Nothing
R/conttables.h.R
In jmv: The 'jamovi' Analyses
Defines functions
contTables
Documented in
contTables
# This file is automatically generated, you probably don't want to edit this
contTablesOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"contTablesOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
rows = NULL,
cols = NULL,
counts = NULL,
layers = NULL,
chiSq = TRUE,
chiSqCorr = FALSE,
zProp = FALSE,
likeRat = FALSE,
fisher = FALSE,
contCoef = FALSE,
phiCra = FALSE,
diffProp = FALSE,
logOdds = FALSE,
odds = FALSE,
relRisk = FALSE,
ci = TRUE,
ciWidth = 95,
compare = "rows",
hypothesis = "different",
gamma = FALSE,
taub = FALSE,
mh = FALSE,
obs = TRUE,
exp = FALSE,
pcRow = FALSE,
pcCol = FALSE,
pcTot = FALSE,
barplot = FALSE,
yaxis = "ycounts",
yaxisPc = "total_pc",
xaxis = "xrows",
bartype = "dodge", ...) {
super$initialize(
package="jmv",
name="contTables",
requiresData=TRUE,
...)
private$..rows <- jmvcore::OptionVariable$new(
"rows",
rows,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..cols <- jmvcore::OptionVariable$new(
"cols",
cols,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..counts <- jmvcore::OptionVariable$new(
"counts",
counts,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..layers <- jmvcore::OptionVariables$new(
"layers",
layers,
default=NULL,
permitted=list(
"factor"))
private$..chiSq <- jmvcore::OptionBool$new(
"chiSq",
chiSq,
default=TRUE)
private$..chiSqCorr <- jmvcore::OptionBool$new(
"chiSqCorr",
chiSqCorr,
default=FALSE)
private$..zProp <- jmvcore::OptionBool$new(
"zProp",
zProp,
default=FALSE)
private$..likeRat <- jmvcore::OptionBool$new(
"likeRat",
likeRat,
default=FALSE)
private$..fisher <- jmvcore::OptionBool$new(
"fisher",
fisher,
default=FALSE)
private$..contCoef <- jmvcore::OptionBool$new(
"contCoef",
contCoef,
default=FALSE)
private$..phiCra <- jmvcore::OptionBool$new(
"phiCra",
phiCra,
default=FALSE)
private$..diffProp <- jmvcore::OptionBool$new(
"diffProp",
diffProp,
default=FALSE)
private$..logOdds <- jmvcore::OptionBool$new(
"logOdds",
logOdds,
default=FALSE)
private$..odds <- jmvcore::OptionBool$new(
"odds",
odds,
default=FALSE)
private$..relRisk <- jmvcore::OptionBool$new(
"relRisk",
relRisk,
default=FALSE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=TRUE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..compare <- jmvcore::OptionList$new(
"compare",
compare,
options=list(
"rows",
"columns"),
default="rows")
private$..hypothesis <- jmvcore::OptionList$new(
"hypothesis",
hypothesis,
options=list(
"different",
"oneGreater",
"twoGreater"),
default="different")
private$..gamma <- jmvcore::OptionBool$new(
"gamma",
gamma,
default=FALSE)
private$..taub <- jmvcore::OptionBool$new(
"taub",
taub,
default=FALSE)
private$..mh <- jmvcore::OptionBool$new(
"mh",
mh,
default=FALSE)
private$..obs <- jmvcore::OptionBool$new(
"obs",
obs,
default=TRUE)
private$..exp <- jmvcore::OptionBool$new(
"exp",
exp,
default=FALSE)
private$..pcRow <- jmvcore::OptionBool$new(
"pcRow",
pcRow,
default=FALSE)
private$..pcCol <- jmvcore::OptionBool$new(
"pcCol",
pcCol,
default=FALSE)
private$..pcTot <- jmvcore::OptionBool$new(
"pcTot",
pcTot,
default=FALSE)
private$..barplot <- jmvcore::OptionBool$new(
"barplot",
barplot,
default=FALSE)
private$..yaxis <- jmvcore::OptionList$new(
"yaxis",
yaxis,
options=list(
"ycounts",
"ypc"),
default="ycounts")
private$..yaxisPc <- jmvcore::OptionList$new(
"yaxisPc",
yaxisPc,
options=list(
"total_pc",
"column_pc",
"row_pc"),
default="total_pc")
private$..xaxis <- jmvcore::OptionList$new(
"xaxis",
xaxis,
options=list(
"xrows",
"xcols"),
default="xrows")
private$..bartype <- jmvcore::OptionList$new(
"bartype",
bartype,
options=list(
"dodge",
"stack"),
default="dodge")
self$.addOption(private$..rows)
self$.addOption(private$..cols)
self$.addOption(private$..counts)
self$.addOption(private$..layers)
self$.addOption(private$..chiSq)
self$.addOption(private$..chiSqCorr)
self$.addOption(private$..zProp)
self$.addOption(private$..likeRat)
self$.addOption(private$..fisher)
self$.addOption(private$..contCoef)
self$.addOption(private$..phiCra)
self$.addOption(private$..diffProp)
self$.addOption(private$..logOdds)
self$.addOption(private$..odds)
self$.addOption(private$..relRisk)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..compare)
self$.addOption(private$..hypothesis)
self$.addOption(private$..gamma)
self$.addOption(private$..taub)
self$.addOption(private$..mh)
self$.addOption(private$..obs)
self$.addOption(private$..exp)
self$.addOption(private$..pcRow)
self$.addOption(private$..pcCol)
self$.addOption(private$..pcTot)
self$.addOption(private$..barplot)
self$.addOption(private$..yaxis)
self$.addOption(private$..yaxisPc)
self$.addOption(private$..xaxis)
self$.addOption(private$..bartype)
}),
active = list(
rows = function() private$..rows$value,
cols = function() private$..cols$value,
counts = function() private$..counts$value,
layers = function() private$..layers$value,
chiSq = function() private$..chiSq$value,
chiSqCorr = function() private$..chiSqCorr$value,
zProp = function() private$..zProp$value,
likeRat = function() private$..likeRat$value,
fisher = function() private$..fisher$value,
contCoef = function() private$..contCoef$value,
phiCra = function() private$..phiCra$value,
diffProp = function() private$..diffProp$value,
logOdds = function() private$..logOdds$value,
odds = function() private$..odds$value,
relRisk = function() private$..relRisk$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
