Pretties up the printing of a complex esci_estimate object.
## S3 method for class 'esci_estimate' print(x, ..., verbose = FALSE)## S3 method for class 'esci_estimate' print(x, ..., verbose = FALSE)
x |
|
... |
S3 signature for generic plot function. |
verbose |
|
test_correlation is suitable for testing a hypothesis about a
the strength of correlation between two continuous variables (designs
in which Pearson's r is a suitable measure of correlation).
test_correlation(estimate, rope = c(0, 0), output_html = FALSE)test_correlation(estimate, rope = c(0, 0), output_html = FALSE)
estimate |
|
rope |
|
output_html |
|
This function can be passed an esci_estimate object generated by
estimate_r().
It can test hypotheses about a specific value for the difference (a point null) or about a range of values (an interval null)
Returns a list with 1-2 data frames
point_null - always returned
test_type - 'Nil hypothesis test', meaning a test against H0 = 0
outcome_variable_name - Name of the outcome variable
effect - Label for the effect being tested
null_words - Express the null in words
confidence - Confidence level, integer (95 for 95%, etc.)
LL - Lower boundary of the confidence% CI for the effect
UL - Upper boundary of the confidence% CI for the effect
CI - Character representation of the CI for the effect
CI_compare - Text description of relation between CI and null
t - If applicable, t value for hypothesis test
df - If applicable, degrees of freedom for hypothesis test
p - If applicable, p value for hypothesis test
p_result - Text representation of p value obtained
null_decision - Text represention of the decision for the null
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
interval_null - returned only if an interval null is specified
test_type - 'Practical significance test', meaning a test against an interval null
outcome_variable_name -
effect - Name of the outcome variable
rope - Test representation of null interval
confidence - Confidence level, integer (95 for 95%, etc.)
CI - Character representation of the CI for the effect
rope_compare - Text description of relation between CI and null interval
p_result - Text representation of p value obtained
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
# example code estimate <- esci::estimate_r(r = 0.536, n = 50) # Test against a point null of exactly 0 test_correlation(estimate) # Test against an interval null (-0.1, 0.1) test_correlation(estimate, rope = c(-0.1, 0.1))# example code estimate <- esci::estimate_r(r = 0.536, n = 50) # Test against a point null of exactly 0 test_correlation(estimate) # Test against an interval null (-0.1, 0.1) test_correlation(estimate, rope = c(-0.1, 0.1))
test_mdiff is suitable for conducting a testing a hypothesis about the
magnitude of difference between two conditions for a continuous outcome
variable. It can test hypotheses about differences in means or medians for
both independent and paired designs.
test_mdiff( estimate, effect_size = c("mean", "median"), rope = c(0, 0), rope_units = c("raw", "sd"), output_html = FALSE )test_mdiff( estimate, effect_size = c("mean", "median"), rope = c(0, 0), rope_units = c("raw", "sd"), output_html = FALSE )
estimate |
|
effect_size |
|
rope |
|
rope_units |
|
output_html |
|
This function can be passed an esci_estimate object generated by
estimate_mdiff_one(), estimate_mdiff_two(),
estimate_mdiff_paired(), or estimate_mdiff_ind_contrast().
It can test hypotheses about a specific value for the difference (a point null) or about a range of values (an interval null)
Returns a list with 1-2 data frames
point_null - always returned
test_type - 'Nil hypothesis test', meaning a test against H0 = 0
outcome_variable_name - Name of the outcome variable
effect - Label for the effect being tested
null_words - Express the null in words
confidence - Confidence level, integer (95 for 95%, etc.)
LL - Lower boundary of the confidence% CI for the effect
UL - Upper boundary of the confidence% CI for the effect
CI - Character representation of the CI for the effect
CI_compare - Text description of relation between CI and null
t - If applicable, t value for hypothesis test
df - If applicable, degrees of freedom for hypothesis test
p - If applicable, p value for hypothesis test
p_result - Text representation of p value obtained
null_decision - Text represention of the decision for the null
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
interval_null - returned only if an interval null is specified
test_type - 'Practical significance test', meaning a test against an interval null
outcome_variable_name -
effect - Name of the outcome variable
rope - Test representation of null interval
confidence - Confidence level, integer (95 for 95%, etc.)
