R/anovaonew.h.R
In jamovi/jmv: The 'jamovi' Analyses
Defines functions
anovaOneW
# This file is automatically generated, you probably don't want to edit this
anovaOneWOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaOneWOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
deps = NULL,
group = NULL,
welchs = TRUE,
fishers = FALSE,
miss = "perAnalysis",
desc = FALSE,
descPlot = FALSE,
norm = FALSE,
qq = FALSE,
eqv = FALSE,
phMethod = "none",
phMeanDif = TRUE,
phSig = TRUE,
phTest = FALSE,
phFlag = FALSE, ...) {
super$initialize(
package="jmv",
name="anovaOneW",
requiresData=TRUE,
...)
private$..deps <- jmvcore::OptionVariables$new(
"deps",
deps,
required=TRUE,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..group <- jmvcore::OptionVariable$new(
"group",
group,
required=TRUE,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..welchs <- jmvcore::OptionBool$new(
"welchs",
welchs,
default=TRUE)
private$..fishers <- jmvcore::OptionBool$new(
"fishers",
fishers,
default=FALSE)
private$..miss <- jmvcore::OptionList$new(
"miss",
miss,
options=list(
"perAnalysis",
"listwise"),
default="perAnalysis")
private$..desc <- jmvcore::OptionBool$new(
"desc",
desc,
default=FALSE)
private$..descPlot <- jmvcore::OptionBool$new(
"descPlot",
descPlot,
default=FALSE)
private$..norm <- jmvcore::OptionBool$new(
"norm",
norm,
default=FALSE)
private$..qq <- jmvcore::OptionBool$new(
"qq",
qq,
default=FALSE)
private$..eqv <- jmvcore::OptionBool$new(
"eqv",
eqv,
default=FALSE)
private$..phMethod <- jmvcore::OptionList$new(
"phMethod",
phMethod,
options=list(
"none",
"gamesHowell",
"tukey"),
default="none")
private$..phMeanDif <- jmvcore::OptionBool$new(
"phMeanDif",
phMeanDif,
default=TRUE)
private$..phSig <- jmvcore::OptionBool$new(
"phSig",
phSig,
default=TRUE)
private$..phTest <- jmvcore::OptionBool$new(
"phTest",
phTest,
default=FALSE)
private$..phFlag <- jmvcore::OptionBool$new(
"phFlag",
phFlag,
default=FALSE)
self$.addOption(private$..deps)
self$.addOption(private$..group)
self$.addOption(private$..welchs)
self$.addOption(private$..fishers)
self$.addOption(private$..miss)
self$.addOption(private$..desc)
self$.addOption(private$..descPlot)
self$.addOption(private$..norm)
self$.addOption(private$..qq)
self$.addOption(private$..eqv)
self$.addOption(private$..phMethod)
self$.addOption(private$..phMeanDif)
self$.addOption(private$..phSig)
self$.addOption(private$..phTest)
self$.addOption(private$..phFlag)
}),
active = list(
deps = function() private$..deps$value,
group = function() private$..group$value,
welchs = function() private$..welchs$value,
fishers = function() private$..fishers$value,
miss = function() private$..miss$value,
desc = function() private$..desc$value,
descPlot = function() private$..descPlot$value,
norm = function() private$..norm$value,
qq = function() private$..qq$value,
eqv = function() private$..eqv$value,
phMethod = function() private$..phMethod$value,
phMeanDif = function() private$..phMeanDif$value,
phSig = function() private$..phSig$value,
phTest = function() private$..phTest$value,
phFlag = function() private$..phFlag$value),
private = list(
..deps = NA,
..group = NA,
..welchs = NA,
..fishers = NA,
..miss = NA,
..desc = NA,
..descPlot = NA,
..norm = NA,
..qq = NA,
..eqv = NA,
..phMethod = NA,
..phMeanDif = NA,
..phSig = NA,
..phTest = NA,
..phFlag = NA)
)
anovaOneWResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaOneWResults",
inherit = jmvcore::Group,
active = list(
anova = function() private$.items[["anova"]],
desc = function() private$.items[["desc"]],
assump = function() private$.items[["assump"]],
plots = function() private$.items[["plots"]],
postHoc = function() private$.items[["postHoc"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="One-Way ANOVA")
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="One-Way ANOVA",
rows="(deps)",
visible="(fishers || welchs)",
clearWith=list(
"group",
"miss"),
columns=list(
list(
`name`="dep",
`title`="",
`content`="($key)",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="test[welch]",
`title`="",
`content`="Welch's",
`type`="text",
`visible`="(fishers && welchs)"),
list(
`name`="test[fisher]",
`title`="",
`content`="Fisher's",
`type`="text",
`visible`="(fishers && welchs)"),
list(