compare = function() private$..compare$value,
hypothesis = function() private$..hypothesis$value,
gamma = function() private$..gamma$value,
taub = function() private$..taub$value,
mh = function() private$..mh$value,
obs = function() private$..obs$value,
exp = function() private$..exp$value,
pcRow = function() private$..pcRow$value,
pcCol = function() private$..pcCol$value,
pcTot = function() private$..pcTot$value,
barplot = function() private$..barplot$value,
yaxis = function() private$..yaxis$value,
yaxisPc = function() private$..yaxisPc$value,
xaxis = function() private$..xaxis$value,
bartype = function() private$..bartype$value),
private = list(
..rows = NA,
..cols = NA,
..counts = NA,
..layers = NA,
..chiSq = NA,
..chiSqCorr = NA,
..zProp = NA,
..likeRat = NA,
..fisher = NA,
..contCoef = NA,
..phiCra = NA,
..diffProp = NA,
..logOdds = NA,
..odds = NA,
..relRisk = NA,
..ci = NA,
..ciWidth = NA,
..compare = NA,
..hypothesis = NA,
..gamma = NA,
..taub = NA,
..mh = NA,
..obs = NA,
..exp = NA,
..pcRow = NA,
..pcCol = NA,
..pcTot = NA,
..barplot = NA,
..yaxis = NA,
..yaxisPc = NA,
..xaxis = NA,
..bartype = NA)
)
contTablesResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"contTablesResults",
inherit = jmvcore::Group,
active = list(
freqs = function() private$.items[["freqs"]],
chiSq = function() private$.items[["chiSq"]],
odds = function() private$.items[["odds"]],
nom = function() private$.items[["nom"]],
gamma = function() private$.items[["gamma"]],
taub = function() private$.items[["taub"]],
mh = function() private$.items[["mh"]],
barplot = function() private$.items[["barplot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Contingency Tables")
self$add(jmvcore::Table$new(
options=options,
name="freqs",
title="Contingency Tables",
columns=list(),
clearWith=list(
"rows",
"cols",
"counts",
"layers")))
self$add(jmvcore::Table$new(
options=options,
name="chiSq",
title="\u03C7\u00B2 Tests",
clearWith=list(
"rows",
"cols",
"counts",
"layers",
"hypothesis",
"compare"),
columns=list(
list(
`name`="test[chiSq]",
`title`="",
`type`="text",
`content`="\u03C7\u00B2",
`visible`="(chiSq)"),
list(
`name`="value[chiSq]",
`title`="Value",
`visible`="(chiSq)"),
list(
`name`="df[chiSq]",
`title`="df",
`type`="integer",
`visible`="(chiSq)"),
list(
`name`="p[chiSq]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(chiSq)"),
list(
`name`="test[chiSqCorr]",
`title`="",
`type`="text",
`content`="\u03C7\u00B2 continuity correction",
`visible`="(chiSqCorr)"),
list(
`name`="value[chiSqCorr]",
`title`="Value",
`visible`="(chiSqCorr)"),
list(
`name`="df[chiSqCorr]",
`title`="df",
`type`="integer",
`visible`="(chiSqCorr)"),
list(
`name`="p[chiSqCorr]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(chiSqCorr)"),
list(
`name`="test[zProp]",
`title`="",
`type`="text",
`content`="z test difference in 2 proportions",
`visible`="(zProp)"),
list(
`name`="value[zProp]",
`title`="Value",
`visible`="(zProp)"),
list(
`name`="df[zProp]",
`title`="df",
`visible`="(zProp)"),
list(
`name`="p[zProp]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(zProp)"),
list(
`name`="test[likeRat]",
`title`="",
`type`="text",
`content`="Likelihood ratio",
`visible`="(likeRat)",
`refs`="vcd"),
list(
`name`="value[likeRat]",
`title`="Value",
`visible`="(likeRat)"),
list(
`name`="df[likeRat]",
`title`="df",
`type`="integer",
`visible`="(likeRat)"),
list(
`name`="p[likeRat]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(likeRat)"),
list(
`name`="test[fisher]",
`title`="",
`type`="text",
`content`="Fisher's exact test",
`visible`="(fisher)"),
list(
`name`="value[fisher]",
`title`="Value",
`visible`="(fisher)"),
list(
`name`="p[fisher]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(fisher)"),
list(
`name`="test[N]",
`title`="",
`type`="text",
`content`="N"),
list(
`name`="value[N]",
`title`="Value",
`type`="integer"))))
self$add(jmvcore::Table$new(
options=options,
name="odds",
title="Comparative Measures",
visible="(diffProp || logOdds || odds || relRisk)",
clearWith=list(
"rows",
"cols",
"counts",
"layers",
"ciWidth",
"compare"),
columns=list(
list(
`name`="t[dp]",
`title`="",
`type`="text",
`content`="Difference in 2 proportions",
`visible`="(diffProp)"),
list(
`name`="v[dp]",
`title`="Value",
`visible`="(diffProp)"),
list(
`name`="cil[dp]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(diffProp && ci)"),
list(
`name`="ciu[dp]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(diffProp && ci)"),
list(
`name`="t[lo]",
`title`="",
`type`="text",
`content`="Log odds ratio",
`visible`="(logOdds)",
`refs`="vcd"),
list(
`name`="v[lo]",
`title`="Value",
`visible`="(logOdds)"),
list(
`name`="cil[lo]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(logOdds && ci)"),
list(
`name`="ciu[lo]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(logOdds && ci)"),
list(
`name`="t[o]",
`title`="",
`type`="text",
`content`="Odds ratio",
`visible`="(odds)"),
list(
`name`="v[o]",
`title`="Value",
`visible`="(odds)"),
list(
`name`="cil[o]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(odds && ci)"),
list(
`name`="ciu[o]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(odds && ci)"),
list(
`name`="t[rr]",
`title`="",
`type`="text",
`content`="Relative risk",
`visible`="(relRisk)"),
list(
`name`="v[rr]",
`title`="Value",
`visible`="(relRisk)"),
list(
`name`="cil[rr]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(relRisk && ci)"),
