Package 'pubh'

Description

axis_labs takes labels from labelled data to use them as axis-labels for plots generated by gformula or ggplot2.

Usage

axis_labs(object)

Arguments

Details

This functions is helpful when data has been already labelled by sjlabelled. It retrives variable labels and use them for plotting.

Value

A ggplot2 object.

Examples

data(kfm, package = "ISwR")
require(sjlabelled, quietly = TRUE)
kfm <- kfm |>
  var_labels(
    dl.milk = "Breast-milk intake (dl/day)",
    sex = "Sex",
    weight = "Child weight (kg)",
    ml.suppl = "Milk substitute (ml/day)",
    mat.weight = "Maternal weight (kg)",
    mat.height = "Maternal height (cm)"
  )

kfm |>
  gf_point(weight ~ dl.milk) |>
  gf_lm(col = 2, interval = "confidence", col = 2) |>
  axis_labs()

kfm |>
  box_plot(dl.milk ~ sex, fill = "thistle", alpha = 0.8) |>
  axis_labs() |>
  gf_star(x1 = 1, y1 = 10.9, x2 = 2, y2 = 11, y3 = 11.2)

Description

bar_error constructs bar charts in with error bars showing 95 confidence intervals around mean values. High of bars represent mean values.

Usage

bar_error(
  object = NULL,
  formula = NULL,
  data = NULL,
  fill = "indianred3",
  col = "black",
  alpha = 0.7,
  ...
)

Arguments

Examples

require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)
data(birthwt, package = "MASS")

birthwt <- birthwt |>
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    Race = factor(race > 1, labels = c("White", "Non-white"))
  ) |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status"
  )

birthwt |>
  bar_error(bwt ~ smoke, fill = "plum3")

birthwt |>
  bar_error(bwt ~ smoke | Race, fill = "plum3")

birthwt |>
  bar_error(bwt ~ smoke, fill = ~Race)

Description

A randomised, double-blind, placebo-controlled trial of intravenous ibuprofen in 455 patients who had sepsis, defined as fever, tachycardia, tachypnea, and acute failure of at least one organ system.

Usage

Bernard

Format

A labelled tibble with 455 rows and 9 variables:

id

Patient ID

treat

Treatment, factor with levels "Placebo" and "Ibuprofen".

race

Race/ethnicity, factor with levels "White", "African American" and "Other".

fate

Mortality status at 30 days, factor with levels "Alive" and "Dead".

apache

Baseline APACHE score.

o2del

Oxygen delivery at baseline.

followup

Follow-up time in hours.

temp0

Baseline temperature in centigrades.

temp10

Temperature after 36 hr in centigrades.

Source

Bernard, GR, et al. (1997) The effects of ibuprofen on the physiology and survival of patients with sepsis, N Engl J Med 336: 912–918.

Description

Bland-Altman agreement plots.

Usage

bland_altman(
  object = NULL,
  formula = NULL,
  data = NULL,
  pch = 20,
  size = 1,
  col = "black",
  transform = FALSE,
  ...
)

Arguments

Details

bland_altman constructs Bland-Altman agreement plots.

Variables in formula are continuous paired observations. When the distribution of the outcome is not normal, but becomes normal with a log-transformation, bland_altman can plot the ratio between outcomes (difference in the log scale) by using option transform = TRUE.

Examples

data(wright, package = "ISwR")

wright |>
  bland_altman(mini.wright ~ std.wright,
    pch = 16,
    ylab = "Large-mini expiratory flow rate (l/min)",
    xlab = "Mean expiratory flow rate (l/min)"
  ) |>
  gf_labs(
    y = "Large-mini expiratory flow rate (l/min)",
    x = "Mean expiratory flow rate (l/min)"
  )

data(Sharples)

Sharples |>
  bland_altman(srweight ~ weight, transform = TRUE) |>
  gf_labs(x = "Mean of weights (kg)", y = "Measured weight / Self-reported weight")

Description

box_plot is a wrap function that calls gf_boxplot to construct more aesthetic box plots.

Usage

box_plot(
  object = NULL,
  formula = NULL,
  data = NULL,
  fill = "indianred3",
  alpha = 0.7,
  outlier.shape = 20,
  outlier.size = 1,
  ...
)

Arguments

Examples

data(kfm, package = "ISwR")
require(sjlabelled, quietly = TRUE)
kfm <- kfm |>
  var_labels(
    dl.milk = "Breast-milk intake (dl/day)",
    sex = "Sex",
    weight = "Child weight (kg)",
    ml.suppl = "Milk substitute (ml/day)",
    mat.weight = "Maternal weight (kg)",
    mat.height = "Maternal height (cm)"
  )

kfm |>
  box_plot(dl.milk ~ sex, fill = "thistle", alpha = 0.8)

t.test(dl.milk ~ sex, data = kfm)

kfm |>
  box_plot(dl.milk ~ sex, fill = "thistle", alpha = 0.8) |>
  gf_star(1, 10.9, 2, 11, 11.4, legend = "p = 0.035", size = 2.5)

Description

A data set containing the prevalence of Helicobacter pylori infection in preschool children according to parental history of duodenal or gastric ulcer.

