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R/mancova.h.R
In jmv: The 'jamovi' Analyses
Defines functions
mancova
Documented in
mancova
# This file is automatically generated, you probably don't want to edit this
mancovaOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"mancovaOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
deps = NULL,
factors = NULL,
covs = NULL,
multivar = list(
"pillai",
"wilks",
"hotel",
"roy"),
boxM = FALSE,
shapiro = FALSE,
qqPlot = FALSE, ...) {
super$initialize(
package="jmv",
name="mancova",
requiresData=TRUE,
...)
private$..deps <- jmvcore::OptionVariables$new(
"deps",
deps,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"),
default=NULL)
private$..covs <- jmvcore::OptionVariables$new(
"covs",
covs,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..multivar <- jmvcore::OptionNMXList$new(
"multivar",
multivar,
options=list(
"pillai",
"wilks",
"hotel",
"roy"),
default=list(
"pillai",
"wilks",
"hotel",
"roy"))
private$..boxM <- jmvcore::OptionBool$new(
"boxM",
boxM,
default=FALSE)
private$..shapiro <- jmvcore::OptionBool$new(
"shapiro",
shapiro,
default=FALSE)
private$..qqPlot <- jmvcore::OptionBool$new(
"qqPlot",
qqPlot,
default=FALSE)
self$.addOption(private$..deps)
self$.addOption(private$..factors)
self$.addOption(private$..covs)
self$.addOption(private$..multivar)
self$.addOption(private$..boxM)
self$.addOption(private$..shapiro)
self$.addOption(private$..qqPlot)
}),
active = list(
deps = function() private$..deps$value,
factors = function() private$..factors$value,
covs = function() private$..covs$value,
multivar = function() private$..multivar$value,
boxM = function() private$..boxM$value,
shapiro = function() private$..shapiro$value,
qqPlot = function() private$..qqPlot$value),
private = list(
..deps = NA,
..factors = NA,
..covs = NA,
..multivar = NA,
..boxM = NA,
..shapiro = NA,
..qqPlot = NA)
)
mancovaResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"mancovaResults",
inherit = jmvcore::Group,
active = list(
multivar = function() private$.items[["multivar"]],
univar = function() private$.items[["univar"]],
assump = function() private$.items[["assump"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="MANCOVA")
self$add(jmvcore::Table$new(
options=options,
name="multivar",
title="Multivariate Tests",
clearWith=list(
"deps",
"factors",
"covs"),
columns=list(
list(
`name`="term[pillai]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:pillai)"),
list(
`name`="test[pillai]",
`title`="",
`type`="text",
`content`="Pillai's Trace",
`visible`="(multivar:pillai)"),
list(
`name`="stat[pillai]",
`title`="value",
`type`="number",
`visible`="(multivar:pillai)"),
list(
`name`="f[pillai]",
`title`="F",
`type`="number",
`visible`="(multivar:pillai)"),
list(
`name`="df1[pillai]",
`title`="df1",
`type`="integer",
`visible`="(multivar:pillai)"),
list(
`name`="df2[pillai]",
`title`="df2",
`type`="integer",
`visible`="(multivar:pillai)"),
list(
`name`="p[pillai]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:pillai)"),
list(
`name`="term[wilks]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:wilks)"),
list(
`name`="test[wilks]",
`title`="",
`type`="text",
`content`="Wilks' Lambda",
`visible`="(multivar:wilks)"),
list(
`name`="stat[wilks]",
`title`="value",
`type`="number",
`visible`="(multivar:wilks)"),
list(
`name`="f[wilks]",
`title`="F",
`type`="number",
`visible`="(multivar:wilks)"),
list(
`name`="df1[wilks]",
`title`="df1",
`type`="integer",
`visible`="(multivar:wilks)"),
list(
`name`="df2[wilks]",
`title`="df2",
`type`="integer",
`visible`="(multivar:wilks)"),
list(
`name`="p[wilks]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:wilks)"),
list(
`name`="term[hotel]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:hotel)"),
list(
`name`="test[hotel]",
`title`="",
`type`="text",
`content`="Hotelling's Trace",
`visible`="(multivar:hotel)"),
list(
`name`="stat[hotel]",
`title`="value",
`type`="number",
`visible`="(multivar:hotel)"),
list(
`name`="f[hotel]",
`title`="F",
`type`="number",
`visible`="(multivar:hotel)"),
list(
`name`="df1[hotel]",
`title`="df1",
`type`="integer",
`visible`="(multivar:hotel)"),
list(
`name`="df2[hotel]",
`title`="df2",
`type`="integer",