`name`="F[welch]",
`title`="F",
`type`="number",
`visible`="(welchs)"),
list(
`name`="F[fisher]",
`title`="F",
`type`="number",
`visible`="(fishers)"),
list(
`name`="df1[welch]",
`title`="df1",
`type`="integer",
`visible`="(welchs)"),
list(
`name`="df1[fisher]",
`title`="df1",
`type`="integer",
`visible`="(fishers)"),
list(
`name`="df2[welch]",
`title`="df2",
`type`="number",
`visible`="(welchs)"),
list(
`name`="df2[fisher]",
`title`="df2",
`type`="integer",
`visible`="(fishers)"),
list(
`name`="p[welch]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(welchs)"),
list(
`name`="p[fisher]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(fishers)"))))
self$add(jmvcore::Table$new(
options=options,
name="desc",
title="Group Descriptives",
rows=0,
visible="(desc)",
clearWith=list(
"group",
"miss"),
columns=list(
list(
`name`="dep",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="group",
`title`="",
`type`="text"),
list(
`name`="num",
`title`="N",
`type`="integer"),
list(
`name`="mean",
`title`="Mean",
`type`="number"),
list(
`name`="sd",
`title`="SD",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
norm = function() private$.items[["norm"]],
eqv = function() private$.items[["eqv"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="norm",
title="Normality Test (Shapiro-Wilk)",
visible="(norm)",
rows="(deps)",
clearWith=list(
"miss",
"group"),
notes=list(
`p`="A low p-value suggests a violation of the assumption of normality"),
columns=list(
list(
`name`="name",
`title`="",
`content`="($key)",
`type`="text"),
list(
`name`="w",
`title`="W",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="eqv",
title="Homogeneity of Variances Test (Levene's)",
refs="car",
visible="(eqv)",
rows="(deps)",
columns=list(
list(
`name`="dep",
`title`="",
`content`="($key)",
`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"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="plots",
title="Plots",
items="(deps)",
clearWith=list(
"group",
"miss"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
desc = function() private$.items[["desc"]],
qq = function() private$.items[["qq"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="$key")
self$add(jmvcore::Image$new(
options=options,
name="desc",
height=350,
visible="(descPlot)",
renderFun=".desc",
clearWith=list()))
self$add(jmvcore::Image$new(
options=options,
name="qq",
width=350,
height=300,
requiresData=TRUE,
visible="(qq)",
renderFun=".qq",
clearWith=list()))}))$new(options=options)))
self$add(jmvcore::Array$new(
options=options,
name="postHoc",
title="Post Hoc Tests",
items="(deps)",
visible="(phMethod:gamesHowell || phMethod:tukey)",
template=jmvcore::Table$new(
options=options,
title="Post Hoc Tests \u2013 $key",
rows="(levels(group))",
clearWith=list(
"group",
"miss",
"phMethod",
"phFlag"),
columns=list(
list(
`name`=".name[md]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phMeanDif)"),
list(
`name`=".stat[md]",
`title`="",
`type`="text",
`content`="Mean difference",
`visible`="(phMeanDif)"),
list(
`name`=".name[t]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phTest)"),
list(
`name`=".stat[t]",
`title`="",
`type`="text",
`content`="t-value",
`visible`="(phTest)"),
list(
`name`=".name[df]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phTest)"),
list(
`name`=".stat[df]",
`title`="",
`type`="text",
`content`="df",
`visible`="(phTest)"),
list(
`name`=".name[p]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phSig)"),
list(
`name`=".stat[p]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(phSig)")))))}))
anovaOneWBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaOneWBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "anovaOneW",
version = c(1,0,0),
options = options,
results = anovaOneWResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' One-Way ANOVA
#'
#' The Analysis of Variance (ANOVA) is used to explore the relationship
#' between a continuous dependent variable, and one or more categorical
#' explanatory variables. This 'One-Way ANOVA' is a simplified version of
#' the 'normal' ANOVA, allowing only a single explanatory factor, however
#' also providing a Welch's ANOVA. The Welch's ANOVA has the advantage that
#' it need not assume that the variances of all groups are equal.
