, Johannes Zimmermann
[aut] , Aidan Wright
[aut] | Title: | Analysis and Visualization of Circular Data |
|---|---|
| Description: | Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results. |
| Authors: | Jeffrey Girard [aut, cre]
, Johannes Zimmermann
[aut] , Aidan Wright
[aut] |
| Maintainer: | Jeffrey Girard <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2024-11-28 06:56:31 UTC |
| Source: | CRAN |
Display the anchors of a circumplex instrument including the total number of anchors and each anchor's numerical value and text label. Anchors are the response options that respondants select from (e.g., 0 = No, 1 = Yes).
anchors(x)anchors(x)
x |
Required. An object of the instrument class. |
The same input object. Prints text to console.
Other instrument functions:
instrument(),
instruments(),
items(),
norms(),
scales()
instrument("csip") anchors(csip)instrument("csip") anchors(csip)
A small example dataset containing standardized scores on eight hypothetical circumplex scales. Taken from Wright, Pincus, Conroy, & Hilsenroth (2009).
aw2009aw2009
A data frame with 5 observations and 8 variables:
circumplex scale at 90 degrees
circumplex scale at 135 degrees
circumplex scale at 180 degrees
circumplex scale at 225 degrees
circumplex scale at 270 degrees
circumplex scale at 315 degrees
circumplex scale at 360 degrees
circumplex scale at 45 degrees
Format a data frame as an HTML table and render it to the web viewer.
html_render(df, caption = NULL, align = "l", ...)html_render(df, caption = NULL, align = "l", ...)
df |
A data frame to be rendered as an HTML table. |
caption |
A string to be displayed above the table. |
align |
A string indicating the alignment of the cells (default = "l"). |
... |
Other arguments to pass to |
HTML syntax for the df table.
Other table functions:
ssm_table()
The circumplex package includes information about numerous circumplex
instruments including instructions for scoring and standardizing items to be
used in conjunction with the score and standardize functions.
This function loads the information for a specific instrument into memory.
See the instruments function to list all available instruments.
instrument(code)instrument(code)
code |
Required. A string (e.g., "iip32") or text in non-standard
evaluation (e.g., iip32). The code of the instrument assigned by this
package and displayed in parentheses by |
The instrument object for the requested circumplex instrument. If the function is called without a name assignment (LHS), then the object will be created in the global environment with the default name as above. Or, if a name is assigned (LHS), the object will have that name instead.
Other instrument functions:
anchors(),
instruments(),
items(),
norms(),
scales()
instrument("iip32") x <- instrument("iip32")instrument("iip32") x <- instrument("iip32")
The circumplex package includes information about numerous circumplex
instruments including instructions for scoring and standardizing items.
Individual instruments can be loaded using the instrument function.
instruments()instruments()
Other instrument functions:
anchors(),
instrument(),
items(),
norms(),
scales()
instruments()instruments()
Rescore each circumplex item using deviation scoring across variables. In other words, subtract each observation's mean response from each response. This effectively removes the presence of a general factor, which can make certain circumplex fit analyses more powerful.
ipsatize(data, items, na.rm = TRUE, prefix = "", suffix = "_i", append = TRUE)ipsatize(data, items, na.rm = TRUE, prefix = "", suffix = "_i", append = TRUE)
data |
Required. A data frame or matrix containing at least circumplex scales. |
items |
Required. A character vector containing the column names, or a
numeric vector containing column indexes, of item variables in |
na.rm |
Optional. A logical that determines whether missing values should be ignored during the calculation of the mean during ipsatization (default = TRUE). |
prefix |
Optional. A string that will be added to the start of each
|
suffix |
Optional. A string that will be added to the end of each
|
append |
Optional. A logical that determines whether to append the
ipsatized scores to |
A data frame that matches data except that the variables specified
in items have been rescored using ipsatization.