`visible`="(multivar:hotel)"),
list(
`name`="p[hotel]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:hotel)"),
list(
`name`="term[roy]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:roy)"),
list(
`name`="test[roy]",
`title`="",
`type`="text",
`content`="Roy's Largest Root",
`visible`="(multivar:roy)"),
list(
`name`="stat[roy]",
`title`="value",
`type`="number",
`visible`="(multivar:roy)"),
list(
`name`="f[roy]",
`title`="F",
`type`="number",
`visible`="(multivar:roy)"),
list(
`name`="df1[roy]",
`title`="df1",
`type`="integer",
`visible`="(multivar:roy)"),
list(
`name`="df2[roy]",
`title`="df2",
`type`="integer",
`visible`="(multivar:roy)"),
list(
`name`="p[roy]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:roy)"))))
self$add(jmvcore::Table$new(
options=options,
name="univar",
title="Univariate Tests",
clearWith=list(
"deps",
"factors",
"covs"),
columns=list(
list(
`name`="term",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="dep",
`title`="Dependent Variable",
`type`="text"),
list(
`name`="ss",
`title`="Sum of Squares",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="ms",
`title`="Mean Square",
`type`="number"),
list(
`name`="F",
`title`="F",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
boxM = function() private$.items[["boxM"]],
shapiro = function() private$.items[["shapiro"]],
qqPlot = function() private$.items[["qqPlot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="boxM",
title="Box's Homogeneity of Covariance Matrices Test",
visible="(boxM)",
rows=1,
clearWith=list(
"deps",
"factors"),
columns=list(
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="shapiro",
title="Shapiro-Wilk Multivariate Normality Test",
visible="(shapiro)",
rows=1,
refs="mvnormtest",
clearWith=list(
"deps"),
columns=list(
list(
`name`="w",
`title`="W",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="qqPlot",
title="Q-Q Plot Assessing Multivariate Normality",
width=450,
height=400,
renderFun=".qqPlot",
visible="(qqPlot)",
clearWith=list(
"deps")))}))$new(options=options))}))
mancovaBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"mancovaBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "mancova",
version = c(1,0,0),
options = options,
results = mancovaResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' MANCOVA
#'
#' Multivariate Analysis of (Co)Variance (MANCOVA) is used to explore the
#' relationship between multiple dependent variables, and one or more
#' categorical and/or continuous explanatory variables.
#'
#'
#' @examples
#' data('iris')
#'
#' mancova(data = iris,
#' deps = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
#' factors = Species)
#'
#' #
#' # MANCOVA
#' #
#' # Multivariate Tests
#' # ---------------------------------------------------------------------------
#' # value F df1 df2 p
#' # ---------------------------------------------------------------------------
#' # Species Pillai's Trace 1.19 53.5 8 290 < .001
#' # Wilks' Lambda 0.0234 199 8 288 < .001
#' # Hotelling's Trace 32.5 581 8 286 < .001
#' # Roy's Largest Root 32.2 1167 4 145 < .001
#' # ---------------------------------------------------------------------------
#' #
#' #
#' # Univariate Tests
#' # -----------------------------------------------------------------------------------------------
#' # Dependent Variable Sum of Squares df Mean Square F p
#' # -----------------------------------------------------------------------------------------------
#' # Species Sepal.Length 63.21 2 31.6061 119.3 < .001
#' # Sepal.Width 11.34 2 5.6725 49.2 < .001
#' # Petal.Length 437.10 2 218.5514 1180.2 < .001
#' # Petal.Width 80.41 2 40.2067 960.0 < .001
#' # Residuals Sepal.Length 38.96 147 0.2650
#' # Sepal.Width 16.96 147 0.1154
#' # Petal.Length 27.22 147 0.1852
#' # Petal.Width 6.16 147 0.0419
#' # -----------------------------------------------------------------------------------------------
#' #
#'
#' @param data the data as a data frame
#' @param deps a string naming the dependent variable from \code{data},
#' variable must be numeric
#' @param factors a vector of strings naming the factors from \code{data}
#' @param covs a vector of strings naming the covariates from \code{data}
#' @param multivar one or more of \code{'pillai'}, \code{'wilks'},