CI - Character representation of the CI for the effect
rope_compare - Text description of relation between CI and null interval
p_result - Text representation of p value obtained
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
# example code data("data_penlaptop1") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # Test mean difference against point null of 0 esci::test_mdiff( estimate, effect_size = "mean" ) # Test median difference against point null of 0 # Note that t, df, p return NA because test is completed # by interval. esci::test_mdiff( estimate, effect_size = "median" ) # Test mean difference against interval null of -10 to 10 esci::test_mdiff( estimate, effect_size = "mean", rope = c(-10, 10) ) # Test mean difference against interval null of d (-0.20, 0.20) d = 0.2 is often # thought of as a small effect, so this test examines if the effect is # negligible (clearly between negligble and small), substantive (clearly more # than small), or unclear. The d boundaries provided are converted to raw scores # and then the CI of the observed effect is compared to the raw-score boundaries esci::test_mdiff( estimate, effect_size = "mean", rope = c(-0.2, 0.2), rope_units = "sd" )# example code data("data_penlaptop1") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # Test mean difference against point null of 0 esci::test_mdiff( estimate, effect_size = "mean" ) # Test median difference against point null of 0 # Note that t, df, p return NA because test is completed # by interval. esci::test_mdiff( estimate, effect_size = "median" ) # Test mean difference against interval null of -10 to 10 esci::test_mdiff( estimate, effect_size = "mean", rope = c(-10, 10) ) # Test mean difference against interval null of d (-0.20, 0.20) d = 0.2 is often # thought of as a small effect, so this test examines if the effect is # negligible (clearly between negligble and small), substantive (clearly more # than small), or unclear. The d boundaries provided are converted to raw scores # and then the CI of the observed effect is compared to the raw-score boundaries esci::test_mdiff( estimate, effect_size = "mean", rope = c(-0.2, 0.2), rope_units = "sd" )
test_pdiff is suitable for testing a hypothesis about a
difference in proportions between two conditions with a categorical outcome
variable. It can test hypotheses for both independent and paired designs.
test_pdiff(estimate, rope = c(0, 0), output_html = FALSE)test_pdiff(estimate, rope = c(0, 0), output_html = FALSE)
estimate |
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rope |
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This function can be passed an esci_estimate object generated by
estimate_pdiff_one(), estimate_pdiff_two(),
estimate_pdiff_paired(), or estimate_pdiff_ind_contrast().
It can test hypotheses about a specific value for the difference (a point null) or about a range of values (an interval null)
Returns a list with 1-2 data frames
point_null - always returned
test_type - 'Nil hypothesis test', meaning a test against H0 = 0
outcome_variable_name - Name of the outcome variable
effect - Label for the effect being tested
null_words - Express the null in words
confidence - Confidence level, integer (95 for 95%, etc.)
LL - Lower boundary of the confidence% CI for the effect
UL - Upper boundary of the confidence% CI for the effect
CI - Character representation of the CI for the effect
CI_compare - Text description of relation between CI and null
t - If applicable, t value for hypothesis test
df - If applicable, degrees of freedom for hypothesis test
p - If applicable, p value for hypothesis test
p_result - Text representation of p value obtained
null_decision - Text represention of the decision for the null
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
interval_null - returned only if an interval null is specified
test_type - 'Practical significance test', meaning a test against an interval null
outcome_variable_name -
effect - Name of the outcome variable
rope - Test representation of null interval
confidence - Confidence level, integer (95 for 95%, etc.)
CI - Character representation of the CI for the effect
rope_compare - Text description of relation between CI and null interval
p_result - Text representation of p value obtained
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c("Original", "Replication"), conf_level = 0.95 ) # Test against null of exactly test_pdiff(estimate) # Test against null of (-0.1, 0.1) test_pdiff(estimate, rope = c(-0.1, 0.1))estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c("Original", "Replication"), conf_level = 0.95 ) # Test against null of exactly test_pdiff(estimate) # Test against null of (-0.1, 0.1) test_pdiff(estimate, rope = c(-0.1, 0.1))
test_rdiff is suitable for testing a hypothesis about a
difference in correlation (r) between two conditions.
At the moment, it can only test hypotheses for independent-group designs.
test_rdiff(estimate, rope = c(0, 0), output_html = FALSE)test_rdiff(estimate, rope = c(0, 0), output_html = FALSE)
estimate |
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rope |
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output_html |
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This function can be passed an esci_estimate object generated by
estimate_rdiff_two().
It can test hypotheses about a specific value for the difference (a point null) or about a range of values (an interval null)
Returns a list with 1-2 data frames
point_null - always returned
test_type - 'Nil hypothesis test', meaning a test against H0 = 0
outcome_variable_name - Name of the outcome variable
effect - Label for the effect being tested
null_words - Express the null in words
confidence - Confidence level, integer (95 for 95%, etc.)
LL - Lower boundary of the confidence% CI for the effect
UL - Upper boundary of the confidence% CI for the effect
CI - Character representation of the CI for the effect
CI_compare - Text description of relation between CI and null
t - If applicable, t value for hypothesis test
df - If applicable, degrees of freedom for hypothesis test
p - If applicable, p value for hypothesis test
p_result - Text representation of p value obtained
null_decision - Text represention of the decision for the null
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
interval_null - returned only if an interval null is specified
test_type - 'Practical significance test', meaning a test against an interval null
outcome_variable_name -
effect - Name of the outcome variable
rope - Test representation of null interval
confidence - Confidence level, integer (95 for 95%, etc.)
CI - Character representation of the CI for the effect
rope_compare - Text description of relation between CI and null interval
p_result - Text representation of p value obtained
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
# example code estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c("Females", "Males"), x_variable_name = "Satisfaction with life", y_variable_name = "Body satisfaction", grouping_variable_name = "Gender", conf_level = .95 ) test_rdiff(estimate)# example code estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c("Females", "Males"), x_variable_name = "Satisfaction with life", y_variable_name = "Body satisfaction", grouping_variable_name = "Gender", conf_level = .95 ) test_rdiff(estimate)