list(
`name`="ciu[rr]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(relRisk && ci)"))))
self$add(jmvcore::Table$new(
options=options,
name="nom",
title="Nominal",
visible="(contCoef || phiCra)",
columns=list(
list(
`name`="t[cont]",
`title`="",
`type`="text",
`content`="Contingency coefficient",
`visible`="(contCoef)"),
list(
`name`="v[cont]",
`title`="Value",
`visible`="(contCoef)"),
list(
`name`="t[phi]",
`title`="",
`type`="text",
`content`="Phi-coefficient",
`visible`="(phiCra)"),
list(
`name`="v[phi]",
`title`="Value",
`visible`="(phiCra)"),
list(
`name`="t[cra]",
`title`="",
`type`="text",
`content`="Cramer's V",
`visible`="(phiCra)"),
list(
`name`="v[cra]",
`title`="Value",
`visible`="(phiCra)"))))
self$add(jmvcore::Table$new(
options=options,
name="gamma",
title="Gamma",
visible="(gamma)",
refs="vcdExtra",
clearWith=list(
"rows",
"cols",
"counts",
"layers"),
columns=list(
list(
`name`="gamma",
`title`="Gamma"),
list(
`name`="se",
`title`="Standard Error"),
list(
`name`="cil",
`title`="Lower",
`superTitle`="Confidence Intervals"),
list(
`name`="ciu",
`title`="Upper",
`superTitle`="Confidence Intervals"))))
self$add(jmvcore::Table$new(
options=options,
name="taub",
title="Kendall's Tau-b",
visible="(taub)",
clearWith=list(
"rows",
"cols",
"counts",
"layers"),
columns=list(
list(
`name`="taub",
`title`="Kendall's Tau-B"),
list(
`name`="t",
`title`="t"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="mh",
title="Mantel-Haenszel Test for Trend",
visible="(mh)",
clearWith=list(
"rows",
"cols",
"counts",
"layers"),
columns=list(
list(
`name`="chi2",
`title`="\u03C7\u00B2"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="barplot",
title="Plots",
width=450,
height=400,
renderFun=".barPlot",
visible="(barplot)",
requiresData=TRUE,
clearWith=list(
"rows",
"cols",
"counts",
"layers",
"yaxis",
"yaxisPc",
"xaxis",
"bartype")))}))
contTablesBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"contTablesBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "contTables",
version = c(1,0,0),
options = options,
results = contTablesResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'integerOnly')
}))
#' Contingency Tables
#'
#' The X² test of association (not to be confused with the X² goodness of fit)
#' is used to test whether two categorical variables are independent or
#' associated. If the p-value is low, it suggests the variables are not
#' independent, and that there is a relationship between the two variables.
#'
#'
#' @examples
#' data('HairEyeColor')
#' dat <- as.data.frame(HairEyeColor)
#'
#' contTables(formula = Freq ~ Hair:Eye, dat)
#'
#' #
#' # CONTINGENCY TABLES
#' #
#' # Contingency Tables
#' # -----------------------------------------------------
#' # Hair Brown Blue Hazel Green Total
#' # -----------------------------------------------------
#' # Black 68 20 15 5 108
#' # Brown 119 84 54 29 286
#' # Red 26 17 14 14 71
#' # Blond 7 94 10 16 127
#' # Total 220 215 93 64 592
#' # -----------------------------------------------------
#' #
#' #
#' # X² Tests
#' # -------------------------------
#' # Value df p
#' # -------------------------------
#' # X² 138 9 < .001
#' # N 592
#' # -------------------------------
#' #
#'
#' # Alternatively, omit the left of the formula (`Freq`) if each row
#' # represents a single observation:
#'
#' contTables(formula = ~ Hair:Eye, dat)
#'
#' @param data the data as a data frame
#' @param rows the variable to use as the rows in the contingency table (not
#' necessary when providing a formula, see the examples)
#' @param cols the variable to use as the columns in the contingency table
#' (not necessary when providing a formula, see the examples)
#' @param counts the variable to use as the counts in the contingency table
#' (not necessary when providing a formula, see the examples)
#' @param layers the variables to use to split the contingency table (not
#' necessary when providing a formula, see the examples)
#' @param chiSq \code{TRUE} (default) or \code{FALSE}, provide X²
#' @param chiSqCorr \code{TRUE} or \code{FALSE} (default), provide X² with
#' continuity correction
#' @param zProp \code{TRUE} or \code{FALSE} (default), provide a z test for
#' differences between two proportions
#' @param likeRat \code{TRUE} or \code{FALSE} (default), provide the
#' likelihood ratio
#' @param fisher \code{TRUE} or \code{FALSE} (default), provide Fisher's exact
#' test
#' @param contCoef \code{TRUE} or \code{FALSE} (default), provide the
#' contingency coefficient
#' @param phiCra \code{TRUE} or \code{FALSE} (default), provide Phi and
#' Cramer's V
#' @param diffProp \code{TRUE} or \code{FALSE} (default), provide the
#' differences in proportions (only available for 2x2 tables)
#' @param logOdds \code{TRUE} or \code{FALSE} (default), provide the log odds
#' ratio (only available for 2x2 tables)
#' @param odds \code{TRUE} or \code{FALSE} (default), provide the odds ratio
#' (only available for 2x2 tables)
#' @param relRisk \code{TRUE} or \code{FALSE} (default), provide the relative
#' risk (only available for 2x2 tables)
#' @param ci \code{TRUE} or \code{FALSE} (default), provide confidence
#' intervals for the comparative measures
#' @param ciWidth a number between 50 and 99.9 (default: 95), width of the
#' confidence intervals to provide
#' @param compare \code{columns} or \code{rows} (default), compare
#' columns/rows in difference of proportions or relative risks (2x2 tables)