Other tidying functions:
norm_standardize(),
score()
data("raw_iipsc") ipsatize(raw_iipsc, items = 1:32) ipsatize(raw_iipsc, items = sprintf("IIP%02d", 1:32))data("raw_iipsc") ipsatize(raw_iipsc, items = 1:32) ipsatize(raw_iipsc, items = sprintf("IIP%02d", 1:32))
Display the items of a circumplex instrument including the total number of
items and each item's number and text. The item ordering/numbering displayed
here is the same ordering/numbering assumed by the score() function.
items(x)items(x)
x |
Required. An object of the instrument class. |
The same input object. Prints text to console.
Other instrument functions:
anchors(),
instrument(),
instruments(),
norms(),
scales()
instrument("csip") items(csip)instrument("csip") items(csip)
A large example dataset containing gender, raw mean scores on the Inventory of Interpersonal Problems - Short Circumplex (IIP-SC), and raw sum scores on the Personality Diagnostic Questionnaire - 4th Edition Plus (PDQ-4+).
jz2017jz2017
A data frame with 1166 observations and 19 variables:
Self-reported Gender
Domineering Problems (IIP-SC) 90 degrees
Vindictive Problems (IIP-SC) 135 degrees
Cold Problems (IIP-SC) 180 degrees
Socially Avoidant Problems (IIP-SC) 225 degrees
Nonassertive Problems(IIP-SC) 270 degrees
Easily Exploited Problems (IIP-SC) 315 degrees
Overly Nurturant Problems (IIP-SC) 360 degrees
Intrusive Problems (IIP-SC) 45 degrees
Paranoid PD Symptoms (PDQ-4+)
Schizoid PD Symptoms (PDQ-4+)
Schizotypal PD Symptoms (PDQ-4+)
Antisocial PD Symptoms (PDQ-4+)
Borderline PD Symptoms (PDQ-4+)
Histrionic PD Symptoms (PDQ-4+)
Narcissistic PD Symptoms (PDQ-4+)
Avoidant PD Symptoms (PDQ-4+)
Dependent PD Symptoms (PDQ-4+)
Obsessive-Compulsive PD Symptoms (PDQ-4+)
Take in a data frame containing circumplex scales, angle definitions for each scale, and normative data (from the package or custom) and return that same data frame with each specified circumplex scale transformed into standard scores (i.e., z-scores) based on comparison to the normative data.
norm_standardize( data, scales, angles = octants(), instrument, sample = 1, prefix = "", suffix = "_z", append = TRUE )norm_standardize( data, scales, angles = octants(), instrument, sample = 1, prefix = "", suffix = "_z", append = TRUE )
data |
Required. A data frame or matrix containing at least circumplex scales. |
scales |
Required. A character vector containing the column names, or a numeric vector containing the column indexes, for the variables (scale scores) to be standardized. |
angles |
Required. A numeric vector containing the angular displacement
of each circumplex scale included in |
instrument |
Required. An instrument object from the package. To see the
available circumplex instruments, see |
sample |
Required. An integer corresponding to the normative sample to
use in standardizing the scale scores (default = 1). See |
prefix |
Optional. A string to include at the beginning of the newly
calculated scale variables' names, before the scale name and |
suffix |
Optional. A string to include at the end of the newly
calculated scale variables' names, after the scale name and |
append |
Optional. A logical that determines whether the calculated
standardized scores should be added as columns to |
A data frame that contains the norm-standardized versions of scales.
Other tidying functions:
ipsatize(),
score()
data("jz2017") instrument("iipsc") norm_standardize(jz2017, scales = 2:9, instrument = iipsc, sample = 1)data("jz2017") instrument("iipsc") norm_standardize(jz2017, scales = 2:9, instrument = iipsc, sample = 1)
Display the norms for a circumplex instrument including the total number of normative data sets available and each data set's number, sample size, population, and source reference and hyperlink. If another normative data set exists that is not yet included in the package, please let us know.
norms(x)norms(x)
x |
Required. An object of the instrument class. |
The same input object. Prints text to console.
Other instrument functions:
anchors(),
instrument(),
instruments(),
items(),
scales()
instrument("csip") norms(csip)instrument("csip") norms(csip)
Return a vector of angular displacements, in degrees, for eight equally
spaced circumplex scales corresponding to the circumplex octants. Can be
passed to the angles parameter of other functions in this package.
octants()octants()
A numeric vector with eight elements, each corresponding to the angular displacement (in degrees) of a subscale, in the following order: PA, BC, DE, FG, HI, JK, LM, NO.
octants()octants()
Return a vector of abbreviations for octant circumplex scales, from PA to NO.