Usage

Brenner

Format

A labelled tibble with 863 rows and 2 variables:

ulcer

History of duodenal or gastric ulcer, factor with levels "No" and "Yes".

infected

Infected with Helicobacter pylori, factor with levels "No" and "Yes".

Source

Brenner H, Rothenbacher D, Bode G, Adler G (1998) Parental history of gastric or duodenal ulcer and prevalence of Helicobacter pylori infection in preschool children: population based study. BMJ 316:665.

Description

bst estimates confidence intervals around the mean, median or geo_mean.

Usage

bst(x, stat = "mean", n = 1000, CI = 95, digits = 2)

Arguments

Value

A data frame with the estimate and confidence intervals.

Examples

data(IgM, package = "ISwR")
bst(IgM, "median")

bst(IgM, "gmean")

Description

coef_det estimates the coefficient of determination (r-squared) from fitted (predicted) and observed values. Outcome from the model is assumed to be numerical.

Usage

coef_det(obs, fit)

Arguments

Value

A scalar, the coefficient of determination (r-squared).

Examples

## Linear regression:
Riboflavin <- seq(0, 80, 10)
OD <- 0.0125 * Riboflavin + rnorm(9, 0.6, 0.03)
titration <- data.frame(Riboflavin, OD)
model1 <- lm(OD ~ Riboflavin, data = titration)
summary(model1)
coef_det(titration$OD, fitted(model1))

## Non-linear regression:
library(nlme, quietly = TRUE)
data(Puromycin)
mm.tx <- gnls(rate ~ SSmicmen(conc, Vm, K),
  data = Puromycin,
  subset = state == "treated"
)
summary(mm.tx)
coef_det(Puromycin$rate[1:12], mm.tx$fitted)

Description

estat calculates descriptives of numerical variables.

Usage

estat(object = NULL, formula = NULL, data = NULL, digits = 2, label = NULL)

Arguments

Value

A data frame with descriptive statistics.

See Also

summary.

Examples

data(kfm, package = "ISwR")
require(sjlabelled, quietly = TRUE)
kfm <- kfm |>
  var_labels(
    dl.milk = "Breast-milk intake (dl/day)",
    sex = "Sex",
    weight = "Child weight (kg)",
    ml.suppl = "Milk substitute (ml/day)",
    mat.weight = "Maternal weight (kg)",
    mat.height = "Maternal height (cm)"
  )

kfm |>
  estat(~dl.milk)

estat(~ dl.milk | sex, data = kfm)

kfm |>
  estat(~ weight | sex)

Description

expand_df expands a data frame by a vector of frequencies.

Usage

expand_df(aggregate.data, index.var = "Freq", retain.freq = FALSE)

Arguments

Details

This is a generic function that resembles weighted frequencies in other statistical packages (for example, Stata). expand.df was adapted from a function developed by deprecated package epicalc (now package epiDisplay).

Value

An expanded data frame with replicates given by the frequencies.

Examples

Freq <- c(5032, 5095, 41, 204)
Mortality <- gl(2, 2, labels = c("No", "Yes"))
Calcium <- gl(2, 1, 4, labels = c("No", "Yes"))
anyca <- data.frame(Freq, Mortality, Calcium)
anyca
anyca.exp <- expand_df(anyca)
with(anyca.exp, table(Calcium, Mortality))

Description

Randomised control trial on children suffering from frequent and severe migraine. Control group represents untreated children. The active treatments were either relaxation alone or relaxation with biofeedback.

Usage

Fentress

Format

A labelled tibble with 18 rows and 2 variables:

pain

Reduction in weekly headache activity expressed as percentage of baseline data.

group

Group, a factor with levels "Untreated", "Relaxation" (alone) and "Biofeedback" (relaxation and biofeedback).

Source

Fentress, DW, et al. (1986) Biofeedback and relaxation-response in the treatment of pediatric migraine. Dev Med Child Neurol 28:1 39-46.

Altman, DA (1991) Practical statistics for medical research. Chapman & Hall/CRC.

Examples

data(Fentress)

Fentress |>
  strip_error(pain ~ group)

Description

freq_cont tabulates a continuous variable by given classes.

Usage

freq_cont(x, bks, dg = 2)

Arguments

Value

A data frame with the classes, the mid-point, the frequencies, the relative and cumulative frequencies.

Examples

data(IgM, package = "ISwR")
Ab <- data.frame(IgM)
estat(~IgM, data = Ab)
freq_cont(IgM, seq(0, 4.5, 0.5))

Description

gen_bst_df is a function called that generates a data frame with confidence intervals of a continuous variable by levels of one or two categorical ones (factors).