#' @param hypothesis \code{'different'} (default), \code{'oneGreater'} or
#' \code{'twoGreater'}, the alternative hypothesis; group 1 different to group
#' 2, group 1 greater than group 2, and group 2 greater than group 1
#' respectively
#' @param gamma \code{TRUE} or \code{FALSE} (default), provide gamma
#' @param taub \code{TRUE} or \code{FALSE} (default), provide Kendall's tau-b
#' @param mh \code{TRUE} or \code{FALSE} (default), provide Mantel-Haenszel
#' test for trend
#' @param obs \code{TRUE} or \code{FALSE} (default), provide the observed
#' counts
#' @param exp \code{TRUE} or \code{FALSE} (default), provide the expected
#' counts
#' @param pcRow \code{TRUE} or \code{FALSE} (default), provide row percentages
#' @param pcCol \code{TRUE} or \code{FALSE} (default), provide column
#' percentages
#' @param pcTot \code{TRUE} or \code{FALSE} (default), provide total
#' percentages
#' @param barplot \code{TRUE} or \code{FALSE} (default), show barplots
#' @param yaxis ycounts (default) or ypc. Use respectively \code{counts} or
#' \code{percentages} for the bar plot y-axis
#' @param yaxisPc total_pc (default), column_pc, or row_pc. Use respectively
#' percentages \code{of total}, \code{within columns}, or \code{within rows}
#' for the bar plot y-axis.
#' @param xaxis rows (default), or columns in bar plot X axis
#' @param bartype stack or side by side (default), barplot type
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$freqs} \tab \tab \tab \tab \tab a table of proportions \cr
#' \code{results$chiSq} \tab \tab \tab \tab \tab a table of X² test results \cr
#' \code{results$odds} \tab \tab \tab \tab \tab a table of comparative measures \cr
#' \code{results$nom} \tab \tab \tab \tab \tab a table of the 'nominal' test results \cr
#' \code{results$gamma} \tab \tab \tab \tab \tab a table of the gamma test results \cr
#' \code{results$taub} \tab \tab \tab \tab \tab a table of the Kendall's tau-b test results \cr
#' \code{results$mh} \tab \tab \tab \tab \tab a table of the Mantel-Haenszel test for trend \cr
#' \code{results$barplot} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$freqs$asDF}
#'
#' \code{as.data.frame(results$freqs)}
#'
#' @export
contTables <- function(
data,
rows,
cols,
counts = NULL,
layers = NULL,
chiSq = TRUE,
chiSqCorr = FALSE,
zProp = FALSE,
likeRat = FALSE,
fisher = FALSE,
contCoef = FALSE,
phiCra = FALSE,
diffProp = FALSE,
logOdds = FALSE,
odds = FALSE,
relRisk = FALSE,
ci = TRUE,
ciWidth = 95,
compare = "rows",
hypothesis = "different",
gamma = FALSE,
taub = FALSE,
mh = FALSE,
obs = TRUE,
exp = FALSE,
pcRow = FALSE,
pcCol = FALSE,
pcTot = FALSE,
barplot = FALSE,
yaxis = "ycounts",
yaxisPc = "total_pc",
xaxis = "xrows",
bartype = "dodge",
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("contTables requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(counts))
counts <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="lhs",
type="vars",
subset="1")
if (missing(rows))
rows <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
subset="1")
if (missing(cols))
cols <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
subset="2")
if (missing(layers))
layers <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
subset="3:")
}
if ( ! missing(rows)) rows <- jmvcore::resolveQuo(jmvcore::enquo(rows))
if ( ! missing(cols)) cols <- jmvcore::resolveQuo(jmvcore::enquo(cols))
if ( ! missing(counts)) counts <- jmvcore::resolveQuo(jmvcore::enquo(counts))
if ( ! missing(layers)) layers <- jmvcore::resolveQuo(jmvcore::enquo(layers))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(rows), rows, NULL),
`if`( ! missing(cols), cols, NULL),
`if`( ! missing(counts), counts, NULL),
`if`( ! missing(layers), layers, NULL))
for (v in rows) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in cols) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in layers) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- contTablesOptions$new(
rows = rows,
cols = cols,
counts = counts,
layers = layers,
chiSq = chiSq,
chiSqCorr = chiSqCorr,
zProp = zProp,
likeRat = likeRat,
fisher = fisher,
contCoef = contCoef,
phiCra = phiCra,
diffProp = diffProp,
logOdds = logOdds,
odds = odds,
relRisk = relRisk,
ci = ci,
ciWidth = ciWidth,
compare = compare,
hypothesis = hypothesis,
gamma = gamma,
taub = taub,
mh = mh,
obs = obs,
exp = exp,
pcRow = pcRow,
pcCol = pcCol,
pcTot = pcTot,
barplot = barplot,
yaxis = yaxis,
yaxisPc = yaxisPc,
xaxis = xaxis,
bartype = bartype)
analysis <- contTablesClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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Defines functions contTables
Documented in contTables
# This file is automatically generated, you probably don't want to edit this
contTablesOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"contTablesOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
rows = NULL,
cols = NULL,
counts = NULL,
layers = NULL,
chiSq = TRUE,
chiSqCorr = FALSE,
zProp = FALSE,
likeRat = FALSE,
fisher = FALSE,
contCoef = FALSE,
phiCra = FALSE,
diffProp = FALSE,
logOdds = FALSE,
odds = FALSE,
relRisk = FALSE,
ci = TRUE,
ciWidth = 95,
compare = "rows",
hypothesis = "different",
gamma = FALSE,
taub = FALSE,
mh = FALSE,
obs = TRUE,
exp = FALSE,
pcRow = FALSE,
pcCol = FALSE,
pcTot = FALSE,
barplot = FALSE,
yaxis = "ycounts",
yaxisPc = "total_pc",
xaxis = "xrows",
bartype = "dodge", ...) {
super$initialize(
package="jmv",
name="contTables",
requiresData=TRUE,
...)