#'
#' For convenience, this method allows specifying multiple dependent
#' variables, resulting in multiple independent tests.
#'
#' Note that the Welch's ANOVA is the same procedure as the Welch's
#' independent samples t-test.
#'
#'
#' @examples
#' data('ToothGrowth')
#' dat <- ToothGrowth
#' dat$dose <- factor(dat$dose)
#'
#' anovaOneW(formula = len ~ dose, data = dat)
#'
#' #
#' # ONE-WAY ANOVA
#' #
#' # One-Way ANOVA (Welch's)
#' # ----------------------------------------
#' # F df1 df2 p
#' # ----------------------------------------
#' # len 68.4 2 37.7 < .001
#' # ----------------------------------------
#' #
#'
#' @param data the data as a data frame
#' @param deps a string naming the dependent variables in \code{data}
#' @param group a string naming the grouping or independent variable in
#' \code{data}
#' @param welchs \code{TRUE} (default) or \code{FALSE}, perform Welch's
#' one-way ANOVA which does not assume equal variances
#' @param fishers \code{TRUE} or \code{FALSE} (default), perform Fisher's
#' one-way ANOVA which assumes equal variances
#' @param miss \code{'perAnalysis'} or \code{'listwise'}, how to handle
#' missing values; \code{'perAnalysis'} excludes missing values for individual
#' dependent variables, \code{'listwise'} excludes a row from all analyses if
#' one of its entries is missing.
#' @param desc \code{TRUE} or \code{FALSE} (default), provide descriptive
#' statistics
#' @param descPlot \code{TRUE} or \code{FALSE} (default), provide descriptive
#' plots
#' @param norm \code{TRUE} or \code{FALSE} (default), perform Shapiro-Wilk
#' test of normality
#' @param qq \code{TRUE} or \code{FALSE} (default), provide a Q-Q plot of
#' residuals
#' @param eqv \code{TRUE} or \code{FALSE} (default), perform Levene's test for
#' homogeneity of variances
#' @param phMethod \code{'none'}, \code{'gamesHowell'} or \code{'tukey'},
#' which post-hoc tests to provide; \code{'none'} shows no post-hoc tests,
#' \code{'gamesHowell'} shows Games-Howell post-hoc tests where no equivalence
#' of variances is assumed, and \code{'tukey'} shows Tukey post-hoc tests
#' where equivalence of variances is assumed
#' @param phMeanDif \code{TRUE} (default) or \code{FALSE}, provide mean
#' differences for post-hoc tests
#' @param phSig \code{TRUE} (default) or \code{FALSE}, provide significance
#' levels for post-hoc tests
#' @param phTest \code{TRUE} or \code{FALSE} (default), provide test results
#' (t-value and degrees of freedom) for post-hoc tests
#' @param phFlag \code{TRUE} or \code{FALSE} (default), flag significant
#' post-hoc comparisons
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$anova} \tab \tab \tab \tab \tab a table of the test results \cr
#' \code{results$desc} \tab \tab \tab \tab \tab a table containing the group descriptives \cr
#' \code{results$assump$norm} \tab \tab \tab \tab \tab a table containing the normality tests \cr
#' \code{results$assump$eqv} \tab \tab \tab \tab \tab a table of homogeneity of variances tests \cr
#' \code{results$plots} \tab \tab \tab \tab \tab an array of groups of plots \cr
#' \code{results$postHoc} \tab \tab \tab \tab \tab an array of post-hoc tables \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$anova$asDF}
#'
#' \code{as.data.frame(results$anova)}
#'
#' @export
anovaOneW <- function(
data,
deps,
group,
welchs = TRUE,
fishers = FALSE,
miss = "perAnalysis",
desc = FALSE,
descPlot = FALSE,
norm = FALSE,
qq = FALSE,
eqv = FALSE,
phMethod = "none",
phMeanDif = TRUE,
phSig = TRUE,
phTest = FALSE,
phFlag = FALSE,