Usage

gen_bst_df(object = NULL, formula = NULL, data = NULL, stat = "mean", ...)

Arguments

Value

A data frame with the confidence intervals by level.

Examples

data(kfm, package = "ISwR")
require(sjlabelled, quietly = TRUE)
kfm <- kfm |>
  var_labels(
    dl.milk = "Breast-milk intake (dl/day)",
    sex = "Sex",
    weight = "Child weight (kg)",
    ml.suppl = "Milk substitute (ml/day)",
    mat.weight = "Maternal weight (kg)",
    mat.height = "Maternal height (cm)"
  )

kfm |>
  gen_bst_df(dl.milk ~ sex)

data(birthwt, package = "MASS")
require(dplyr, quietly = TRUE)
birthwt <- mutate(birthwt,
  smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
  Race = factor(race > 1, labels = c("White", "Non-white"))
)

birthwt <- birthwt |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status"
  )

gen_bst_df(bwt ~ smoke | Race, data = birthwt)

Description

Geometric mean.

Usage

geo_mean(x)

Arguments

Value

A scalar, the calculated geometric mean.

Examples

data(IgM, package = "ISwR")
Ab <- data.frame(IgM)
estat(~IgM, data = Ab)
geo_mean(IgM)

Description

get_r2 is a is a wrap function that calls r2 from package performance. Calculates the R2 or pseudo-R2 value for different regression model objects, returning a character object for easy printing in tables of coefficients.

Usage

get_r2(model, ...)

Arguments

Details

The main purpose of get_r2 is to allow easy printing of R2 value in tables of coefficients (see examples).

See Also

r2.

Examples

require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)

data(birthwt, package = "MASS")
birthwt <- birthwt |>
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    race = factor(race, labels = c("White", "African American", "Other"))
  ) |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status",
    race = "Race"
  )

model_norm <- lm(bwt ~ smoke + race, data = birthwt)

get_r2(model_norm)

Description

gf_star Is a function used to display differences between groups (see details).

Usage

gf_star(fig, x1, y1, x2, y2, y3, legend = "*", ...)

Arguments

Details

This function draws an horizontal line from coordinate (x1, y2) to coordinate (x2, y2). Draws vertical lines below the horizontal line, towards data, from (x1, y1) to (x1, y2) and from (x2, y1) to (x2, y2). Finally, adds text above the horizontal line, at the mid point between x1 and x2. See examples.

Examples

data(kfm, package = "ISwR")
require(sjlabelled, quietly = TRUE)
kfm <- kfm |>
  var_labels(
    dl.milk = "Breast-milk intake (dl/day)",
    sex = "Sex",
    weight = "Child weight (kg)",
    ml.suppl = "Milk substitute (ml/day)",
    mat.weight = "Maternal weight (kg)",
    mat.height = "Maternal height (cm)"
  )

kfm |>
  box_plot(dl.milk ~ sex, fill = "thistle", alpha = 0.8) |>
  gf_star(x1 = 1, y1 = 10.9, x2 = 2, y2 = 11, y3 = 11.2)

kfm |>
  box_plot(dl.milk ~ sex, fill = "thistle", alpha = 0.8) |>
  gf_star(1, 10.9, 2, 11, 11.4, legend = "p = 0.035", size = 2.5)

data(energy, package = "ISwR")
energy <- energy |>
  var_labels(
    expend = "Energy expenditure (MJ/day)",
    stature = "Stature"
  )

energy |>
  strip_error(expend ~ stature, col = "red") |>
  gf_star(1, 13, 2, 13.2, 13.4, "**")

Description

glm_coef displays estimates with confidence intervals and p-values from generalised linear models (see Details).

Usage

glm_coef(
  model,
  digits = 2,
  alpha = 0.05,
  labels = NULL,
  se_rob = FALSE,
  type = "cond",
  exp_norm = FALSE
)

Arguments

Details

glm_coef recognises objects (models) from the following classes: clm, clogit, coxph, gee, glm, glmerMod, lm, lme, lmerMod, multinom, negbin, polr and surveg

For models from logistic regression (including conditional logistic, ordinal and multinomial), Poisson or survival analysis, coefficient estimates and corresponding confidence intervals are automatically exponentiated (back-transformed).

By default, glm_coef uses naive standard errors for calculating confidence intervals but has the option of using robust standard errors instead.

glm_coef can display two different data frames depending on the option of type, for type type = "cond" (the default), glm_coef displays the standard table of coefficients with confidence intervals and p-values; for type = "ext", glm_coef displays additional statistics including standard errors.

Please read the Vignette on Regression for more details.

Value

A data frame with estimates, confidence intervals and p-values from glm objects.

