R/anovarm.h.R
In jamovi/jmv: The 'jamovi' Analyses
Defines functions
anovaRM
# This file is automatically generated, you probably don't want to edit this
anovaRMOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaRMOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
rm = list(
list(label="RM Factor 1", levels=list(
"Level 1",
"Level 2"))),
rmCells = NULL,
bs = NULL,
cov = NULL,
effectSize = NULL,
depLabel = "Dependent",
rmTerms = NULL,
bsTerms = NULL,
ss = "3",
spherTests = FALSE,
spherCorr = list(
"none"),
leveneTest = FALSE,
qq = FALSE,
contrasts = NULL,
postHoc = NULL,
postHocCorr = list(
"tukey"),
emMeans = list(
list()),
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE,
ciWidthEmm = 95,
emmPlotData = FALSE,
emmPlotError = "ci",
groupSumm = FALSE, ...) {
super$initialize(
package="jmv",
name="anovaRM",
requiresData=TRUE,
...)
private$..rm <- jmvcore::OptionArray$new(
"rm",
rm,
default=list(
list(label="RM Factor 1", levels=list(
"Level 1",
"Level 2"))),
template=jmvcore::OptionGroup$new(
"rm",
NULL,
elements=list(
jmvcore::OptionString$new(
"label",
NULL),
jmvcore::OptionArray$new(
"levels",
NULL,
template=jmvcore::OptionString$new(
"levels",
NULL)))))
private$..rmCells <- jmvcore::OptionArray$new(
"rmCells",
rmCells,
default=NULL,
template=jmvcore::OptionGroup$new(
"rmCells",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"measure",
NULL),
jmvcore::OptionArray$new(
"cell",
NULL,
template=jmvcore::OptionString$new(
"cell",
NULL)))))
private$..bs <- jmvcore::OptionVariables$new(
"bs",
bs,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"),
default=NULL)
private$..cov <- jmvcore::OptionVariables$new(
"cov",
cov,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..effectSize <- jmvcore::OptionNMXList$new(
"effectSize",
effectSize,
options=list(
"eta",
"partEta",
"ges",
"omega"),
default=NULL)
private$..depLabel <- jmvcore::OptionString$new(
"depLabel",
depLabel,
default="Dependent")
private$..rmTerms <- jmvcore::OptionTerms$new(
"rmTerms",
rmTerms,
default=NULL)
private$..bsTerms <- jmvcore::OptionTerms$new(
"bsTerms",
bsTerms,
default=NULL)
private$..ss <- jmvcore::OptionList$new(
"ss",
ss,
options=list(
"2",
"3"),
default="3")
private$..spherTests <- jmvcore::OptionBool$new(
"spherTests",
spherTests,
default=FALSE)
private$..spherCorr <- jmvcore::OptionNMXList$new(
"spherCorr",
spherCorr,
options=list(
"none",
"GG",
"HF"),
default=list(
"none"))
private$..leveneTest <- jmvcore::OptionBool$new(
"leveneTest",
leveneTest,
default=FALSE)
private$..qq <- jmvcore::OptionBool$new(
"qq",
qq,
default=FALSE)
private$..contrasts <- jmvcore::OptionArray$new(
"contrasts",
contrasts,
hidden=TRUE,
items="(bs)",
default=NULL,
template=jmvcore::OptionGroup$new(
"contrasts",
NULL,
elements=list(
jmvcore::OptionString$new(
"var",
NULL,
content="$key"),
jmvcore::OptionList$new(
"type",
NULL,
options=list(
"none",
"deviation",
"simple",
"difference",
"helmert",
"repeated",
"polynomial")))))
private$..postHoc <- jmvcore::OptionArray$new(
"postHoc",
postHoc,
template=jmvcore::OptionArray$new(
"postHoc",
NULL,
template=jmvcore::OptionString$new(
"postHoc",
NULL)),
default=NULL)
private$..postHocCorr <- jmvcore::OptionNMXList$new(
"postHocCorr",
postHocCorr,
options=list(
"none",
"tukey",
"scheffe",
"bonf",
"holm"),
default=list(
"tukey"))
private$..emMeans <- jmvcore::OptionArray$new(
"emMeans",
emMeans,
default=list(
list()),
template=jmvcore::OptionVariables$new(
"emMeans",
NULL))
private$..emmPlots <- jmvcore::OptionBool$new(
"emmPlots",
emmPlots,
default=TRUE)
private$..emmTables <- jmvcore::OptionBool$new(
"emmTables",
emmTables,
default=FALSE)
private$..emmWeights <- jmvcore::OptionBool$new(
"emmWeights",
emmWeights,
default=TRUE)
private$..ciWidthEmm <- jmvcore::OptionNumber$new(
"ciWidthEmm",
ciWidthEmm,
min=50,
max=99.9,
default=95)
private$..emmPlotData <- jmvcore::OptionBool$new(
"emmPlotData",
emmPlotData,
default=FALSE)
private$..emmPlotError <- jmvcore::OptionList$new(
"emmPlotError",
emmPlotError,
options=list(
"none",
"ci",
"se"),
default="ci")
private$..groupSumm <- jmvcore::OptionBool$new(
"groupSumm",
groupSumm,
default=FALSE)
self$.addOption(private$..rm)
self$.addOption(private$..rmCells)
self$.addOption(private$..bs)
self$.addOption(private$..cov)
self$.addOption(private$..effectSize)
self$.addOption(private$..depLabel)
self$.addOption(private$..rmTerms)
self$.addOption(private$..bsTerms)
self$.addOption(private$..ss)
self$.addOption(private$..spherTests)
self$.addOption(private$..spherCorr)
self$.addOption(private$..leveneTest)
self$.addOption(private$..qq)
self$.addOption(private$..contrasts)
self$.addOption(private$..postHoc)
self$.addOption(private$..postHocCorr)
self$.addOption(private$..emMeans)
self$.addOption(private$..emmPlots)
self$.addOption(private$..emmTables)
self$.addOption(private$..emmWeights)
self$.addOption(private$..ciWidthEmm)
self$.addOption(private$..emmPlotData)
self$.addOption(private$..emmPlotError)
self$.addOption(private$..groupSumm)
}),
active = list(
rm = function() private$..rm$value,
rmCells = function() private$..rmCells$value,
bs = function() private$..bs$value,
cov = function() private$..cov$value,
effectSize = function() private$..effectSize$value,
depLabel = function() private$..depLabel$value,
rmTerms = function() private$..rmTerms$value,
bsTerms = function() private$..bsTerms$value,
ss = function() private$..ss$value,
spherTests = function() private$..spherTests$value,
spherCorr = function() private$..spherCorr$value,
leveneTest = function() private$..leveneTest$value,
qq = function() private$..qq$value,
contrasts = function() private$..contrasts$value,