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("anovaOneW requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(deps))
deps <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="lhs",
required=TRUE)
if (missing(group))
group <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs")
}
if ( ! missing(deps)) deps <- jmvcore::resolveQuo(jmvcore::enquo(deps))
if ( ! missing(group)) group <- jmvcore::resolveQuo(jmvcore::enquo(group))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(deps), deps, NULL),
`if`( ! missing(group), group, NULL))
for (v in group) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- anovaOneWOptions$new(
deps = deps,
group = group,
welchs = welchs,
fishers = fishers,
miss = miss,
desc = desc,
descPlot = descPlot,
norm = norm,
qq = qq,
eqv = eqv,
phMethod = phMethod,
phMeanDif = phMeanDif,
phSig = phSig,
phTest = phTest,
phFlag = phFlag)
analysis <- anovaOneWClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
jamovi/jmv documentation built on Jan. 17, 2025, 10:31 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions anovaOneW
# This file is automatically generated, you probably don't want to edit this
anovaOneWOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaOneWOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
deps = NULL,
group = NULL,
welchs = TRUE,
fishers = FALSE,
miss = "perAnalysis",
desc = FALSE,
descPlot = FALSE,
norm = FALSE,
qq = FALSE,
eqv = FALSE,
phMethod = "none",
phMeanDif = TRUE,
phSig = TRUE,
phTest = FALSE,
phFlag = FALSE, ...) {
super$initialize(
package="jmv",
name="anovaOneW",
requiresData=TRUE,
...)
private$..deps <- jmvcore::OptionVariables$new(
"deps",
deps,
required=TRUE,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..group <- jmvcore::OptionVariable$new(
"group",
group,
required=TRUE,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..welchs <- jmvcore::OptionBool$new(
"welchs",
welchs,
default=TRUE)
private$..fishers <- jmvcore::OptionBool$new(
"fishers",
fishers,
default=FALSE)
private$..miss <- jmvcore::OptionList$new(
"miss",
miss,
options=list(
"perAnalysis",
"listwise"),
default="perAnalysis")
private$..desc <- jmvcore::OptionBool$new(
"desc",
desc,
default=FALSE)
private$..descPlot <- jmvcore::OptionBool$new(
"descPlot",
descPlot,
default=FALSE)
private$..norm <- jmvcore::OptionBool$new(
"norm",
norm,
default=FALSE)
private$..qq <- jmvcore::OptionBool$new(
"qq",
qq,
default=FALSE)
private$..eqv <- jmvcore::OptionBool$new(
"eqv",
eqv,
default=FALSE)
private$..phMethod <- jmvcore::OptionList$new(
"phMethod",
phMethod,
options=list(
"none",
"gamesHowell",
"tukey"),
default="none")
private$..phMeanDif <- jmvcore::OptionBool$new(
"phMeanDif",
phMeanDif,
default=TRUE)
private$..phSig <- jmvcore::OptionBool$new(
"phSig",
phSig,
default=TRUE)
private$..phTest <- jmvcore::OptionBool$new(
"phTest",
phTest,
default=FALSE)
private$..phFlag <- jmvcore::OptionBool$new(
"phFlag",
phFlag,
default=FALSE)
self$.addOption(private$..deps)
self$.addOption(private$..group)
self$.addOption(private$..welchs)
self$.addOption(private$..fishers)
self$.addOption(private$..miss)
self$.addOption(private$..desc)
self$.addOption(private$..descPlot)
self$.addOption(private$..norm)
self$.addOption(private$..qq)
self$.addOption(private$..eqv)
self$.addOption(private$..phMethod)
self$.addOption(private$..phMeanDif)
self$.addOption(private$..phSig)
self$.addOption(private$..phTest)
self$.addOption(private$..phFlag)
}),
active = list(
deps = function() private$..deps$value,
group = function() private$..group$value,
welchs = function() private$..welchs$value,
fishers = function() private$..fishers$value,
miss = function() private$..miss$value,