Examples

require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)

## Continuous outcome.
data(birthwt, package = "MASS")
birthwt <- birthwt |>
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    race = factor(race, labels = c("White", "African American", "Other"))
  ) |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status",
    race = "Race"
  )

model_norm <- lm(bwt ~ smoke + race, data = birthwt)

glm_coef(model_norm, labels = model_labels(model_norm))

## Logistic regression.
data(diet, package = "Epi")
model_binom <- glm(chd ~ fibre, data = diet, family = binomial)
model_binom |>
  glm_coef(labels = c("Constant", "Fibre intake (g/day)"))

model_binom |>
  glm_coef(labels = c("Constant", "Fibre intake (g/day)"), type = "ext")

Description

Harmonic mean.

Usage

harm_mean(x)

Arguments

Value

A scalar, the calculated harmonic mean.

Examples

data(IgM, package = "ISwR")
Ab <- data.frame(IgM)
estat(~IgM, data = Ab)
harm_mean(IgM)

Description

hist_norm constructs histograms and adds corresponding Normal density curve.

Usage

hist_norm(
  object = NULL,
  formula = NULL,
  data = NULL,
  bins = 20,
  fill = "indianred3",
  color = "black",
  alpha = 0.4,
  ...
)

Arguments

Examples

require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)
data(birthwt, package = "MASS")
birthwt <- birthwt |>
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    Race = factor(race > 1, labels = c("White", "Non-white"))
  ) |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status"
  )

birthwt |>
  hist_norm(~bwt, alpha = 0.7, bins = 20, fill = "cadetblue")

birthwt |>
  hist_norm(~ bwt | smoke, alpha = 0.7, bins = 20, fill = "cadetblue")

Description

Number of CD4+ T-cells and CD8+ T-cells in blood samples from patients in remission from Hodgkin's disease or in remission from disseminated malignancies.

Usage

Hodgkin

Format

A labelled tibble with 40 rows and 3 variables:

CD4

Concentration of CD4+ T-cells (cells / mm^3).

CD8

Concentration of CD8+ T-cells (cells / mm^3).

Group

Group, factor with levels "Non-Hodgkin" and "Hodgkin".

Source

Shapiro, CM, et al (1986) Immunologic status of patients in remission from Hodgkin's disease and disseminated malignancies. Am J Med Sci 293:366-370.

Altman, DA (1991) Practical statistics for medical research. Chapman & Hall/CRC.

Examples

data(Hodgkin)
require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)

Hodgkin <- Hodgkin |>
  mutate(
    Ratio = CD4 / CD8
  ) |>
  var_labels(
    Ratio = "CD4+ / CD8+ T-cells"
  )

estat(~ Ratio | Group, data = Hodgkin)

Hodgkin |>
  qq_plot(~ Ratio | Group)

Hodgkin$Ratio <- Hodgkin$CD4 / Hodgkin$CD8
estat(~ Ratio | Group, data = Hodgkin)

qq_plot(~ Ratio | Group, data = Hodgkin)

Description

inv_logit Calculates the inverse of the logit (probability in logistic regression)

Usage

inv_logit(x)

Arguments

Description

jack_knife Ranks the squared differences between mean values from Jackknife analysis (arithmetic mean estimated by removing one observation at a time) and the original mean value.

Usage

jack_knife(x)

Arguments

Value

Data frame with the ranked squared differences.

See Also

rank_leverage.

Examples

x <- rnorm(10, 170, 8)
x
mean(x)
jack_knife(x)

x <- rnorm(100, 170, 8)
mean(x)
head(jack_knife(x))

Description

Body weight and plasma volume in eight healthy men.

Usage

Kirkwood

Format

A labelled data frame with 8 rows and 3 variables:

subject

Subject ID.

weight

Body weight in kg.

volume

Plasma volume in litres.

Source

Kirkwood, BR and Sterne, JAC (2003) Essential Medical Statistics. Second Edition. Blackwell.

Examples

data(Kirkwood)

Kirkwood |>
  gf_point(volume ~ weight) |>
  gf_lm(col = "indianred3", interval = "confidence", fill = "indianred3")

Description

knife_mean is an internal function. Calculates arithmetic means by removing one observation at a time.

Usage

knife_mean(x)

Arguments

Value

A vector with the mean calculations.

Examples

x <- rnorm(10, 170, 8)
x
mean(x)
knife_mean(x)

Description

leverage is an internal function called by rank_leverage.

Usage

leverage(x)

Arguments

Details

Estimates the leverage of each observation around the arithmetic mean.

Value

Variable with corresponding leverage estimations

Examples

x <- rnorm(10, 170, 8)
x
mean(x)
leverage(x)
rank_leverage(x)

Description

logistic_gof performs the Hosmer and Lemeshow test to test the goodness of fit of a logistic regression model. This function is part of residuals.lrm from package rms.

Usage

logistic_gof(model)

Arguments

Author(s)

Frank Harell, Vanderbilt University <[email protected]>

References

Hosmer DW, Hosmer T, Lemeshow S, le Cessie S, Lemeshow S. A comparison of goodness-of-fit tests for the logistic regression model. Stat in Med 16:965–980, 1997.