PANO()PANO()
A character vector with eight elements, each corresponding to the abbreviation of an octant subscale: PA, BC, DE, FG, HI, JK, LM, NO.
PANO()PANO()
Return a vector of angular displacements, in degrees, for four equally spaced
circumplex scales corresponding to the circumplex poles. Can be passed to the
angles parameter of other functions in this package.
poles()poles()
A numeric vector with four elements, each corresponding to the angular displacement (in degrees) of a subscale, in the following order: PA, DE, HI, LM.
poles()poles()
Return a vector of angular displacements, in degrees, for four equally spaced
circumplex scales corresponding to the circumplex quadrants. Can be passed to
the angles parameter of other functions in this package.
quadrants()quadrants()
A numeric vector with eight elements, each corresponding to the angular displacement (in degrees) of a subscale, in the following order: BC, FG, JK, NO.
quadrants()quadrants()
A small example dataset containing raw item responses on the Inventory of Interpersonal Problems, Short Circumplex (IIP-SC). This data set is useful for testing functions that operate on item-level data.
raw_iipscraw_iipsc
A data frame with 10 observations and 32 variables.
Display the scales of a circumplex instrument including the total number of scales and each scale's abbreviation, hypothetical angle, and text label.
scales(x, items = FALSE)scales(x, items = FALSE)
x |
Required. An object of the instrument class. |
items |
Optional. A logical determining whether the items for each scale should be displayed below its other information (default = FALSE). |
The same input object. Prints text to console.
Other instrument functions:
anchors(),
instrument(),
instruments(),
items(),
norms()
instrument("csip") scales(csip) scales(csip, items = TRUE)instrument("csip") scales(csip) scales(csip, items = TRUE)
Calculate mean scores on circumplex scales from item responses by using a set of scoring instructions, which may be loaded from the package or created as a custom data frame.
score( data, items, instrument, na.rm = TRUE, prefix = "", suffix = "", append = TRUE )score( data, items, instrument, na.rm = TRUE, prefix = "", suffix = "", append = TRUE )
data |
Required. A data frame containing at least circumplex scales. |
items |
Required. The variable names or column numbers for the variables
in |
instrument |
Required. An instrument object from the package. To see the
available circumplex instruments, use |
na.rm |
Optional. A logical that determines if missing values should be omitted from the calculation of scores (default = TRUE). When set to TRUE, scales with missing data are essentially calculated with mean imputation. |
prefix |
Optional. A string to include at the beginning of the newly
calculated scale variables' names, before |
suffix |
Optional. A string to include at the end of the newly
calculated scale variables' names, after |
append |
Optional. A logical that determines whether the calculated
score variables will be appended to |
A data frame that matches .data except that new variables are
appended that contain mean scores on each variable included in key.
Other tidying functions:
ipsatize(),
norm_standardize()
data("raw_iipsc") instrument("iipsc") score(raw_iipsc, items = 1:32, instrument = iipsc, prefix = "IIPSC_")data("raw_iipsc") instrument("iipsc") score(raw_iipsc, items = 1:32, instrument = iipsc, prefix = "IIPSC_")
Calculate SSM parameters with bootstrapped confidence intervals for a variety of different analysis types. Depending on what arguments are supplied, either mean-based or correlation-based analyses will be performed, one or more groups will be used to stratify the data, and contrasts between groups or measures will be calculated.