desc = function() private$..desc$value,
descPlot = function() private$..descPlot$value,
norm = function() private$..norm$value,
qq = function() private$..qq$value,
eqv = function() private$..eqv$value,
phMethod = function() private$..phMethod$value,
phMeanDif = function() private$..phMeanDif$value,
phSig = function() private$..phSig$value,
phTest = function() private$..phTest$value,
phFlag = function() private$..phFlag$value),
private = list(
..deps = NA,
..group = NA,
..welchs = NA,
..fishers = NA,
..miss = NA,
..desc = NA,
..descPlot = NA,
..norm = NA,
..qq = NA,
..eqv = NA,
..phMethod = NA,
..phMeanDif = NA,
..phSig = NA,
..phTest = NA,
..phFlag = NA)
)
anovaOneWResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaOneWResults",
inherit = jmvcore::Group,
active = list(
anova = function() private$.items[["anova"]],
desc = function() private$.items[["desc"]],
assump = function() private$.items[["assump"]],
plots = function() private$.items[["plots"]],
postHoc = function() private$.items[["postHoc"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="One-Way ANOVA")
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="One-Way ANOVA",
rows="(deps)",
visible="(fishers || welchs)",
clearWith=list(
"group",
"miss"),
columns=list(
list(
`name`="dep",
`title`="",
`content`="($key)",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="test[welch]",
`title`="",
`content`="Welch's",
`type`="text",
`visible`="(fishers && welchs)"),
list(
`name`="test[fisher]",
`title`="",
`content`="Fisher's",
`type`="text",
`visible`="(fishers && welchs)"),
list(
`name`="F[welch]",
`title`="F",
`type`="number",
`visible`="(welchs)"),
list(
`name`="F[fisher]",
`title`="F",
`type`="number",
`visible`="(fishers)"),
list(
`name`="df1[welch]",
`title`="df1",
`type`="integer",
`visible`="(welchs)"),
list(
`name`="df1[fisher]",
`title`="df1",
`type`="integer",
`visible`="(fishers)"),
list(
`name`="df2[welch]",
`title`="df2",
`type`="number",
`visible`="(welchs)"),
list(
`name`="df2[fisher]",
`title`="df2",
`type`="integer",
`visible`="(fishers)"),
list(
`name`="p[welch]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(welchs)"),
list(
`name`="p[fisher]",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(fishers)"))))
self$add(jmvcore::Table$new(
options=options,
name="desc",
title="Group Descriptives",
rows=0,
visible="(desc)",
clearWith=list(
"group",
"miss"),
columns=list(
list(
`name`="dep",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="group",
`title`="",
`type`="text"),
list(
`name`="num",
`title`="N",
`type`="integer"),
list(
`name`="mean",
`title`="Mean",
`type`="number"),
list(
`name`="sd",
`title`="SD",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
norm = function() private$.items[["norm"]],
eqv = function() private$.items[["eqv"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="norm",
title="Normality Test (Shapiro-Wilk)",
visible="(norm)",
rows="(deps)",
clearWith=list(
"miss",
"group"),
notes=list(
`p`="A low p-value suggests a violation of the assumption of normality"),
columns=list(
list(
`name`="name",
`title`="",
`content`="($key)",
`type`="text"),
list(
`name`="w",
`title`="W",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="eqv",
title="Homogeneity of Variances Test (Levene's)",
refs="car",
visible="(eqv)",
rows="(deps)",
columns=list(
list(
`name`="dep",
`title`="",
`content`="($key)",
`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"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="plots",
title="Plots",
items="(deps)",
clearWith=list(
"group",