#' \code{'hotel'}, or \code{'roy'}; use Pillai's Trace, Wilks' Lambda,
#' Hotelling's Trace, and Roy's Largest Root multivariate statistics,
#' respectively
#' @param boxM \code{TRUE} or \code{FALSE} (default), provide Box's M test
#' @param shapiro \code{TRUE} or \code{FALSE} (default), provide Shapiro-Wilk
#' test
#' @param qqPlot \code{TRUE} or \code{FALSE} (default), provide a Q-Q plot of
#' multivariate normality
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$multivar} \tab \tab \tab \tab \tab a table \cr
#' \code{results$univar} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$boxM} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$shapiro} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$qqPlot} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$multivar$asDF}
#'
#' \code{as.data.frame(results$multivar)}
#'
#' @export
mancova <- function(
data,
deps,
factors = NULL,
covs = NULL,
multivar = list(
"pillai",
"wilks",
"hotel",
"roy"),
boxM = FALSE,
shapiro = FALSE,
qqPlot = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("mancova requires jmvcore to be installed (restart may be required)")
if ( ! missing(deps)) deps <- jmvcore::resolveQuo(jmvcore::enquo(deps))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if ( ! missing(covs)) covs <- jmvcore::resolveQuo(jmvcore::enquo(covs))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(deps), deps, NULL),
`if`( ! missing(factors), factors, NULL),
`if`( ! missing(covs), covs, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- mancovaOptions$new(
deps = deps,
factors = factors,
covs = covs,
multivar = multivar,
boxM = boxM,
shapiro = shapiro,
qqPlot = qqPlot)
analysis <- mancovaClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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Defines functions mancova
Documented in mancova
# This file is automatically generated, you probably don't want to edit this
mancovaOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"mancovaOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
deps = NULL,
factors = NULL,
covs = NULL,
multivar = list(
"pillai",
"wilks",
"hotel",
"roy"),
boxM = FALSE,
shapiro = FALSE,
qqPlot = FALSE, ...) {
super$initialize(
package="jmv",
name="mancova",
requiresData=TRUE,
...)
private$..deps <- jmvcore::OptionVariables$new(
"deps",
deps,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"),
default=NULL)
private$..covs <- jmvcore::OptionVariables$new(
"covs",
covs,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..multivar <- jmvcore::OptionNMXList$new(
"multivar",
multivar,
options=list(
"pillai",
"wilks",
"hotel",
"roy"),
default=list(
"pillai",
"wilks",
"hotel",
"roy"))
private$..boxM <- jmvcore::OptionBool$new(
"boxM",
boxM,
default=FALSE)
private$..shapiro <- jmvcore::OptionBool$new(
"shapiro",
shapiro,
default=FALSE)
private$..qqPlot <- jmvcore::OptionBool$new(
"qqPlot",
qqPlot,
default=FALSE)
self$.addOption(private$..deps)
self$.addOption(private$..factors)
self$.addOption(private$..covs)
self$.addOption(private$..multivar)
self$.addOption(private$..boxM)
self$.addOption(private$..shapiro)
self$.addOption(private$..qqPlot)
}),
active = list(
deps = function() private$..deps$value,
factors = function() private$..factors$value,
covs = function() private$..covs$value,
multivar = function() private$..multivar$value,
boxM = function() private$..boxM$value,
shapiro = function() private$..shapiro$value,
qqPlot = function() private$..qqPlot$value),
private = list(
..deps = NA,
..factors = NA,
..covs = NA,
..multivar = NA,
..boxM = NA,
..shapiro = NA,
..qqPlot = NA)
)
mancovaResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"mancovaResults",
inherit = jmvcore::Group,
active = list(
multivar = function() private$.items[["multivar"]],
univar = function() private$.items[["univar"]],
assump = function() private$.items[["assump"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="MANCOVA")
self$add(jmvcore::Table$new(
options=options,
name="multivar",
title="Multivariate Tests",
clearWith=list(
"deps",
"factors",
"covs"),
columns=list(
list(
`name`="term[pillai]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:pillai)"),
list(
`name`="test[pillai]",
`title`="",
`type`="text",