postHoc = function() private$..postHoc$value,
postHocCorr = function() private$..postHocCorr$value,
emMeans = function() private$..emMeans$value,
emmPlots = function() private$..emmPlots$value,
emmTables = function() private$..emmTables$value,
emmWeights = function() private$..emmWeights$value,
ciWidthEmm = function() private$..ciWidthEmm$value,
emmPlotData = function() private$..emmPlotData$value,
emmPlotError = function() private$..emmPlotError$value,
groupSumm = function() private$..groupSumm$value),
private = list(
..rm = NA,
..rmCells = NA,
..bs = NA,
..cov = NA,
..effectSize = NA,
..depLabel = NA,
..rmTerms = NA,
..bsTerms = NA,
..ss = NA,
..spherTests = NA,
..spherCorr = NA,
..leveneTest = NA,
..qq = NA,
..contrasts = NA,
..postHoc = NA,
..postHocCorr = NA,
..emMeans = NA,
..emmPlots = NA,
..emmTables = NA,
..emmWeights = NA,
..ciWidthEmm = NA,
..emmPlotData = NA,
..emmPlotError = NA,
..groupSumm = NA)
)
anovaRMResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaRMResults",
inherit = jmvcore::Group,
active = list(
rmTable = function() private$.items[["rmTable"]],
bsTable = function() private$.items[["bsTable"]],
assump = function() private$.items[["assump"]],
contrasts = function() private$.items[["contrasts"]],
postHoc = function() private$.items[["postHoc"]],
emm = function() private$.items[["emm"]],
groupSummary = function() private$.items[["groupSummary"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Repeated Measures ANOVA")
self$add(jmvcore::Table$new(
options=options,
name="rmTable",
title="Within Subjects Effects",
refs="afex",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"),
columns=list(
list(
`name`="name[none]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(spherCorr:none)"),
list(
`name`="correction[none]",
`title`="Sphericity Correction",
`type`="text",
`content`="None",
`visible`="(spherCorr:none && (spherCorr:GG || spherCorr:HF))"),
list(
`name`="ss[none]",
`title`="Sum of Squares",
`type`="number",
`visible`="(spherCorr:none)"),
list(
`name`="df[none]",
`title`="df",
`type`="integer",
`visible`="(spherCorr:none)"),
list(
`name`="ms[none]",
`title`="Mean Square",
`type`="number",
`visible`="(spherCorr:none)"),
list(
`name`="F[none]",
`title`="F",
`type`="number",
`visible`="(spherCorr:none)"),
list(
`name`="p[none]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(spherCorr:none)"),
list(
`name`="ges[none]",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges && spherCorr:none)"),
list(
`name`="eta[none]",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta && spherCorr:none)"),
list(
`name`="partEta[none]",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta && spherCorr:none)"),
list(
`name`="omega[none]",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega && spherCorr:none)"),
list(
`name`="name[GG]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(spherCorr:GG)"),
list(
`name`="correction[GG]",
`title`="Sphericity Correction",
`type`="text",
`content`="Greenhouse-Geisser",
`visible`="(spherCorr:GG)"),
list(
`name`="ss[GG]",
`title`="Sum of Squares",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="df[GG]",
`title`="df",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="ms[GG]",
`title`="Mean Square",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="F[GG]",
`title`="F",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="p[GG]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(spherCorr:GG)"),
list(
`name`="ges[GG]",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges && spherCorr:GG)"),
list(
`name`="eta[GG]",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta && spherCorr:GG)"),
list(
`name`="partEta[GG]",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta && spherCorr:GG)"),
list(
`name`="omega[GG]",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega && spherCorr:GG)"),
list(
`name`="name[HF]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(spherCorr:HF)"),
list(
`name`="correction[HF]",
`title`="Sphericity Correction",
`type`="text",
`content`="Huynh-Feldt",
`visible`="(spherCorr:HF)"),
list(
`name`="ss[HF]",
`title`="Sum of Squares",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="df[HF]",
`title`="df",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="ms[HF]",
`title`="Mean Square",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="F[HF]",
`title`="F",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="p[HF]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(spherCorr:HF)"),
list(
`name`="ges[HF]",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges && spherCorr:HF)"),
list(
`name`="eta[HF]",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta && spherCorr:HF)"),
list(
`name`="partEta[HF]",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta && spherCorr:HF)"),
list(
`name`="omega[HF]",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega && spherCorr:HF)"))))
self$add(jmvcore::Table$new(
options=options,
name="bsTable",
title="Between Subjects Effects",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"),
columns=list(
list(
`name`="name",
`title`="",
`content`=".",
`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"),
list(
`name`="ges",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges)"),
list(
`name`="eta",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta)"),
list(
`name`="partEta",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta)"),
list(
`name`="omega",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega)"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
spherTable = function() private$.items[["spherTable"]],
leveneTable = function() private$.items[["leveneTable"]],