ssm_analyze( data, scales, angles = octants(), measures = NULL, grouping = NULL, contrast = FALSE, boots = 2000, interval = 0.95, listwise = TRUE, measures_labels = NULL )ssm_analyze( data, scales, angles = octants(), measures = NULL, grouping = NULL, contrast = FALSE, boots = 2000, interval = 0.95, listwise = TRUE, measures_labels = NULL )
data |
Required. A data frame containing at least circumplex scales. |
scales |
Required. A character vector of column names, or a numeric
vector of column indexes, from |
angles |
Optional. A numeric vector containing the angular displacement
of each circumplex scale included in |
measures |
Optional. Either |
grouping |
Optional. Either |
contrast |
Optional. A logical indicating whether to output the difference between two measures' or two groups' SSM parameters. Can only be set to TRUE when there are exactly two measures and one group, one measure and two groups, or no measures and two groups (default = FALSE). |
boots |
Optional. A single positive whole number indicating how many bootstrap resamples to use when estimating the confidence intervals (default = 2000). |
interval |
Optional. A single positive number between 0 and 1 (exclusive) that indicates what confidence level to use when estimating the confidence intervals (default = 0.95). |
listwise |
Optional. A logical indicating whether missing values should be handled by listwise deletion (TRUE) or pairwise deletion (FALSE). Note that pairwise deletion may result in different missing data patterns in each bootstrap resample and is slower to compute (default = TRUE). |
measures_labels |
Optional. Either |
A list containing the results and description of the analysis.
results |
A data frame with the SSM parameter estimates |
details |
A list with the number of bootstrap resamples (boots), the confidence interval percentage level (interval), and the angular displacement of scales (angles) |
call |
A language object containing the function call that created this object |
scores |
A data frame containing the mean scale scores |
type |
A string indicating what type of SSM analysis was done |
Other ssm functions:
ssm_parameters(),
ssm_score(),
ssm_table()
Other analysis functions:
ssm_parameters(),
ssm_score()
# Load example data data("jz2017") # Single-group mean-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO") ) # Single-group correlation-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = c("NARPD", "ASPD") ) # Multiple-group mean-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), grouping = "Gender" ) # Multiple-group mean-based SSM with contrast ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), grouping = "Gender", contrast = TRUE ) # Single-group correlation-based SSM with contrast ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = c("NARPD", "ASPD"), contrast = TRUE ) # Multiple-group correlation-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = "NARPD", grouping = "Gender" ) # Multiple-group correlation-based SSM with contrast ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = "NARPD", grouping = "Gender", contrast = TRUE )# Load example data data("jz2017") # Single-group mean-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO") ) # Single-group correlation-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = c("NARPD", "ASPD") ) # Multiple-group mean-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), grouping = "Gender" ) # Multiple-group mean-based SSM with contrast ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), grouping = "Gender", contrast = TRUE ) # Single-group correlation-based SSM with contrast ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = c("NARPD", "ASPD"), contrast = TRUE ) # Multiple-group correlation-based SSM ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = "NARPD", grouping = "Gender" ) # Multiple-group correlation-based SSM with contrast ssm_analyze( jz2017, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO"), measures = "NARPD", grouping = "Gender", contrast = TRUE )
Calculate SSM parameters (without confidence intervals) for a set of scores
and generate a data frame with customizable labels for each parameter value.
This function requires the input to be a numeric vector (or coercable to one)
and returns only the parameters. See ssm_score() for a similar
function that calculates SSM parameters for each row of a data frame.
ssm_parameters( scores, angles = octants(), prefix = "", suffix = "", e_label = "Elev", x_label = "Xval", y_label = "Yval", a_label = "Ampl", d_label = "Disp", f_label = "Fit" )ssm_parameters( scores, angles = octants(), prefix = "", suffix = "", e_label = "Elev", x_label = "Xval", y_label = "Yval", a_label = "Ampl", d_label = "Disp", f_label = "Fit" )
scores |
Required. A numeric vector (or single row data frame) containing one score for each of a set of circumplex scales. |
angles |
Required. A numeric vector containing the angular displacement
of each circumplex scale included in |
prefix |
Optional. A string to append to the beginning of all of the SSM parameters' variable names (default = ""). |
suffix |
Optional. A string to append to the end of all of the SSM parameters' variable names (default = ""). |
e_label |
Optional. A string representing the variable name of the SSM elevation parameter (default = "Elev"). |
x_label |
Optional. A string representing the variable name of the SSM x-value parameter (default = "Xval"). |
y_label |
Optional. A string representing the variable name of the SSM y-value parameter (default = "Yval"). |
a_label |
Optional. A string representing the variable name of the SSM amplitude parameter (default = "Ampl"). |
d_label |
Optional. A string representing the variable name of the SSM displacement parameter (default = "Disp"). |
f_label |
Optional. A string representing the variable name of the SSM fit or R-squared value (default = "Fit"). |
A data frame containing the SSM parameters calculated from scores.