"miss"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
desc = function() private$.items[["desc"]],
qq = function() private$.items[["qq"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="$key")
self$add(jmvcore::Image$new(
options=options,
name="desc",
height=350,
visible="(descPlot)",
renderFun=".desc",
clearWith=list()))
self$add(jmvcore::Image$new(
options=options,
name="qq",
width=350,
height=300,
requiresData=TRUE,
visible="(qq)",
renderFun=".qq",
clearWith=list()))}))$new(options=options)))
self$add(jmvcore::Array$new(
options=options,
name="postHoc",
title="Post Hoc Tests",
items="(deps)",
visible="(phMethod:gamesHowell || phMethod:tukey)",
template=jmvcore::Table$new(
options=options,
title="Post Hoc Tests \u2013 $key",
rows="(levels(group))",
clearWith=list(
"group",
"miss",
"phMethod",
"phFlag"),
columns=list(
list(
`name`=".name[md]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phMeanDif)"),
list(
`name`=".stat[md]",
`title`="",
`type`="text",
`content`="Mean difference",
`visible`="(phMeanDif)"),
list(
`name`=".name[t]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phTest)"),
list(
`name`=".stat[t]",
`title`="",
`type`="text",
`content`="t-value",
`visible`="(phTest)"),
list(
`name`=".name[df]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phTest)"),
list(
`name`=".stat[df]",
`title`="",
`type`="text",
`content`="df",
`visible`="(phTest)"),
list(
`name`=".name[p]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(phSig)"),
list(
`name`=".stat[p]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(phSig)")))))}))
anovaOneWBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"anovaOneWBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "anovaOneW",
version = c(1,0,0),
options = options,
results = anovaOneWResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' One-Way ANOVA
#'
#' The Analysis of Variance (ANOVA) is used to explore the relationship
#' between a continuous dependent variable, and one or more categorical
#' explanatory variables. This 'One-Way ANOVA' is a simplified version of
#' the 'normal' ANOVA, allowing only a single explanatory factor, however
#' also providing a Welch's ANOVA. The Welch's ANOVA has the advantage that
#' it need not assume that the variances of all groups are equal.
#'
#' For convenience, this method allows specifying multiple dependent
#' variables, resulting in multiple independent tests.
#'
#' Note that the Welch's ANOVA is the same procedure as the Welch's
#' independent samples t-test.
#'
#'
#' @examples
#' data('ToothGrowth')
#' dat <- ToothGrowth
#' dat$dose <- factor(dat$dose)
#'
#' anovaOneW(formula = len ~ dose, data = dat)
#'
#' #
#' # ONE-WAY ANOVA
#' #
#' # One-Way ANOVA (Welch's)
#' # ----------------------------------------
#' # F df1 df2 p
#' # ----------------------------------------
#' # len 68.4 2 37.7 < .001
#' # ----------------------------------------
#' #
#'
#' @param data the data as a data frame
#' @param deps a string naming the dependent variables in \code{data}
#' @param group a string naming the grouping or independent variable in
#' \code{data}
#' @param welchs \code{TRUE} (default) or \code{FALSE}, perform Welch's
#' one-way ANOVA which does not assume equal variances
#' @param fishers \code{TRUE} or \code{FALSE} (default), perform Fisher's
#' one-way ANOVA which assumes equal variances
#' @param miss \code{'perAnalysis'} or \code{'listwise'}, how to handle
#' missing values; \code{'perAnalysis'} excludes missing values for individual
#' dependent variables, \code{'listwise'} excludes a row from all analyses if
#' one of its entries is missing.