Examples

data(diet, package = "Epi")
model <- glm(chd ~ fibre, data = diet, family = binomial)
glm_coef(model, labels = c("Constant", "Fibre intake (g/day)"))
logistic_gof(model)

Description

An international case-control study to test the hypothesis that breast cancer is related to the age that a woman gives childbirth.

Usage

Macmahon

Format

A labelled tibble with 185 rows and 2 variables:

cancer

Diagnosed with breast cancer, a factor with levels "No" and "Yes".

age

Age mother gives childbirth, factor with levels "<20", "20-24", "25-29", "30-34" and ">34".

Source

Macmahon, B. et al. (1970). Age at first birth and breast cancer risk. Bull WHO 43, 209-221.

Description

mhor computes odds ratios by levels of the stratum variable as well as the Mantel-Haenszel pooled odds ratio. The test for effect modification (test for interaction) is also displayed.

Usage

mhor(object = NULL, formula = NULL, data = NULL)

Arguments

Value

Odds ratios with 95 outcome by levels of stratum. The Mantel-Haenszel pooled OR and the test for effect modification is also reported.

Examples

data(oswego, package = "epitools")
require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)
oswego <- oswego |>
  mutate(
    ill = factor(ill, labels = c("No", "Yes")),
    sex = factor(sex, labels = c("Female", "Male")),
    chocolate.ice.cream = factor(chocolate.ice.cream, labels = c("No", "Yes"))
  ) |>
  var_labels(
    ill = "Developed illness",
    sex = "Sex",
    chocolate.ice.cream = "Consumed chocolate ice cream"
  )

oswego |>
  mhor(ill ~ sex / chocolate.ice.cream)

Description

model_labels replaces row names in glm_coef with labels from the original data frame.

Usage

model_labels(model, intercept = TRUE)

Arguments

Details

model_labels does not handle yet interaction terms, see examples.

Please read the Vignette on Regression for more examples.

Examples

require(dplyr, quietly = TRUE)
require(sjlabelled, quietly = TRUE)

data(birthwt, package = "MASS")
birthwt <- birthwt |>
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    race = factor(race, labels = c("White", "African American", "Other"))
  ) |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status",
    race = "Race"
  )

model_norm <- lm(bwt ~ smoke + race, data = birthwt)

glm_coef(model_norm, labels = model_labels(model_norm))

model_int <- lm(formula = bwt ~ smoke * race, data = birthwt)

model_int |>
  glm_coef(labels = c(
    model_labels(model_int),
    "Smoker: African American",
    "Smoker: Other"
  ))

Description

multiple displays results from post-doc analysis and constructs corresponding plot.

Usage

multiple(
  model,
  formula,
  adjust = "mvt",
  type = "response",
  reverse = TRUE,
  level = 0.95,
  digits = 2,
  ...
)

Arguments

Details

The default adjusting method is "mvt" which uses the multivariate t distribution. Other options are: "bonferroni", "holm", "hochberg", "tukey" and "none". The default option for argument reverse is to make reverse comparisons, i.e., against the reference level matching comparisons from lm and glm.

Value

A list with objects: df A data frame with adjusted p-values, fig_ci a plot with estimates and adjusted confidence intervals and fig_pval a plot comparing adjusted p-values.

See Also

emmeans, pwpp.

Examples

data(birthwt, package = "MASS")
birthwt$race <- factor(birthwt$race, labels = c("White", "African American", "Other"))

model_1 <- aov(bwt ~ race, data = birthwt)
multiple(model_1, ~race)$df

multiple(model_1, ~race)$fig_ci |>
  gf_labs(y = "Race", x = "Difference in birth weights (g)")

multiple(model_1, ~race)$fig_pval |>
  gf_labs(y = "Race")

Description

odds_trend calculates the odds ratio with confidence intervals (Wald) for different levels (three or more) of the exposure variable, constructs the corresponding plot and calculates if the trend is significant or not.

Usage

odds_trend(formula, data, angle = 45, hjust = 1, method = "wald", ...)

Arguments

Details

odds_trend is a wrap function that calls oddsratio from package epitools.

Additional methods for confidence intervals include: "midp", "fisher", and "small".

Value

A list with components df a data frame with the results and fig corresponding plot.

See Also

oddsratio.

Examples

## A cross-sectional study looked at the association between obesity and a biopsy resulting
## from mammography screening.

Freq <- c(3441, 34, 39137, 519, 20509, 280, 12149, 196, 11882, 199)
Biopsy <- gl(2, 1, 10, labels = c("No", "Yes"))
Weight <- gl(5, 2, 10, labels = c(
  "Underweight", "Normal", "Over (11-24%)",
  "Over (25-39%)", "Over (> 39%)"
))
breast <- data.frame(Freq, Biopsy, Weight)
breast

breast <- expand_df(breast)
require(sjlabelled, quietly = TRUE)

breast <- var_labels(breast,
  Weight = "Weight group"
)

odds_trend(Biopsy ~ Weight, data = breast)$df
odds_trend(Biopsy ~ Weight, data = breast)$fig

Description

Study of onchocerciasis ("river blindness") in Sierra Leone, in which subjects were classified according to whether they lived in villages in savannah or rainforest area.