qq = function() private$.items[["qq"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumptions")
self$add(jmvcore::Table$new(
options=options,
name="spherTable",
title="Tests of Sphericity",
visible="(spherTests)",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text"),
list(
`name`="mauch",
`title`="Mauchly's W",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="gg",
`title`="Greenhouse-Geisser \u03B5",
`type`="number"),
list(
`name`="hf",
`title`="Huynh-Feldt \u03B5",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="leveneTable",
title="Homogeneity of Variances Test (Levene's)",
visible="(leveneTest)",
clearWith=list(
"bs",
"rmCells",
"cov"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text"),
list(
`name`="F",
`type`="number"),
list(
`name`="df1",
`type`="integer"),
list(
`name`="df2",
`type`="integer"),
list(
`name`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="qq",
title="Q-Q Plot",
visible="(qq)",
width=450,
height=400,
renderFun=".qqPlot",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms")))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="contrasts",
title="Contrasts",
items=0,
visible="(contrasts)",
template=jmvcore::Table$new(
options=options,
title="Contrasts - $key",
rows=0,
columns=list(
list(
`name`="contrast",
`title`="",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")))))
self$add(jmvcore::Array$new(
options=options,
name="postHoc",
title="Post Hoc Tests",
items="(postHoc)",
refs="emmeans",
template=jmvcore::Table$new(
options=options,
title="",
columns=list(),
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"))))
self$add(jmvcore::Array$new(
options=options,
name="emm",
title="Estimated Marginal Means",
refs="emmeans",
clearWith=list(
"emMeans"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
emmPlot = function() private$.items[["emmPlot"]],
emmTable = function() private$.items[["emmTable"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Image$new(
options=options,
name="emmPlot",
title="",
width=450,
height=400,
renderFun=".emmPlot",
visible="(emmPlots)",
clearWith=list(
"ciWidthEmm",
"emmWeights",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms",
"emmPlotData",
"emmPlotError",
"depLabel")))
self$add(jmvcore::Table$new(
options=options,
name="emmTable",
title="",
visible="(emmTables)",
columns=list(),
clearWith=list(
"ciWidthEmm",
"emmWeights",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms")))}))$new(options=options)))
self$add(jmvcore::Table$new(
options=options,
name="groupSummary",
title="Group Summary",
visible="(groupSumm)",
clearWith=list(
"dep",
"factors",
"cov"),
columns=list(
list(
`name`="n",
`title`="N",
`type`="integer"),
list(
`name`="ex",
`title`="Excluded",
`type`="integer"))))}))
anovaRMBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaRMBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "anovaRM",
version = c(2,0,0),
options = options,
results = anovaRMResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Repeated Measures ANOVA
#'
#' The Repeated Measures ANOVA is used to explore the relationship
#' between a continuous dependent variable and one or more categorical
#' explanatory variables, where one or more of the explanatory variables
#' are 'within subjects' (where multiple measurements are from the same
#' subject). Additionally, this analysis allows the inclusion of
#' covariates, allowing for repeated measures ANCOVAs as well.
#'
#' This analysis requires that the data be in 'wide format', where each
#' row represents a subject (as opposed to long format, where each
#' measurement of the dependent variable is represented as a row).
#'
#' A non-parametric equivalent of the repeated measures ANOVA also exists;
#' the Friedman test. However, it has the limitation of only being able to
#' test a single factor.
#'
#'
#' @examples
#' \donttest{
#'
#' data('bugs', package = 'jmv')
#'
#' anovaRM(
#' data = bugs,
#' rm = list(
#' list(
#' label = 'Frightening',
#' levels = c('Low', 'High'))),
#' rmCells = list(
#' list(
#' measure = 'LDLF',
#' cell = 'Low'),
#' list(
#' measure = 'LDHF',
#' cell = 'High')),
#' rmTerms = list(
#' 'Frightening'))
#'
#' #
#' # REPEATED MEASURES ANOVA
#' #
#' # Within Subjects Effects
#' # -----------------------------------------------------------------------
#' # Sum of Squares df Mean Square F p
#' # -----------------------------------------------------------------------
#' # Frightening 126 1 126.11 44.2 < .001
#' # Residual 257 90 2.85
#' # -----------------------------------------------------------------------
#' # Note. Type 3 Sums of Squares
#' #
#' #
#' #
#' # Between Subjects Effects
#' # -----------------------------------------------------------------
#' # Sum of Squares df Mean Square F p
#' # -----------------------------------------------------------------
#' # Residual 954 90 10.6
#' # -----------------------------------------------------------------
#' # Note. Type 3 Sums of Squares
#' #
#'}
#' @param data the data as a data frame
#' @param rm a list of lists, where each list describes the \code{label} (as a
#' string) and the \code{levels} (as vector of strings) of a particular
#' repeated measures factor
#' @param rmCells a list of lists, where each list decribes a repeated measure
#' (as a string) from \code{data} defined as \code{measure} and the particular
#' combination of levels from \code{rm} that it belongs to (as a vector of
#' strings) defined as \code{cell}
#' @param bs a vector of strings naming the between subjects factors from
#' \code{data}
#' @param cov a vector of strings naming the covariates from \code{data}.