private$..rows <- jmvcore::OptionVariable$new(
"rows",
rows,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..cols <- jmvcore::OptionVariable$new(
"cols",
cols,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..counts <- jmvcore::OptionVariable$new(
"counts",
counts,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..layers <- jmvcore::OptionVariables$new(
"layers",
layers,
default=NULL,
permitted=list(
"factor"))
private$..chiSq <- jmvcore::OptionBool$new(
"chiSq",
chiSq,
default=TRUE)
private$..chiSqCorr <- jmvcore::OptionBool$new(
"chiSqCorr",
chiSqCorr,
default=FALSE)
private$..zProp <- jmvcore::OptionBool$new(
"zProp",
zProp,
default=FALSE)
private$..likeRat <- jmvcore::OptionBool$new(
"likeRat",
likeRat,
default=FALSE)
private$..fisher <- jmvcore::OptionBool$new(
"fisher",
fisher,
default=FALSE)
private$..contCoef <- jmvcore::OptionBool$new(
"contCoef",
contCoef,
default=FALSE)
private$..phiCra <- jmvcore::OptionBool$new(
"phiCra",
phiCra,
default=FALSE)
private$..diffProp <- jmvcore::OptionBool$new(
"diffProp",
diffProp,
default=FALSE)
private$..logOdds <- jmvcore::OptionBool$new(
"logOdds",
logOdds,
default=FALSE)
private$..odds <- jmvcore::OptionBool$new(
"odds",
odds,
default=FALSE)
private$..relRisk <- jmvcore::OptionBool$new(
"relRisk",
relRisk,
default=FALSE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=TRUE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..compare <- jmvcore::OptionList$new(
"compare",
compare,
options=list(
"rows",
"columns"),
default="rows")
private$..hypothesis <- jmvcore::OptionList$new(
"hypothesis",
hypothesis,
options=list(
"different",
"oneGreater",
"twoGreater"),
default="different")
private$..gamma <- jmvcore::OptionBool$new(
"gamma",
gamma,
default=FALSE)
private$..taub <- jmvcore::OptionBool$new(
"taub",
taub,
default=FALSE)
private$..mh <- jmvcore::OptionBool$new(
"mh",
mh,
default=FALSE)
private$..obs <- jmvcore::OptionBool$new(
"obs",
obs,
default=TRUE)
private$..exp <- jmvcore::OptionBool$new(
"exp",
exp,
default=FALSE)
private$..pcRow <- jmvcore::OptionBool$new(
"pcRow",
pcRow,
default=FALSE)
private$..pcCol <- jmvcore::OptionBool$new(
"pcCol",
pcCol,
default=FALSE)
private$..pcTot <- jmvcore::OptionBool$new(
"pcTot",
pcTot,
default=FALSE)
private$..barplot <- jmvcore::OptionBool$new(
"barplot",
barplot,
default=FALSE)
private$..yaxis <- jmvcore::OptionList$new(
"yaxis",
yaxis,
options=list(
"ycounts",
"ypc"),
default="ycounts")
private$..yaxisPc <- jmvcore::OptionList$new(
"yaxisPc",
yaxisPc,
options=list(
"total_pc",
"column_pc",
"row_pc"),
default="total_pc")
private$..xaxis <- jmvcore::OptionList$new(
"xaxis",
xaxis,
options=list(
"xrows",
"xcols"),
default="xrows")
private$..bartype <- jmvcore::OptionList$new(
"bartype",
bartype,
options=list(
"dodge",
"stack"),
default="dodge")
self$.addOption(private$..rows)
self$.addOption(private$..cols)
self$.addOption(private$..counts)
self$.addOption(private$..layers)
self$.addOption(private$..chiSq)
self$.addOption(private$..chiSqCorr)
self$.addOption(private$..zProp)
self$.addOption(private$..likeRat)
self$.addOption(private$..fisher)
self$.addOption(private$..contCoef)
self$.addOption(private$..phiCra)
self$.addOption(private$..diffProp)
self$.addOption(private$..logOdds)
self$.addOption(private$..odds)
self$.addOption(private$..relRisk)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..compare)
self$.addOption(private$..hypothesis)
self$.addOption(private$..gamma)
self$.addOption(private$..taub)
self$.addOption(private$..mh)
self$.addOption(private$..obs)
self$.addOption(private$..exp)
self$.addOption(private$..pcRow)
self$.addOption(private$..pcCol)
self$.addOption(private$..pcTot)
self$.addOption(private$..barplot)
self$.addOption(private$..yaxis)
self$.addOption(private$..yaxisPc)
self$.addOption(private$..xaxis)
self$.addOption(private$..bartype)
}),
active = list(
rows = function() private$..rows$value,
cols = function() private$..cols$value,
counts = function() private$..counts$value,
layers = function() private$..layers$value,
chiSq = function() private$..chiSq$value,
chiSqCorr = function() private$..chiSqCorr$value,
zProp = function() private$..zProp$value,
likeRat = function() private$..likeRat$value,
fisher = function() private$..fisher$value,
contCoef = function() private$..contCoef$value,
phiCra = function() private$..phiCra$value,
diffProp = function() private$..diffProp$value,
logOdds = function() private$..logOdds$value,
odds = function() private$..odds$value,
relRisk = function() private$..relRisk$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
compare = function() private$..compare$value,
hypothesis = function() private$..hypothesis$value,
gamma = function() private$..gamma$value,
taub = function() private$..taub$value,
mh = function() private$..mh$value,
obs = function() private$..obs$value,
exp = function() private$..exp$value,
pcRow = function() private$..pcRow$value,
pcCol = function() private$..pcCol$value,
pcTot = function() private$..pcTot$value,