Usage

Oncho

Format

A labelled tibble with 1302 rows and 7 variables:

id

Subject ID.

mf

Infected with Onchocerciasis volvulus, factor with levels "Not-infected" and "Infected".

area

Area of residence, factor with levels "Savannah" and "Rainforest".

agegrp

Age group in years, factor with levels "5-9", "10-19", "20-39" and "40+".

sex

Subject sex, factor with levels "Male" and "Female".

mfload

Microfiliariae load.

lesions

Severe eye lesions, factor with levels "No" and "Yes".

Source

McMahon, JE, Sowa, SIC, Maude, GH and Kirkwood BR (1988) Onchocerciasis in Sierra Leone 2: a comparison of forest and savannah villages. Trans Roy Soc Trop Med Hyg 82: 595-600.

Kirkwood, BR and Sterne, JAC (2003) Essential Medical Statistics. Second Edition. Blackwell.

Description

predict_inv Calculates the value the predictor x that generates value y with a simple linear model.

Usage

predict_inv(model, y)

Arguments

Value

The estimated value of the predictor.

Examples

## Spectrophotometry example. Titration curve for riboflavin (nmol/ml). The sample has an absorbance
## of 1.15. Aim is to estimate the concentration of riboflavin in the sample.

Riboflavin <- seq(0, 80, 10)
OD <- 0.0125 * Riboflavin + rnorm(9, 0.6, 0.03)
titration <- data.frame(Riboflavin, OD)

require(sjlabelled, quietly = TRUE)
titration <- titration |>
  var_labels(
    Riboflavin = "Riboflavin (nmol/ml)",
    OD = "Optical density"
  )

titration |>
  gf_point(OD ~ Riboflavin) |>
  gf_smooth(col = "indianred3", se = TRUE, lwd = 0.5, method = "loess")

## Model with intercept different from zero:
model <- lm(OD ~ Riboflavin, data = titration)
glm_coef(model)

predict_inv(model, 1.15)

Description

prop_or is a simple function to calculate a proportion, from another proportion and the odds ratio between them.

Usage

prop_or(p2, or)

Arguments

Value

p1, the proportion in the exposed group (p1).

Examples

flu <- matrix(c(20, 80, 220, 140), nrow = 2)
colnames(flu) <- c("Yes", "No")
rownames(flu) <- c("Vaccine", "Placebo")
flu

or <- (20 * 140) / (80 * 220)
p2 <- 80 / 220
prop_or(p2 = p2, or = or)
20 / 240

Description

Pseudo R2 (logistic regression) pseudo_r2 Calculates R2 analogues (pseudo R2) of logistic regression.

Usage

pseudo_r2(model)

Arguments

Details

pseudo_r2 calculates three pseudo R2 of logistic regression models: 1) Nagelkerke, @0 Cox and Snell, 3) Hosmer and Lemeshow.

Value

A data frame with the calculated pseudo R2 values.

Examples

data(Oncho)
model_oncho <- glm(mf ~ area, data = Oncho, binomial)
glm_coef(model_oncho, labels = c("Constant", "Area (rainforest/savannah)"))
pseudo_r2(model_oncho)

Description

qq_plot constructs quantile-quantile plots against the standard normal distribution (also known as quantile-normal plots).

Usage

qq_plot(
  object = NULL,
  formula = NULL,
  data = NULL,
  pch = 20,
  col = "indianred3",
  ylab = NULL,
  ...
)

Arguments

Examples

data(kfm, package = "ISwR")
require(sjlabelled, quietly = TRUE)
kfm <- kfm |>
  var_labels(
    dl.milk = "Breast-milk intake (dl/day)",
    sex = "Sex",
    weight = "Child weight (kg)",
    ml.suppl = "Milk substitute (ml/day)",
    mat.weight = "Maternal weight (kg)",
    mat.height = "Maternal height (cm)"
  )

kfm |>
  qq_plot(~dl.milk)

qq_plot(~ dl.milk | sex, data = kfm)

Description

rank_influence calculates influence measures of each data observation on models and then ranks them.

Usage

rank_influence(model)

Arguments

Details

rank_influence is a wrap function that calls influence.measures, ranks observations on their significance influence on the model and displays the 10 most influential observations (if they are significant).

See Also

influence.measures.

Examples

data(diet, package = "Epi")
model <- glm(chd ~ fibre, data = diet, family = binomial)
rank_influence(model)

Description

rank_leverage ranks observations by their leverage (influence) on the arithmetic mean.

Usage

rank_leverage(x)

Arguments

Value

A data frame ranking observations by their leverage around the mean.