#' Variables must be numeric
#' @param effectSize one or more of \code{'eta'}, \code{'partEta'}, or
#' \code{'omega'}; use eta², partial eta², and omega² effect sizes,
#' respectively
#' @param depLabel a string (default: 'Dependent') describing the label used
#' for the dependent variable throughout the analysis
#' @param rmTerms a list of character vectors describing the repeated measures
#' terms to go into the model
#' @param bsTerms a list of character vectors describing the between subjects
#' terms to go into the model
#' @param ss \code{'2'} or \code{'3'} (default), the sum of squares to use
#' @param spherTests \code{TRUE} or \code{FALSE} (default), perform sphericity
#' tests
#' @param spherCorr one or more of \code{'none'} (default), \code{'GG'}, or
#' \code{}HF\code{}; use no p-value correction, the Greenhouse-Geisser p-value
#' correction, and the Huynh-Feldt p-value correction for shericity,
#' respectively
#' @param leveneTest \code{TRUE} or \code{FALSE} (default), test for
#' homogeneity of variances (i.e., Levene's test)
#' @param qq \code{TRUE} or \code{FALSE} (default), provide a Q-Q plot of
#' residuals
#' @param contrasts in development
#' @param postHoc a list of character vectors describing the post-hoc tests
#' that need to be computed
#' @param postHocCorr one or more of \code{'none'}, \code{'tukey'} (default),
#' \code{'scheffe'}, \code{'bonf'}, or \code{'holm'}; use no, Tukey, Scheffe,
#' Bonferroni and Holm posthoc corrections, respectively
#' @param emMeans a list of lists specifying the variables for which the
#' estimated marginal means need to be calculate. Supports up to three
#' variables per term.
#' @param emmPlots \code{TRUE} (default) or \code{FALSE}, provide estimated
#' marginal means plots
#' @param emmTables \code{TRUE} or \code{FALSE} (default), provide estimated
#' marginal means tables
#' @param emmWeights \code{TRUE} (default) or \code{FALSE}, weigh each cell
#' equally or weigh them according to the cell frequency
#' @param ciWidthEmm a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the estimated marginal means
#' @param emmPlotData \code{TRUE} or \code{FALSE} (default), plot the data on
#' top of the marginal means
#' @param emmPlotError \code{'none'}, \code{'ci'} (default), or \code{'se'}.
#' Use no error bars, use confidence intervals, or use standard errors on the
#' marginal mean plots, respectively
#' @param groupSumm \code{TRUE} or \code{FALSE} (default), report a summary of
#' the different groups
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$rmTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$bsTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$spherTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$leveneTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$qq} \tab \tab \tab \tab \tab a q-q plot \cr
#' \code{results$contrasts} \tab \tab \tab \tab \tab an array of tables \cr
#' \code{results$postHoc} \tab \tab \tab \tab \tab an array of tables \cr
#' \code{results$emm} \tab \tab \tab \tab \tab an array of the estimated marginal means plots + tables \cr
#' \code{results$groupSummary} \tab \tab \tab \tab \tab a summary of the groups \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$rmTable$asDF}
#'
#' \code{as.data.frame(results$rmTable)}
#'
#' @export
anovaRM <- function(
data,
rm = list(
list(label="RM Factor 1", levels=list(
"Level 1",
"Level 2"))),
rmCells = NULL,
bs = NULL,
cov = NULL,
effectSize = NULL,
depLabel = "Dependent",
rmTerms = NULL,
bsTerms = NULL,
ss = "3",
spherTests = FALSE,
spherCorr = list(
"none"),
leveneTest = FALSE,
qq = FALSE,
contrasts = NULL,
postHoc = NULL,
postHocCorr = list(
"tukey"),
emMeans = list(
list()),
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE,
ciWidthEmm = 95,
emmPlotData = FALSE,
emmPlotError = "ci",
groupSumm = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("anovaRM requires jmvcore to be installed (restart may be required)")
if ( ! missing(bs)) bs <- jmvcore::resolveQuo(jmvcore::enquo(bs))
if ( ! missing(cov)) cov <- jmvcore::resolveQuo(jmvcore::enquo(cov))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(bs), bs, NULL),
`if`( ! missing(cov), cov, NULL))
for (v in bs) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(rmTerms, "formula")) rmTerms <- jmvcore::decomposeFormula(rmTerms)
if (inherits(bsTerms, "formula")) bsTerms <- jmvcore::decomposeFormula(bsTerms)
if (inherits(emMeans, "formula")) emMeans <- jmvcore::decomposeFormula(emMeans)
options <- anovaRMOptions$new(
rm = rm,
rmCells = rmCells,
bs = bs,
cov = cov,
effectSize = effectSize,
depLabel = depLabel,
rmTerms = rmTerms,
bsTerms = bsTerms,
ss = ss,
spherTests = spherTests,
spherCorr = spherCorr,
leveneTest = leveneTest,
qq = qq,
contrasts = contrasts,
postHoc = postHoc,
postHocCorr = postHocCorr,
emMeans = emMeans,
emmPlots = emmPlots,
emmTables = emmTables,
emmWeights = emmWeights,
ciWidthEmm = ciWidthEmm,
emmPlotData = emmPlotData,
emmPlotError = emmPlotError,
groupSumm = groupSumm)
analysis <- anovaRMClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
jamovi/jmv documentation built on April 13, 2024, 10:03 p.m.