`content`="Pillai's Trace",
`visible`="(multivar:pillai)"),
list(
`name`="stat[pillai]",
`title`="value",
`type`="number",
`visible`="(multivar:pillai)"),
list(
`name`="f[pillai]",
`title`="F",
`type`="number",
`visible`="(multivar:pillai)"),
list(
`name`="df1[pillai]",
`title`="df1",
`type`="integer",
`visible`="(multivar:pillai)"),
list(
`name`="df2[pillai]",
`title`="df2",
`type`="integer",
`visible`="(multivar:pillai)"),
list(
`name`="p[pillai]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:pillai)"),
list(
`name`="term[wilks]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:wilks)"),
list(
`name`="test[wilks]",
`title`="",
`type`="text",
`content`="Wilks' Lambda",
`visible`="(multivar:wilks)"),
list(
`name`="stat[wilks]",
`title`="value",
`type`="number",
`visible`="(multivar:wilks)"),
list(
`name`="f[wilks]",
`title`="F",
`type`="number",
`visible`="(multivar:wilks)"),
list(
`name`="df1[wilks]",
`title`="df1",
`type`="integer",
`visible`="(multivar:wilks)"),
list(
`name`="df2[wilks]",
`title`="df2",
`type`="integer",
`visible`="(multivar:wilks)"),
list(
`name`="p[wilks]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:wilks)"),
list(
`name`="term[hotel]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:hotel)"),
list(
`name`="test[hotel]",
`title`="",
`type`="text",
`content`="Hotelling's Trace",
`visible`="(multivar:hotel)"),
list(
`name`="stat[hotel]",
`title`="value",
`type`="number",
`visible`="(multivar:hotel)"),
list(
`name`="f[hotel]",
`title`="F",
`type`="number",
`visible`="(multivar:hotel)"),
list(
`name`="df1[hotel]",
`title`="df1",
`type`="integer",
`visible`="(multivar:hotel)"),
list(
`name`="df2[hotel]",
`title`="df2",
`type`="integer",
`visible`="(multivar:hotel)"),
list(
`name`="p[hotel]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:hotel)"),
list(
`name`="term[roy]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(multivar:roy)"),
list(
`name`="test[roy]",
`title`="",
`type`="text",
`content`="Roy's Largest Root",
`visible`="(multivar:roy)"),
list(
`name`="stat[roy]",
`title`="value",
`type`="number",
`visible`="(multivar:roy)"),
list(
`name`="f[roy]",
`title`="F",
`type`="number",
`visible`="(multivar:roy)"),
list(
`name`="df1[roy]",
`title`="df1",
`type`="integer",
`visible`="(multivar:roy)"),
list(
`name`="df2[roy]",
`title`="df2",
`type`="integer",
`visible`="(multivar:roy)"),
list(
`name`="p[roy]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(multivar:roy)"))))
self$add(jmvcore::Table$new(
options=options,
name="univar",
title="Univariate Tests",
clearWith=list(
"deps",
"factors",
"covs"),
columns=list(
list(
`name`="term",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="dep",
`title`="Dependent Variable",
`type`="text"),
list(
`name`="ss",
`title`="Sum of Squares",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="ms",
`title`="Mean Square",
`type`="number"),
list(
`name`="F",
`title`="F",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
boxM = function() private$.items[["boxM"]],
shapiro = function() private$.items[["shapiro"]],
qqPlot = function() private$.items[["qqPlot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="boxM",
title="Box's Homogeneity of Covariance Matrices Test",
visible="(boxM)",
rows=1,
clearWith=list(
"deps",
"factors"),
columns=list(
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="shapiro",
title="Shapiro-Wilk Multivariate Normality Test",
visible="(shapiro)",
rows=1,
refs="mvnormtest",
clearWith=list(
"deps"),
columns=list(
list(
`name`="w",
`title`="W",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="qqPlot",
title="Q-Q Plot Assessing Multivariate Normality",
width=450,
height=400,
renderFun=".qqPlot",
visible="(qqPlot)",
clearWith=list(
"deps")))}))$new(options=options))}))
mancovaBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"mancovaBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "mancova",
version = c(1,0,0),
options = options,
results = mancovaResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' MANCOVA
#'
#' Multivariate Analysis of (Co)Variance (MANCOVA) is used to explore the
#' relationship between multiple dependent variables, and one or more
#' categorical and/or continuous explanatory variables.