#' @param desc \code{TRUE} or \code{FALSE} (default), provide descriptive
#' statistics
#' @param descPlot \code{TRUE} or \code{FALSE} (default), provide descriptive
#' plots
#' @param norm \code{TRUE} or \code{FALSE} (default), perform Shapiro-Wilk
#' test of normality
#' @param qq \code{TRUE} or \code{FALSE} (default), provide a Q-Q plot of
#' residuals
#' @param eqv \code{TRUE} or \code{FALSE} (default), perform Levene's test for
#' homogeneity of variances
#' @param phMethod \code{'none'}, \code{'gamesHowell'} or \code{'tukey'},
#' which post-hoc tests to provide; \code{'none'} shows no post-hoc tests,
#' \code{'gamesHowell'} shows Games-Howell post-hoc tests where no equivalence
#' of variances is assumed, and \code{'tukey'} shows Tukey post-hoc tests
#' where equivalence of variances is assumed
#' @param phMeanDif \code{TRUE} (default) or \code{FALSE}, provide mean
#' differences for post-hoc tests
#' @param phSig \code{TRUE} (default) or \code{FALSE}, provide significance
#' levels for post-hoc tests
#' @param phTest \code{TRUE} or \code{FALSE} (default), provide test results
#' (t-value and degrees of freedom) for post-hoc tests
#' @param phFlag \code{TRUE} or \code{FALSE} (default), flag significant
#' post-hoc comparisons
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$anova} \tab \tab \tab \tab \tab a table of the test results \cr
#' \code{results$desc} \tab \tab \tab \tab \tab a table containing the group descriptives \cr
#' \code{results$assump$norm} \tab \tab \tab \tab \tab a table containing the normality tests \cr
#' \code{results$assump$eqv} \tab \tab \tab \tab \tab a table of homogeneity of variances tests \cr
#' \code{results$plots} \tab \tab \tab \tab \tab an array of groups of plots \cr
#' \code{results$postHoc} \tab \tab \tab \tab \tab an array of post-hoc tables \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$anova$asDF}
#'
#' \code{as.data.frame(results$anova)}
#'
#' @export
anovaOneW <- function(
data,
deps,
group,
welchs = TRUE,
fishers = FALSE,
miss = "perAnalysis",
desc = FALSE,
descPlot = FALSE,
norm = FALSE,
qq = FALSE,
eqv = FALSE,
phMethod = "none",
phMeanDif = TRUE,
phSig = TRUE,
phTest = FALSE,
phFlag = FALSE,
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("anovaOneW requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(deps))
deps <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="lhs",
required=TRUE)
if (missing(group))
group <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs")
}
if ( ! missing(deps)) deps <- jmvcore::resolveQuo(jmvcore::enquo(deps))
if ( ! missing(group)) group <- jmvcore::resolveQuo(jmvcore::enquo(group))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(deps), deps, NULL),
`if`( ! missing(group), group, NULL))
for (v in group) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- anovaOneWOptions$new(
deps = deps,
group = group,
welchs = welchs,
fishers = fishers,
miss = miss,
desc = desc,
descPlot = descPlot,
norm = norm,
qq = qq,
eqv = eqv,
phMethod = phMethod,
phMeanDif = phMeanDif,
phSig = phSig,
phTest = phTest,
phFlag = phFlag)
analysis <- anovaOneWClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.