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Defines functions anovaRM
# This file is automatically generated, you probably don't want to edit this
anovaRMOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaRMOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
rm = list(
list(label="RM Factor 1", levels=list(
"Level 1",
"Level 2"))),
rmCells = NULL,
bs = NULL,
cov = NULL,
effectSize = NULL,
depLabel = "Dependent",
rmTerms = NULL,
bsTerms = NULL,
ss = "3",
spherTests = FALSE,
spherCorr = list(
"none"),
leveneTest = FALSE,
qq = FALSE,
contrasts = NULL,
postHoc = NULL,
postHocCorr = list(
"tukey"),
emMeans = list(
list()),
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE,
ciWidthEmm = 95,
emmPlotData = FALSE,
emmPlotError = "ci",
groupSumm = FALSE, ...) {
super$initialize(
package="jmv",
name="anovaRM",
requiresData=TRUE,
...)
private$..rm <- jmvcore::OptionArray$new(
"rm",
rm,
default=list(
list(label="RM Factor 1", levels=list(
"Level 1",
"Level 2"))),
template=jmvcore::OptionGroup$new(
"rm",
NULL,
elements=list(
jmvcore::OptionString$new(
"label",
NULL),
jmvcore::OptionArray$new(
"levels",
NULL,
template=jmvcore::OptionString$new(
"levels",
NULL)))))
private$..rmCells <- jmvcore::OptionArray$new(
"rmCells",
rmCells,
default=NULL,
template=jmvcore::OptionGroup$new(
"rmCells",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"measure",
NULL),
jmvcore::OptionArray$new(
"cell",
NULL,
template=jmvcore::OptionString$new(
"cell",
NULL)))))
private$..bs <- jmvcore::OptionVariables$new(
"bs",
bs,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"),
default=NULL)
private$..cov <- jmvcore::OptionVariables$new(
"cov",
cov,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..effectSize <- jmvcore::OptionNMXList$new(
"effectSize",
effectSize,
options=list(
"eta",
"partEta",
"ges",
"omega"),
default=NULL)
private$..depLabel <- jmvcore::OptionString$new(
"depLabel",
depLabel,
default="Dependent")
private$..rmTerms <- jmvcore::OptionTerms$new(
"rmTerms",
rmTerms,
default=NULL)
private$..bsTerms <- jmvcore::OptionTerms$new(
"bsTerms",
bsTerms,
default=NULL)
private$..ss <- jmvcore::OptionList$new(
"ss",
ss,
options=list(
"2",
"3"),
default="3")
private$..spherTests <- jmvcore::OptionBool$new(
"spherTests",
spherTests,
default=FALSE)
private$..spherCorr <- jmvcore::OptionNMXList$new(
"spherCorr",
spherCorr,
options=list(
"none",
"GG",
"HF"),
default=list(
"none"))
private$..leveneTest <- jmvcore::OptionBool$new(
"leveneTest",
leveneTest,
default=FALSE)
private$..qq <- jmvcore::OptionBool$new(
"qq",
qq,
default=FALSE)
private$..contrasts <- jmvcore::OptionArray$new(
"contrasts",
contrasts,
hidden=TRUE,
items="(bs)",
default=NULL,
template=jmvcore::OptionGroup$new(
"contrasts",
NULL,
elements=list(
jmvcore::OptionString$new(
"var",
NULL,
content="$key"),
jmvcore::OptionList$new(
"type",
NULL,
options=list(
"none",
"deviation",
"simple",
"difference",
"helmert",
"repeated",
"polynomial")))))
private$..postHoc <- jmvcore::OptionArray$new(
"postHoc",
postHoc,
template=jmvcore::OptionArray$new(
"postHoc",
NULL,
template=jmvcore::OptionString$new(
"postHoc",
NULL)),
default=NULL)
private$..postHocCorr <- jmvcore::OptionNMXList$new(
"postHocCorr",
postHocCorr,
options=list(
"none",
"tukey",
"scheffe",
"bonf",
"holm"),
default=list(
"tukey"))
private$..emMeans <- jmvcore::OptionArray$new(
"emMeans",
emMeans,
default=list(
list()),
template=jmvcore::OptionVariables$new(
"emMeans",
NULL))
private$..emmPlots <- jmvcore::OptionBool$new(
"emmPlots",
emmPlots,
default=TRUE)
private$..emmTables <- jmvcore::OptionBool$new(
"emmTables",
emmTables,
default=FALSE)
private$..emmWeights <- jmvcore::OptionBool$new(
"emmWeights",
emmWeights,
default=TRUE)
private$..ciWidthEmm <- jmvcore::OptionNumber$new(
"ciWidthEmm",
ciWidthEmm,
min=50,
max=99.9,
default=95)
private$..emmPlotData <- jmvcore::OptionBool$new(
"emmPlotData",
emmPlotData,
default=FALSE)
private$..emmPlotError <- jmvcore::OptionList$new(
"emmPlotError",
emmPlotError,
options=list(
"none",
"ci",
"se"),
default="ci")
private$..groupSumm <- jmvcore::OptionBool$new(
"groupSumm",
groupSumm,
default=FALSE)
self$.addOption(private$..rm)
self$.addOption(private$..rmCells)
self$.addOption(private$..bs)
self$.addOption(private$..cov)
self$.addOption(private$..effectSize)
self$.addOption(private$..depLabel)
self$.addOption(private$..rmTerms)
self$.addOption(private$..bsTerms)
self$.addOption(private$..ss)
self$.addOption(private$..spherTests)
self$.addOption(private$..spherCorr)
self$.addOption(private$..leveneTest)
self$.addOption(private$..qq)
self$.addOption(private$..contrasts)
self$.addOption(private$..postHoc)
self$.addOption(private$..postHocCorr)
self$.addOption(private$..emMeans)
self$.addOption(private$..emmPlots)
self$.addOption(private$..emmTables)
self$.addOption(private$..emmWeights)
self$.addOption(private$..ciWidthEmm)
self$.addOption(private$..emmPlotData)
self$.addOption(private$..emmPlotError)
self$.addOption(private$..groupSumm)
}),
active = list(
rm = function() private$..rm$value,
rmCells = function() private$..rmCells$value,
bs = function() private$..bs$value,
cov = function() private$..cov$value,
effectSize = function() private$..effectSize$value,
depLabel = function() private$..depLabel$value,
rmTerms = function() private$..rmTerms$value,
bsTerms = function() private$..bsTerms$value,
ss = function() private$..ss$value,
spherTests = function() private$..spherTests$value,
spherCorr = function() private$..spherCorr$value,
leveneTest = function() private$..leveneTest$value,
qq = function() private$..qq$value,
contrasts = function() private$..contrasts$value,
postHoc = function() private$..postHoc$value,
postHocCorr = function() private$..postHocCorr$value,
emMeans = function() private$..emMeans$value,