#'
#'
#' @examples
#' data('iris')
#'
#' mancova(data = iris,
#' deps = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width),
#' factors = Species)
#'
#' #
#' # MANCOVA
#' #
#' # Multivariate Tests
#' # ---------------------------------------------------------------------------
#' # value F df1 df2 p
#' # ---------------------------------------------------------------------------
#' # Species Pillai's Trace 1.19 53.5 8 290 < .001
#' # Wilks' Lambda 0.0234 199 8 288 < .001
#' # Hotelling's Trace 32.5 581 8 286 < .001
#' # Roy's Largest Root 32.2 1167 4 145 < .001
#' # ---------------------------------------------------------------------------
#' #
#' #
#' # Univariate Tests
#' # -----------------------------------------------------------------------------------------------
#' # Dependent Variable Sum of Squares df Mean Square F p
#' # -----------------------------------------------------------------------------------------------
#' # Species Sepal.Length 63.21 2 31.6061 119.3 < .001
#' # Sepal.Width 11.34 2 5.6725 49.2 < .001
#' # Petal.Length 437.10 2 218.5514 1180.2 < .001
#' # Petal.Width 80.41 2 40.2067 960.0 < .001
#' # Residuals Sepal.Length 38.96 147 0.2650
#' # Sepal.Width 16.96 147 0.1154
#' # Petal.Length 27.22 147 0.1852
#' # Petal.Width 6.16 147 0.0419
#' # -----------------------------------------------------------------------------------------------
#' #
#'
#' @param data the data as a data frame
#' @param deps a string naming the dependent variable from \code{data},
#' variable must be numeric
#' @param factors a vector of strings naming the factors from \code{data}
#' @param covs a vector of strings naming the covariates from \code{data}
#' @param multivar one or more of \code{'pillai'}, \code{'wilks'},
#' \code{'hotel'}, or \code{'roy'}; use Pillai's Trace, Wilks' Lambda,
#' Hotelling's Trace, and Roy's Largest Root multivariate statistics,
#' respectively
#' @param boxM \code{TRUE} or \code{FALSE} (default), provide Box's M test
#' @param shapiro \code{TRUE} or \code{FALSE} (default), provide Shapiro-Wilk
#' test
#' @param qqPlot \code{TRUE} or \code{FALSE} (default), provide a Q-Q plot of
#' multivariate normality
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$multivar} \tab \tab \tab \tab \tab a table \cr
#' \code{results$univar} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$boxM} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$shapiro} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$qqPlot} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$multivar$asDF}
#'
#' \code{as.data.frame(results$multivar)}
#'
#' @export
mancova <- function(
data,
deps,
factors = NULL,
covs = NULL,
multivar = list(
"pillai",
"wilks",
"hotel",
"roy"),
boxM = FALSE,
shapiro = FALSE,
qqPlot = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("mancova requires jmvcore to be installed (restart may be required)")
if ( ! missing(deps)) deps <- jmvcore::resolveQuo(jmvcore::enquo(deps))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if ( ! missing(covs)) covs <- jmvcore::resolveQuo(jmvcore::enquo(covs))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(deps), deps, NULL),
`if`( ! missing(factors), factors, NULL),
`if`( ! missing(covs), covs, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- mancovaOptions$new(
deps = deps,
factors = factors,
covs = covs,
multivar = multivar,
boxM = boxM,
shapiro = shapiro,
qqPlot = qqPlot)
analysis <- mancovaClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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