barplot = function() private$..barplot$value,
yaxis = function() private$..yaxis$value,
yaxisPc = function() private$..yaxisPc$value,
xaxis = function() private$..xaxis$value,
bartype = function() private$..bartype$value),
private = list(
..rows = NA,
..cols = NA,
..counts = NA,
..layers = NA,
..chiSq = NA,
..chiSqCorr = NA,
..zProp = NA,
..likeRat = NA,
..fisher = NA,
..contCoef = NA,
..phiCra = NA,
..diffProp = NA,
..logOdds = NA,
..odds = NA,
..relRisk = NA,
..ci = NA,
..ciWidth = NA,
..compare = NA,
..hypothesis = NA,
..gamma = NA,
..taub = NA,
..mh = NA,
..obs = NA,
..exp = NA,
..pcRow = NA,
..pcCol = NA,
..pcTot = NA,
..barplot = NA,
..yaxis = NA,
..yaxisPc = NA,
..xaxis = NA,
..bartype = NA)
)
contTablesResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"contTablesResults",
inherit = jmvcore::Group,
active = list(
freqs = function() private$.items[["freqs"]],
chiSq = function() private$.items[["chiSq"]],
odds = function() private$.items[["odds"]],
nom = function() private$.items[["nom"]],
gamma = function() private$.items[["gamma"]],
taub = function() private$.items[["taub"]],
mh = function() private$.items[["mh"]],
barplot = function() private$.items[["barplot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Contingency Tables")
self$add(jmvcore::Table$new(
options=options,
name="freqs",
title="Contingency Tables",
columns=list(),
clearWith=list(
"rows",
"cols",
"counts",
"layers")))
self$add(jmvcore::Table$new(
options=options,
name="chiSq",
title="\u03C7\u00B2 Tests",
clearWith=list(
"rows",
"cols",
"counts",
"layers",
"hypothesis",
"compare"),
columns=list(
list(
`name`="test[chiSq]",
`title`="",
`type`="text",
`content`="\u03C7\u00B2",
`visible`="(chiSq)"),
list(
`name`="value[chiSq]",
`title`="Value",
`visible`="(chiSq)"),
list(
`name`="df[chiSq]",
`title`="df",
`type`="integer",
`visible`="(chiSq)"),
list(
`name`="p[chiSq]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(chiSq)"),
list(
`name`="test[chiSqCorr]",
`title`="",
`type`="text",
`content`="\u03C7\u00B2 continuity correction",
`visible`="(chiSqCorr)"),
list(
`name`="value[chiSqCorr]",
`title`="Value",
`visible`="(chiSqCorr)"),
list(
`name`="df[chiSqCorr]",
`title`="df",
`type`="integer",
`visible`="(chiSqCorr)"),
list(
`name`="p[chiSqCorr]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(chiSqCorr)"),
list(
`name`="test[zProp]",
`title`="",
`type`="text",
`content`="z test difference in 2 proportions",
`visible`="(zProp)"),
list(
`name`="value[zProp]",
`title`="Value",
`visible`="(zProp)"),
list(
`name`="df[zProp]",
`title`="df",
`visible`="(zProp)"),
list(
`name`="p[zProp]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(zProp)"),
list(
`name`="test[likeRat]",
`title`="",
`type`="text",
`content`="Likelihood ratio",
`visible`="(likeRat)",
`refs`="vcd"),
list(
`name`="value[likeRat]",
`title`="Value",
`visible`="(likeRat)"),
list(
`name`="df[likeRat]",
`title`="df",
`type`="integer",
`visible`="(likeRat)"),
list(
`name`="p[likeRat]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(likeRat)"),
list(
`name`="test[fisher]",
`title`="",
`type`="text",
`content`="Fisher's exact test",
`visible`="(fisher)"),
list(
`name`="value[fisher]",
`title`="Value",
`visible`="(fisher)"),
list(
`name`="p[fisher]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(fisher)"),
list(
`name`="test[N]",
`title`="",
`type`="text",
`content`="N"),
list(
`name`="value[N]",
`title`="Value",
`type`="integer"))))
self$add(jmvcore::Table$new(
options=options,
name="odds",
title="Comparative Measures",
visible="(diffProp || logOdds || odds || relRisk)",
clearWith=list(
"rows",
"cols",
"counts",
"layers",
"ciWidth",
"compare"),
columns=list(
list(
`name`="t[dp]",
`title`="",
`type`="text",
`content`="Difference in 2 proportions",
`visible`="(diffProp)"),
list(
`name`="v[dp]",
`title`="Value",
`visible`="(diffProp)"),
list(
`name`="cil[dp]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(diffProp && ci)"),
list(
`name`="ciu[dp]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(diffProp && ci)"),
list(
`name`="t[lo]",
`title`="",
`type`="text",
`content`="Log odds ratio",
`visible`="(logOdds)",
`refs`="vcd"),
list(
`name`="v[lo]",
`title`="Value",
`visible`="(logOdds)"),
list(
`name`="cil[lo]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(logOdds && ci)"),
list(
`name`="ciu[lo]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(logOdds && ci)"),
list(
`name`="t[o]",
`title`="",
`type`="text",
`content`="Odds ratio",
`visible`="(odds)"),
list(
`name`="v[o]",
`title`="Value",
`visible`="(odds)"),
list(
`name`="cil[o]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(odds && ci)"),
list(
`name`="ciu[o]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(odds && ci)"),
list(
`name`="t[rr]",
`title`="",
`type`="text",
`content`="Relative risk",
`visible`="(relRisk)"),
list(
`name`="v[rr]",
`title`="Value",
`visible`="(relRisk)"),
list(
`name`="cil[rr]",
`title`="Lower",
`superTitle`="Confidence Intervals",
`visible`="(relRisk && ci)"),
list(