emmPlots = function() private$..emmPlots$value,
emmTables = function() private$..emmTables$value,
emmWeights = function() private$..emmWeights$value,
ciWidthEmm = function() private$..ciWidthEmm$value,
emmPlotData = function() private$..emmPlotData$value,
emmPlotError = function() private$..emmPlotError$value,
groupSumm = function() private$..groupSumm$value),
private = list(
..rm = NA,
..rmCells = NA,
..bs = NA,
..cov = NA,
..effectSize = NA,
..depLabel = NA,
..rmTerms = NA,
..bsTerms = NA,
..ss = NA,
..spherTests = NA,
..spherCorr = NA,
..leveneTest = NA,
..qq = NA,
..contrasts = NA,
..postHoc = NA,
..postHocCorr = NA,
..emMeans = NA,
..emmPlots = NA,
..emmTables = NA,
..emmWeights = NA,
..ciWidthEmm = NA,
..emmPlotData = NA,
..emmPlotError = NA,
..groupSumm = NA)
)
anovaRMResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaRMResults",
inherit = jmvcore::Group,
active = list(
rmTable = function() private$.items[["rmTable"]],
bsTable = function() private$.items[["bsTable"]],
assump = function() private$.items[["assump"]],
contrasts = function() private$.items[["contrasts"]],
postHoc = function() private$.items[["postHoc"]],
emm = function() private$.items[["emm"]],
groupSummary = function() private$.items[["groupSummary"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Repeated Measures ANOVA")
self$add(jmvcore::Table$new(
options=options,
name="rmTable",
title="Within Subjects Effects",
refs="afex",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"),
columns=list(
list(
`name`="name[none]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(spherCorr:none)"),
list(
`name`="correction[none]",
`title`="Sphericity Correction",
`type`="text",
`content`="None",
`visible`="(spherCorr:none && (spherCorr:GG || spherCorr:HF))"),
list(
`name`="ss[none]",
`title`="Sum of Squares",
`type`="number",
`visible`="(spherCorr:none)"),
list(
`name`="df[none]",
`title`="df",
`type`="integer",
`visible`="(spherCorr:none)"),
list(
`name`="ms[none]",
`title`="Mean Square",
`type`="number",
`visible`="(spherCorr:none)"),
list(
`name`="F[none]",
`title`="F",
`type`="number",
`visible`="(spherCorr:none)"),
list(
`name`="p[none]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(spherCorr:none)"),
list(
`name`="ges[none]",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges && spherCorr:none)"),
list(
`name`="eta[none]",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta && spherCorr:none)"),
list(
`name`="partEta[none]",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta && spherCorr:none)"),
list(
`name`="omega[none]",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega && spherCorr:none)"),
list(
`name`="name[GG]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(spherCorr:GG)"),
list(
`name`="correction[GG]",
`title`="Sphericity Correction",
`type`="text",
`content`="Greenhouse-Geisser",
`visible`="(spherCorr:GG)"),
list(
`name`="ss[GG]",
`title`="Sum of Squares",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="df[GG]",
`title`="df",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="ms[GG]",
`title`="Mean Square",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="F[GG]",
`title`="F",
`type`="number",
`visible`="(spherCorr:GG)"),
list(
`name`="p[GG]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(spherCorr:GG)"),
list(
`name`="ges[GG]",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges && spherCorr:GG)"),
list(
`name`="eta[GG]",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta && spherCorr:GG)"),
list(
`name`="partEta[GG]",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta && spherCorr:GG)"),
list(
`name`="omega[GG]",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega && spherCorr:GG)"),
list(
`name`="name[HF]",
`title`="",
`type`="text",
`combineBelow`=TRUE,
`visible`="(spherCorr:HF)"),
list(
`name`="correction[HF]",
`title`="Sphericity Correction",
`type`="text",
`content`="Huynh-Feldt",
`visible`="(spherCorr:HF)"),
list(
`name`="ss[HF]",
`title`="Sum of Squares",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="df[HF]",
`title`="df",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="ms[HF]",
`title`="Mean Square",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="F[HF]",
`title`="F",
`type`="number",
`visible`="(spherCorr:HF)"),
list(
`name`="p[HF]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(spherCorr:HF)"),
list(
`name`="ges[HF]",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges && spherCorr:HF)"),
list(
`name`="eta[HF]",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta && spherCorr:HF)"),
list(
`name`="partEta[HF]",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta && spherCorr:HF)"),
list(
`name`="omega[HF]",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega && spherCorr:HF)"))))
self$add(jmvcore::Table$new(
options=options,
name="bsTable",
title="Between Subjects Effects",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"),
columns=list(
list(
`name`="name",
`title`="",
`content`=".",
`type`="text"),
list(
`name`="ss",
`title`="Sum of Squares",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="ms",
`title`="Mean Square",
`type`="number"),
list(
`name`="F",
`title`="F",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="ges",
`title`="\u03B7\u00B2<sub>G</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:ges)"),
list(
`name`="eta",
`title`="\u03B7\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:eta)"),
list(
`name`="partEta",
`title`="\u03B7\u00B2<sub>p</sub>",
`type`="number",
`format`="zto",
`visible`="(effectSize:partEta)"),
list(
`name`="omega",
`title`="\u03C9\u00B2",
`type`="number",
`format`="zto",
`visible`="(effectSize:omega)"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
spherTable = function() private$.items[["spherTable"]],