See Also

jack_knife.

Examples

x <- rnorm(10, 170, 8)
x
mean(x)
rank_leverage(x)

x <- rnorm(100, 170, 8)
mean(x)
head(rank_leverage(x))

Description

reference_range estimates the reference range (reference interval) of a numerical variable.

Usage

reference_range(avg, std)

Arguments

Details

The reference range assumes normality and represents the limits that would include 95 observations.

Value

A data frame with the reference range limits.

Examples

x <- rnorm(100, 170, 8)
round(mean(x), 2)
round(sd(x), 2)

round(reference_range(mean(x), sd(x)), 2)

Description

Calculates the coefficient of variation (relative dispersion) of a variable. The relative dispersion is defined as the standard deviation over the arithmetic mean.

Usage

rel_dis(x)

Arguments

Value

The coefficient of variation (relative dispersion).

Examples

height <- rnorm(100, 170, 8)
rel_dis(height)

Description

A clinical trial on the value of extracorporeal membrane oxygenation for term neonates with severe respiratory failure. RCT compares active treatment against conventional management.

Usage

Roberts

Format

A labelled tibble with 185 rows and 2 variables:

emo

Extracorporeal membrane oxygenation treatment, factor with levels "No" and "Yes".

survived

One year survival, factor with levels "No" and "Yes".

Source

Roberts, TE (1998) Extracorporeal Membrane Oxygenation Economics Working Group. Economic evaluation and randomised controlled trial of extracorporeal membrane oxygenation: UK collaborative trial. Brit Med J 317:911-16.

Description

A case-control study of oral contraceptives and stroke in young women with presence or absence of hypertension. Cases represent thrombotic stroke and controls are hospital controls. The group of no hypertension includes normal blood pressure (<140/90 mm Hg) and borderline hypertension (140-159/90-94 mm Hg). Hypertension group includes moderate hypertension (160-179/95-109 mm Hg) and severe hypertension (180+/110+ mm Hg). This data has been used as an example of join exposure by Rothman for measuring interactions (see examples).

Usage

Rothman

Format

A labelled tibble with 477 rows and 3 variables:

stroke

Thrombotic stroke, factor with levels "No" and "Yes".

oc

Current user of oral contraceptives, factor with levels "Non-user" and "User".

ht

Hypertension, factor with levels "No" (<160/95 mm Hg) and "Yes".

Source

Collaborative Group for the Study of Stroke in Young Women (1975) Oral contraceptives and stroke in young women. JAMA 231:718-722.

Rothman, KJ (2002) Epidemiology. An Introduction. Oxford University Press.

Examples

data(Rothman)

mhor(stroke ~ ht / oc, data = Rothman)

## Model with standard interaction term:
model1 <- glm(stroke ~ ht * oc, data = Rothman, family = binomial)
glm_coef(model1)

## Model considering join exposure:
Rothman$join <- 0
Rothman$join[Rothman$oc == "Non-user" & Rothman$ht == "Yes"] <- 1
Rothman$join[Rothman$oc == "User" & Rothman$ht == "No"] <- 2
Rothman$join[Rothman$oc == "User" & Rothman$ht == "Yes"] <- 3
Rothman$join <- factor(Rothman$join, labels = c(
  "Unexposed", "Hypertension", "OC user",
  "OC and hypertension"
))

require(sjlabelled, quietly = TRUE)
Rothman$join <- set_label(Rothman$join, label = "Exposure")

model2 <- glm(stroke ~ join, data = Rothman, family = binomial)
glm_coef(model2)

Description

round_pval is an internal function called by glm_coef to round p-values from model coefficients.

Usage

round_pval(pval)

Arguments

Description

A case-control study to investigate the effects of passive smoking on cancer. Passive smoking was defined as exposure to the cigarette smoke of a spouse who smoked at least one cigarette per day for at least 6 months.

Usage

Sandler

Format

A labelled tibble with 998 rows and 3 variables:

passive

Passive smoker, factor with levels "No" and "Yes".

cancer

Diagnosed with cancer, factor with levels "No" and "Yes".

smoke

Active smoker, factor with levels "No" and "Yes".

Source

Sandler, DP, Everson, RB, Wilcox, AJ (1985). Passive smoking in adulthood and cancer risk. Amer J Epidem, 121: 37-48.

Examples

data(Sandler)

mhor(cancer ~ smoke / passive, data = Sandler)

Description

Data on measured and self-reported weight from 40–50 year old participants in the 1989/1990 Life In New Zealand Survey.

Usage

Sharples

Format

A tibble with 343 rows and 4 variables:

srweight

Self-reported weight in kg.

weight

Measured weight in kg.

srbmi

Body mass index calculated from self-reported weight and self-reported height in kg/m^2.

mbmi

Body mass index calculated from measured weight and measured height in kg/m^2.