`name`="ciu[rr]",
`title`="Upper",
`superTitle`="Confidence Intervals",
`visible`="(relRisk && ci)"))))
self$add(jmvcore::Table$new(
options=options,
name="nom",
title="Nominal",
visible="(contCoef || phiCra)",
columns=list(
list(
`name`="t[cont]",
`title`="",
`type`="text",
`content`="Contingency coefficient",
`visible`="(contCoef)"),
list(
`name`="v[cont]",
`title`="Value",
`visible`="(contCoef)"),
list(
`name`="t[phi]",
`title`="",
`type`="text",
`content`="Phi-coefficient",
`visible`="(phiCra)"),
list(
`name`="v[phi]",
`title`="Value",
`visible`="(phiCra)"),
list(
`name`="t[cra]",
`title`="",
`type`="text",
`content`="Cramer's V",
`visible`="(phiCra)"),
list(
`name`="v[cra]",
`title`="Value",
`visible`="(phiCra)"))))
self$add(jmvcore::Table$new(
options=options,
name="gamma",
title="Gamma",
visible="(gamma)",
refs="vcdExtra",
clearWith=list(
"rows",
"cols",
"counts",
"layers"),
columns=list(
list(
`name`="gamma",
`title`="Gamma"),
list(
`name`="se",
`title`="Standard Error"),
list(
`name`="cil",
`title`="Lower",
`superTitle`="Confidence Intervals"),
list(
`name`="ciu",
`title`="Upper",
`superTitle`="Confidence Intervals"))))
self$add(jmvcore::Table$new(
options=options,
name="taub",
title="Kendall's Tau-b",
visible="(taub)",
clearWith=list(
"rows",
"cols",
"counts",
"layers"),
columns=list(
list(
`name`="taub",
`title`="Kendall's Tau-B"),
list(
`name`="t",
`title`="t"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="mh",
title="Mantel-Haenszel Test for Trend",
visible="(mh)",
clearWith=list(
"rows",
"cols",
"counts",
"layers"),
columns=list(
list(
`name`="chi2",
`title`="\u03C7\u00B2"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="barplot",
title="Plots",
width=450,
height=400,
renderFun=".barPlot",
visible="(barplot)",
requiresData=TRUE,
clearWith=list(
"rows",
"cols",
"counts",
"layers",
"yaxis",
"yaxisPc",
"xaxis",
"bartype")))}))
contTablesBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"contTablesBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "contTables",
version = c(1,0,0),
options = options,
results = contTablesResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'integerOnly')
}))
#' Contingency Tables
#'
#' The X² test of association (not to be confused with the X² goodness of fit)
#' is used to test whether two categorical variables are independent or
#' associated. If the p-value is low, it suggests the variables are not
#' independent, and that there is a relationship between the two variables.
#'
#'
#' @examples
#' data('HairEyeColor')
#' dat <- as.data.frame(HairEyeColor)
#'
#' contTables(formula = Freq ~ Hair:Eye, dat)
#'
#' #
#' # CONTINGENCY TABLES
#' #
#' # Contingency Tables
#' # -----------------------------------------------------
#' # Hair Brown Blue Hazel Green Total
#' # -----------------------------------------------------
#' # Black 68 20 15 5 108
#' # Brown 119 84 54 29 286
#' # Red 26 17 14 14 71
#' # Blond 7 94 10 16 127
#' # Total 220 215 93 64 592
#' # -----------------------------------------------------
#' #
#' #
#' # X² Tests
#' # -------------------------------
#' # Value df p
#' # -------------------------------
#' # X² 138 9 < .001
#' # N 592
#' # -------------------------------
#' #
#'
#' # Alternatively, omit the left of the formula (`Freq`) if each row
#' # represents a single observation:
#'
#' contTables(formula = ~ Hair:Eye, dat)
#'
#' @param data the data as a data frame
#' @param rows the variable to use as the rows in the contingency table (not
#' necessary when providing a formula, see the examples)
#' @param cols the variable to use as the columns in the contingency table
#' (not necessary when providing a formula, see the examples)
#' @param counts the variable to use as the counts in the contingency table
#' (not necessary when providing a formula, see the examples)
#' @param layers the variables to use to split the contingency table (not
#' necessary when providing a formula, see the examples)
#' @param chiSq \code{TRUE} (default) or \code{FALSE}, provide X²
#' @param chiSqCorr \code{TRUE} or \code{FALSE} (default), provide X² with
#' continuity correction
#' @param zProp \code{TRUE} or \code{FALSE} (default), provide a z test for
#' differences between two proportions
#' @param likeRat \code{TRUE} or \code{FALSE} (default), provide the
#' likelihood ratio
#' @param fisher \code{TRUE} or \code{FALSE} (default), provide Fisher's exact
#' test
#' @param contCoef \code{TRUE} or \code{FALSE} (default), provide the
#' contingency coefficient
#' @param phiCra \code{TRUE} or \code{FALSE} (default), provide Phi and
#' Cramer's V
#' @param diffProp \code{TRUE} or \code{FALSE} (default), provide the
#' differences in proportions (only available for 2x2 tables)
#' @param logOdds \code{TRUE} or \code{FALSE} (default), provide the log odds
#' ratio (only available for 2x2 tables)
#' @param odds \code{TRUE} or \code{FALSE} (default), provide the odds ratio
#' (only available for 2x2 tables)
#' @param relRisk \code{TRUE} or \code{FALSE} (default), provide the relative