leveneTable = function() private$.items[["leveneTable"]],
qq = function() private$.items[["qq"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumptions")
self$add(jmvcore::Table$new(
options=options,
name="spherTable",
title="Tests of Sphericity",
visible="(spherTests)",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text"),
list(
`name`="mauch",
`title`="Mauchly's W",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="gg",
`title`="Greenhouse-Geisser \u03B5",
`type`="number"),
list(
`name`="hf",
`title`="Huynh-Feldt \u03B5",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="leveneTable",
title="Homogeneity of Variances Test (Levene's)",
visible="(leveneTest)",
clearWith=list(
"bs",
"rmCells",
"cov"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text"),
list(
`name`="F",
`type`="number"),
list(
`name`="df1",
`type`="integer"),
list(
`name`="df2",
`type`="integer"),
list(
`name`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Image$new(
options=options,
name="qq",
title="Q-Q Plot",
visible="(qq)",
width=450,
height=400,
renderFun=".qqPlot",
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms")))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="contrasts",
title="Contrasts",
items=0,
visible="(contrasts)",
template=jmvcore::Table$new(
options=options,
title="Contrasts - $key",
rows=0,
columns=list(
list(
`name`="contrast",
`title`="",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")))))
self$add(jmvcore::Array$new(
options=options,
name="postHoc",
title="Post Hoc Tests",
items="(postHoc)",
refs="emmeans",
template=jmvcore::Table$new(
options=options,
title="",
columns=list(),
clearWith=list(
"dependent",
"ss",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms"))))
self$add(jmvcore::Array$new(
options=options,
name="emm",
title="Estimated Marginal Means",
refs="emmeans",
clearWith=list(
"emMeans"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
emmPlot = function() private$.items[["emmPlot"]],
emmTable = function() private$.items[["emmTable"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Image$new(
options=options,
name="emmPlot",
title="",
width=450,
height=400,
renderFun=".emmPlot",
visible="(emmPlots)",
clearWith=list(
"ciWidthEmm",
"emmWeights",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms",
"emmPlotData",
"emmPlotError",
"depLabel")))
self$add(jmvcore::Table$new(
options=options,
name="emmTable",
title="",
visible="(emmTables)",
columns=list(),
clearWith=list(
"ciWidthEmm",
"emmWeights",
"rmCells",
"rmcModelTerms",
"bscModelTerms",
"bs",
"rm",
"cov",
"rmTerms",
"bsTerms")))}))$new(options=options)))
self$add(jmvcore::Table$new(
options=options,
name="groupSummary",
title="Group Summary",
visible="(groupSumm)",
clearWith=list(
"dep",
"factors",
"cov"),
columns=list(
list(
`name`="n",
`title`="N",
`type`="integer"),
list(
`name`="ex",
`title`="Excluded",
`type`="integer"))))}))
anovaRMBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaRMBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "anovaRM",
version = c(2,0,0),
options = options,
results = anovaRMResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Repeated Measures ANOVA
#'
#' The Repeated Measures ANOVA is used to explore the relationship
#' between a continuous dependent variable and one or more categorical
#' explanatory variables, where one or more of the explanatory variables
#' are 'within subjects' (where multiple measurements are from the same
#' subject). Additionally, this analysis allows the inclusion of
#' covariates, allowing for repeated measures ANCOVAs as well.
#'
#' This analysis requires that the data be in 'wide format', where each
#' row represents a subject (as opposed to long format, where each
#' measurement of the dependent variable is represented as a row).
#'
#' A non-parametric equivalent of the repeated measures ANOVA also exists;
#' the Friedman test. However, it has the limitation of only being able to
#' test a single factor.
#'
#'
#' @examples
#' \donttest{
#'
#' data('bugs', package = 'jmv')
#'
#' anovaRM(
#' data = bugs,
#' rm = list(
#' list(
#' label = 'Frightening',
#' levels = c('Low', 'High'))),
#' rmCells = list(
#' list(
#' measure = 'LDLF',
#' cell = 'Low'),
#' list(
#' measure = 'LDHF',
#' cell = 'High')),
#' rmTerms = list(
#' 'Frightening'))
#'
#' #
#' # REPEATED MEASURES ANOVA
#' #
#' # Within Subjects Effects
#' # -----------------------------------------------------------------------
#' # Sum of Squares df Mean Square F p
#' # -----------------------------------------------------------------------
#' # Frightening 126 1 126.11 44.2 < .001
#' # Residual 257 90 2.85
#' # -----------------------------------------------------------------------
#' # Note. Type 3 Sums of Squares
#' #
#' #
#' #
#' # Between Subjects Effects
#' # -----------------------------------------------------------------
#' # Sum of Squares df Mean Square F p
#' # -----------------------------------------------------------------
#' # Residual 954 90 10.6
#' # -----------------------------------------------------------------
#' # Note. Type 3 Sums of Squares
#' #
#'}
#' @param data the data as a data frame
#' @param rm a list of lists, where each list describes the \code{label} (as a
#' string) and the \code{levels} (as vector of strings) of a particular
#' repeated measures factor
#' @param rmCells a list of lists, where each list decribes a repeated measure
#' (as a string) from \code{data} defined as \code{measure} and the particular
#' combination of levels from \code{rm} that it belongs to (as a vector of
#' strings) defined as \code{cell}
#' @param bs a vector of strings naming the between subjects factors from
#' \code{data}
#' @param cov a vector of strings naming the covariates from \code{data}.