Other ssm functions:
ssm_analyze(),
ssm_score(),
ssm_table()
Other analysis functions:
ssm_analyze(),
ssm_score()
# Manually enter octant scores scores <- c(0.55, 0.58, 0.62, 0.76, 1.21, 1.21, 1.48, 0.90) ssm_parameters(scores) # Customize several of the labels ssm_parameters(scores, x_label = "LOV", y_label = "DOM") # Add a prefix to all labels ssm_parameters(scores, prefix = "IIP_")# Manually enter octant scores scores <- c(0.55, 0.58, 0.62, 0.76, 1.21, 1.21, 1.48, 0.90) ssm_parameters(scores) # Customize several of the labels ssm_parameters(scores, x_label = "LOV", y_label = "DOM") # Add a prefix to all labels ssm_parameters(scores, prefix = "IIP_")
Take in the results of a Structural Summary Method analysis and plot the point and interval estimate for each row (e.g., group or measure) in a circular space quantified by displacement and amplitude.
ssm_plot_circle( ssm_object, amax = NULL, legend_font_size = 12, scale_font_size = 12, drop_lowfit = FALSE, repel = FALSE, angle_labels = NULL, legend.box.spacing = 0, palette = "Set2", ... )ssm_plot_circle( ssm_object, amax = NULL, legend_font_size = 12, scale_font_size = 12, drop_lowfit = FALSE, repel = FALSE, angle_labels = NULL, legend.box.spacing = 0, palette = "Set2", ... )
ssm_object |
Required. The output of |
amax |
A positive real number corresponding to the radius of the circle. It is used to scale the amplitude values and will determine which amplitude labels are drawn. |
legend_font_size |
A positive real number corresponding to the size (in pt) of the text labels in the legend (default = 12). |
scale_font_size |
A positive real number corresponding to the size (in pt) of the text labels for the amplitude and displacement scales (default = 12). |
drop_lowfit |
A logical determining whether profiles with low model fit (<.70) should be omitted or plotted with dashed borders (default = FALSE). |
repel |
An experimental argument for plotting text labels instead of colors. |
angle_labels |
A character vector specifying text labels to plot around
the circle for each scale. Can also specify NULL to default to numerical
angle labels or a vector of empty strings ("") to hide the labels. If not
NULL, must have the same length and ordering as the |
legend.box.spacing |
A double corresponding to the distance (in inches) to add between the data plot and the legend (default = 0). |
palette |
A string corresponding to the palette to be used from ColorBrewer for the color and fill aesthetics. If set to NULL, all points will appear blue and no legend will be there (useful for showing the coverage of a high number of variables). |
... |
Currently ignored. |
A ggplot variable containing a completed circular plot.
data("jz2017") res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD") ) ssm_plot_circle(res)data("jz2017") res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD") ) ssm_plot_circle(res)
Take in the results of a Structural Summary Method analysis with pairwise contrasts and plot the point and interval estimates for each parameter's contrast (e.g., between groups or measures).
ssm_plot_contrast( ssm_object, drop_xy = FALSE, sig_color = "#fc8d62", ns_color = "white", linesize = 1.25, fontsize = 12, ... )ssm_plot_contrast( ssm_object, drop_xy = FALSE, sig_color = "#fc8d62", ns_color = "white", linesize = 1.25, fontsize = 12, ... )
ssm_object |
Required. The results output of |
drop_xy |
A logical determining whether the X-Value and Y-Value parameters should be removed from the plot (default = FALSE). |
sig_color |
Optional. A string corresponding to the color to use to denote significant contrasts (default = "#fc8d62"). |
ns_color |
Optional. A string corresponding to the color to use to denote non-significant contrasts (default = "white"). |
linesize |
Optional. A positive number corresponding to the size of the point range elements in mm (default = 1.5). |
fontsize |
Optional. A positive number corresponding to the size of the axis labels, numbers, and facet headings in pt (default = 12). |
... |
Additional arguments will be ignored. |
A ggplot variable containing difference point-ranges faceted by SSM parameter. An interval that does not contain the value of zero has p<.05.