#' risk (only available for 2x2 tables)
#' @param ci \code{TRUE} or \code{FALSE} (default), provide confidence
#' intervals for the comparative measures
#' @param ciWidth a number between 50 and 99.9 (default: 95), width of the
#' confidence intervals to provide
#' @param compare \code{columns} or \code{rows} (default), compare
#' columns/rows in difference of proportions or relative risks (2x2 tables)
#' @param hypothesis \code{'different'} (default), \code{'oneGreater'} or
#' \code{'twoGreater'}, the alternative hypothesis; group 1 different to group
#' 2, group 1 greater than group 2, and group 2 greater than group 1
#' respectively
#' @param gamma \code{TRUE} or \code{FALSE} (default), provide gamma
#' @param taub \code{TRUE} or \code{FALSE} (default), provide Kendall's tau-b
#' @param mh \code{TRUE} or \code{FALSE} (default), provide Mantel-Haenszel
#' test for trend
#' @param obs \code{TRUE} or \code{FALSE} (default), provide the observed
#' counts
#' @param exp \code{TRUE} or \code{FALSE} (default), provide the expected
#' counts
#' @param pcRow \code{TRUE} or \code{FALSE} (default), provide row percentages
#' @param pcCol \code{TRUE} or \code{FALSE} (default), provide column
#' percentages
#' @param pcTot \code{TRUE} or \code{FALSE} (default), provide total
#' percentages
#' @param barplot \code{TRUE} or \code{FALSE} (default), show barplots
#' @param yaxis ycounts (default) or ypc. Use respectively \code{counts} or
#' \code{percentages} for the bar plot y-axis
#' @param yaxisPc total_pc (default), column_pc, or row_pc. Use respectively
#' percentages \code{of total}, \code{within columns}, or \code{within rows}
#' for the bar plot y-axis.
#' @param xaxis rows (default), or columns in bar plot X axis
#' @param bartype stack or side by side (default), barplot type
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$freqs} \tab \tab \tab \tab \tab a table of proportions \cr
#' \code{results$chiSq} \tab \tab \tab \tab \tab a table of X² test results \cr
#' \code{results$odds} \tab \tab \tab \tab \tab a table of comparative measures \cr
#' \code{results$nom} \tab \tab \tab \tab \tab a table of the 'nominal' test results \cr
#' \code{results$gamma} \tab \tab \tab \tab \tab a table of the gamma test results \cr
#' \code{results$taub} \tab \tab \tab \tab \tab a table of the Kendall's tau-b test results \cr
#' \code{results$mh} \tab \tab \tab \tab \tab a table of the Mantel-Haenszel test for trend \cr
#' \code{results$barplot} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$freqs$asDF}
#'
#' \code{as.data.frame(results$freqs)}
#'
#' @export
contTables <- function(
data,
rows,
cols,
counts = NULL,
layers = NULL,
chiSq = TRUE,
chiSqCorr = FALSE,
zProp = FALSE,
likeRat = FALSE,
fisher = FALSE,
contCoef = FALSE,
phiCra = FALSE,
diffProp = FALSE,
logOdds = FALSE,
odds = FALSE,
relRisk = FALSE,
ci = TRUE,
ciWidth = 95,
compare = "rows",
hypothesis = "different",
gamma = FALSE,
taub = FALSE,
mh = FALSE,
obs = TRUE,
exp = FALSE,
pcRow = FALSE,
pcCol = FALSE,
pcTot = FALSE,
barplot = FALSE,
yaxis = "ycounts",
yaxisPc = "total_pc",
xaxis = "xrows",
bartype = "dodge",
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("contTables requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(counts))
counts <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="lhs",
type="vars",
subset="1")
if (missing(rows))
rows <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
subset="1")
if (missing(cols))
cols <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
subset="2")
if (missing(layers))
layers <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
subset="3:")
}
if ( ! missing(rows)) rows <- jmvcore::resolveQuo(jmvcore::enquo(rows))
if ( ! missing(cols)) cols <- jmvcore::resolveQuo(jmvcore::enquo(cols))
if ( ! missing(counts)) counts <- jmvcore::resolveQuo(jmvcore::enquo(counts))
if ( ! missing(layers)) layers <- jmvcore::resolveQuo(jmvcore::enquo(layers))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(rows), rows, NULL),
`if`( ! missing(cols), cols, NULL),
`if`( ! missing(counts), counts, NULL),
`if`( ! missing(layers), layers, NULL))
for (v in rows) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in cols) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in layers) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- contTablesOptions$new(
rows = rows,
cols = cols,
counts = counts,
layers = layers,
chiSq = chiSq,
chiSqCorr = chiSqCorr,
zProp = zProp,
likeRat = likeRat,
fisher = fisher,
contCoef = contCoef,
phiCra = phiCra,
diffProp = diffProp,
logOdds = logOdds,
odds = odds,
relRisk = relRisk,
ci = ci,
ciWidth = ciWidth,
compare = compare,
hypothesis = hypothesis,
gamma = gamma,
taub = taub,
mh = mh,
obs = obs,
exp = exp,
pcRow = pcRow,
pcCol = pcCol,
pcTot = pcTot,
barplot = barplot,
yaxis = yaxis,
yaxisPc = yaxisPc,
xaxis = xaxis,
bartype = bartype)
analysis <- contTablesClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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