#' Variables must be numeric
#' @param effectSize one or more of \code{'eta'}, \code{'partEta'}, or
#' \code{'omega'}; use eta², partial eta², and omega² effect sizes,
#' respectively
#' @param depLabel a string (default: 'Dependent') describing the label used
#' for the dependent variable throughout the analysis
#' @param rmTerms a list of character vectors describing the repeated measures
#' terms to go into the model
#' @param bsTerms a list of character vectors describing the between subjects
#' terms to go into the model
#' @param ss \code{'2'} or \code{'3'} (default), the sum of squares to use
#' @param spherTests \code{TRUE} or \code{FALSE} (default), perform sphericity
#' tests
#' @param spherCorr one or more of \code{'none'} (default), \code{'GG'}, or
#' \code{}HF\code{}; use no p-value correction, the Greenhouse-Geisser p-value
#' correction, and the Huynh-Feldt p-value correction for shericity,
#' respectively
#' @param leveneTest \code{TRUE} or \code{FALSE} (default), test for
#' homogeneity of variances (i.e., Levene's test)
#' @param qq \code{TRUE} or \code{FALSE} (default), provide a Q-Q plot of
#' residuals
#' @param contrasts in development
#' @param postHoc a list of character vectors describing the post-hoc tests
#' that need to be computed
#' @param postHocCorr one or more of \code{'none'}, \code{'tukey'} (default),
#' \code{'scheffe'}, \code{'bonf'}, or \code{'holm'}; use no, Tukey, Scheffe,
#' Bonferroni and Holm posthoc corrections, respectively
#' @param emMeans a list of lists specifying the variables for which the
#' estimated marginal means need to be calculate. Supports up to three
#' variables per term.
#' @param emmPlots \code{TRUE} (default) or \code{FALSE}, provide estimated
#' marginal means plots
#' @param emmTables \code{TRUE} or \code{FALSE} (default), provide estimated
#' marginal means tables
#' @param emmWeights \code{TRUE} (default) or \code{FALSE}, weigh each cell
#' equally or weigh them according to the cell frequency
#' @param ciWidthEmm a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the estimated marginal means
#' @param emmPlotData \code{TRUE} or \code{FALSE} (default), plot the data on
#' top of the marginal means
#' @param emmPlotError \code{'none'}, \code{'ci'} (default), or \code{'se'}.
#' Use no error bars, use confidence intervals, or use standard errors on the
#' marginal mean plots, respectively
#' @param groupSumm \code{TRUE} or \code{FALSE} (default), report a summary of
#' the different groups
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$rmTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$bsTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$spherTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$leveneTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$qq} \tab \tab \tab \tab \tab a q-q plot \cr
#' \code{results$contrasts} \tab \tab \tab \tab \tab an array of tables \cr
#' \code{results$postHoc} \tab \tab \tab \tab \tab an array of tables \cr
#' \code{results$emm} \tab \tab \tab \tab \tab an array of the estimated marginal means plots + tables \cr
#' \code{results$groupSummary} \tab \tab \tab \tab \tab a summary of the groups \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$rmTable$asDF}
#'
#' \code{as.data.frame(results$rmTable)}
#'
#' @export
anovaRM <- function(
data,
rm = list(
list(label="RM Factor 1", levels=list(
"Level 1",
"Level 2"))),
rmCells = NULL,
bs = NULL,
cov = NULL,
effectSize = NULL,
depLabel = "Dependent",
rmTerms = NULL,
bsTerms = NULL,
ss = "3",
spherTests = FALSE,
spherCorr = list(
"none"),
leveneTest = FALSE,
qq = FALSE,
contrasts = NULL,
postHoc = NULL,
postHocCorr = list(
"tukey"),
emMeans = list(
list()),
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE,
ciWidthEmm = 95,
emmPlotData = FALSE,
emmPlotError = "ci",
groupSumm = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("anovaRM requires jmvcore to be installed (restart may be required)")
if ( ! missing(bs)) bs <- jmvcore::resolveQuo(jmvcore::enquo(bs))
if ( ! missing(cov)) cov <- jmvcore::resolveQuo(jmvcore::enquo(cov))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(bs), bs, NULL),
`if`( ! missing(cov), cov, NULL))
for (v in bs) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(rmTerms, "formula")) rmTerms <- jmvcore::decomposeFormula(rmTerms)
if (inherits(bsTerms, "formula")) bsTerms <- jmvcore::decomposeFormula(bsTerms)
if (inherits(emMeans, "formula")) emMeans <- jmvcore::decomposeFormula(emMeans)
options <- anovaRMOptions$new(
rm = rm,
rmCells = rmCells,
bs = bs,
cov = cov,
effectSize = effectSize,
depLabel = depLabel,
rmTerms = rmTerms,
bsTerms = bsTerms,
ss = ss,
spherTests = spherTests,
spherCorr = spherCorr,
leveneTest = leveneTest,
qq = qq,
contrasts = contrasts,
postHoc = postHoc,
postHocCorr = postHocCorr,
emMeans = emMeans,
emmPlots = emmPlots,
emmTables = emmTables,
emmWeights = emmWeights,
ciWidthEmm = ciWidthEmm,
emmPlotData = emmPlotData,
emmPlotError = emmPlotError,
groupSumm = groupSumm)
analysis <- anovaRMClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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