Source

Sharples, H, et al. (2012) Agreement between measured and self-reported height, weight and BMI in predominantly European middle-aged New Zealanders: findings from a nationwide 1989 survey. New Zealand Med J 125: 60-69.

Examples

Sharples |>
  bland_altman(srweight ~ weight, transform = TRUE) |>
  gf_labs(x = "Mean of weights (kg)", y = "Measured weight / Self-reported weight")

Description

ss_jk is an internal function called by jack_knife. It calculates the squared difference of a numerical variable around a given value (for example, the mean).

Usage

ss_jk(obs, stat)

Arguments

Value

The squared difference between a variable and a given value.

Examples

x <- rnorm(10, 170, 8)
x
mean(x)
ss_jk(x, mean(x))
jack_knife(x)

Description

stats_quotes is an internal function called by estat.

Usage

stats_quotes(x, data2, digits = 2)

Arguments

Description

strip_error constructs strip plots with error bars showing 95 confidence intervals around mean values.

Usage

strip_error(
  object = NULL,
  formula = NULL,
  data = NULL,
  pch = 20,
  size = 1,
  alpha = 0.7,
  col = "indianred3",
  ...
)

Arguments

Examples

data(energy, package = "ISwR")
require(sjlabelled, quietly = TRUE)
energy <- energy |>
  var_labels(
    expend = "Energy expenditure (MJ/day)",
    stature = "Stature"
  )

energy |>
  strip_error(expend ~ stature, col = "red")

t.test(expend ~ stature, data = energy)

## Adding an horizontal line to show significant difference:
energy |>
  strip_error(expend ~ stature, col = "red") |>
  gf_star(1, 13, 2, 13.2, 13.4, "**")

data(birthwt, package = "MASS")
require(dplyr, quietly = TRUE)
birthwt <- birthwt |>
  mutate(
    smoke = factor(smoke, labels = c("Non-smoker", "Smoker")),
    Race = factor(race > 1, labels = c("White", "Non-white"))
  ) |>
  var_labels(
    bwt = "Birth weight (g)",
    smoke = "Smoking status"
  )

birthwt |>
  strip_error(bwt ~ smoke | Race, col = "darksalmon")

birthwt |>
  strip_error(bwt ~ smoke, col = ~Race)

birthwt |>
  strip_error(bwt ~ smoke, pch = ~Race, col = ~Race)

birthwt |>
  strip_error(bwt ~ smoke | Race)

Description

Randomised control trial of an antiepilectic drug (prograbide), in which the number of seizures of 59 patients at baseline and other four follow-up visits were recorded.

Usage

Thall

Format

A tibble with 59 rows and 8 variables:

id

Subject ID.

treat

Treatment, factor with levels "Control" and "Prograbide".

base

Number of seizures at baseline.

age

Age in years at baseline.

y1

Number of seizures at year one follow-up.

y2

Number of seizures at year two follow-up.

y3

Number of seizures at year three follow-up.

y4

Number of seizures at year four follow-up.

Source

Thall, PF and Vail, SC (1990) Some covariance models for longitudinal count data with over-dispersion. Biometrics, 46: 657-671.

Stukel, TA (1993) Comparison of methods for the analysis of longitudinal data. Statistics Med 12: 1339-1351.

Shoukri, MM and Chaudhary, MA (2007) Analysis of correlated data with SAS and R. Third Edition. Chapman & Hall/CRC.

Description

Data of peak knee velocity in walking at flexion and extension in studies about functional performance in cerebral palsy.

Usage

Tuzson

Format

A labelled tibble with 18 rows and 2 variables:

flexion

Peak knee velocity in gait: flexion (degree/s).

extension

Peak knee velocity in gait: extension (degree/s).

Source

Tuzson, AE, Granata, KP, and Abel, MF (2003) Spastic velocity threshold constrains functional performance in cerebral palsy. Arch Phys Med Rehabil 84: 1363-1368.

Examples

data(Tuzson)

Tuzson |>
  gf_point(flexion ~ extension)

cor.test(~ flexion + extension, data = Tuzson)

Description

Data represents women participating in a health survey in Whickham, England in 1972-1974.

Usage

Vanderpump

Format

A labelled tibble with 1314 rows and 3 variables:

vstatus

Vitality status, factor with levels "Alive" and "Death".

smoker

Smoking status, factor with levels "Non-smoker" and "Smoker".

agegrp

Age group, factor with levels "18-44", "45-64" and "64+".

Source

Vanderpump, MP, et al (1996) Thyroid, 6:155-160.

Appleton, DR, French, JM and Vanderpump, PJ (1996) Ignoring a covariate: An example of Simpson's paradox. The American Statistician 50:340-341.

Vittinghoff, E, Glidden, DV, Shiboski, SC and McCulloh, CE (2005) Regression methods in Biostatistics. Springer.

Examples

data(Vanderpump)

mhor(vstatus ~ agegrp / smoker, data = Vanderpump)