data("jz2017") res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD"), contrast = TRUE ) ssm_plot_contrast(res)data("jz2017") res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD"), contrast = TRUE ) ssm_plot_contrast(res)
Take in the results of a Structural Summary Method analysis and plot the scores by angle and the estimated SSM curve.
ssm_plot_curve( ssm_object, angle_labels = NULL, base_size = 11, drop_lowfit = FALSE, ... )ssm_plot_curve( ssm_object, angle_labels = NULL, base_size = 11, drop_lowfit = FALSE, ... )
ssm_object |
Required. The results output of |
angle_labels |
Optional. Either NULL or a character vector that
determines the x-axis labels. If NULL, the labels will be the angle
numbers. If a character vector, must be the same length and in the same
order as the |
base_size |
Optional. A positive number corresponding to the base font size in pts (default = 11). |
drop_lowfit |
Optional. A logical indicating whether to omit profiles with low fit (<.70) or include them with dashed lines (default = FALSE). |
... |
Additional arguments will be ignored. |
A ggplot object depicting the SSM curve(s) of each profile.
data("jz2017") res <- ssm_analyze( jz2017, scales = 2:9, measures = 10:13 ) ssm_plot_curve(res) ssm_plot_curve(res, angle_lables = PANO())data("jz2017") res <- ssm_analyze( jz2017, scales = 2:9, measures = 10:13 ) ssm_plot_curve(res) ssm_plot_curve(res, angle_lables = PANO())
Calculate the SSM parameters for each row of a data frame and add the results as additional columns. This can be useful when the SSM is being used for the description or visualization of individual data points rather than for statistical inference on groups of data points.
ssm_score(data, scales, angles = octants(), append = TRUE, ...)ssm_score(data, scales, angles = octants(), append = TRUE, ...)
data |
Required. A data frame containing at least circumplex scales. |
scales |
Required. The variable names or column numbers for the
variables in |
angles |
Required. A numeric vector containing the angular displacement
of each circumplex scale included in |
append |
Optional. A logical indicating whether to append the output to
|
... |
Optional. Additional parameters to pass to
|
A data frame containing .data plus six additional columns
containing the SSM parameters (calculated rowwise).
Other ssm functions:
ssm_analyze(),
ssm_parameters(),
ssm_table()
Other analysis functions:
ssm_analyze(),
ssm_parameters()
data("aw2009") ssm_score( aw2009, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO") )data("aw2009") ssm_score( aw2009, scales = c("PA", "BC", "DE", "FG", "HI", "JK", "LM", "NO") )
Take in the results of an SSM analysis and return an HTML table with the desired formatting.
ssm_table(ssm_object, caption = NULL, drop_xy = FALSE, render = TRUE)ssm_table(ssm_object, caption = NULL, drop_xy = FALSE, render = TRUE)
ssm_object |
Required. The results output of |
caption |
A string to be displayed above the table (default = NULL). |
drop_xy |
A logical indicating whether the x-value and y-value parameters should be omitted from the output (default = FALSE). |
render |
A logical indicating whether the table should be displayed in the RStudio viewer or web browser (default = TRUE). |
A data frame containing the information for the HTML table. As a side-effect, may also output the HTML table to the web viewer.
Other ssm functions:
ssm_analyze(),
ssm_parameters(),
ssm_score()
Other table functions:
html_render()
# Load example data data("jz2017") # Create table of profile results res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD") ) ssm_table(res) # Create table of contrast results res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD"), contrast = TRUE ) ssm_table(res)# Load example data data("jz2017") # Create table of profile results res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD") ) ssm_table(res) # Create table of contrast results res <- ssm_analyze( jz2017, scales = 2:9, measures = c("NARPD", "ASPD"), contrast = TRUE ) ssm_table(res)