Nothing
R/pca.h.R
In jmv: The 'jamovi' Analyses
Defines functions
pca
Documented in
pca
# This file is automatically generated, you probably don't want to edit this
pcaOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"pcaOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
nFactorMethod = "parallel",
nFactors = 1,
minEigen = 1,
rotation = "varimax",
hideLoadings = 0.3,
sortLoadings = FALSE,
screePlot = FALSE,
eigen = FALSE,
factorCor = FALSE,
factorSummary = FALSE,
kmo = FALSE,
bartlett = FALSE, ...) {
super$initialize(
package="jmv",
name="pca",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
takeFromDataIfMissing=TRUE,
suggested=list(
"ordinal",
"continuous"),
permitted=list(
"numeric"),
rejectInf=FALSE)
private$..nFactorMethod <- jmvcore::OptionList$new(
"nFactorMethod",
nFactorMethod,
options=list(
"parallel",
"eigen",
"fixed"),
default="parallel")
private$..nFactors <- jmvcore::OptionInteger$new(
"nFactors",
nFactors,
min=1,
default=1)
private$..minEigen <- jmvcore::OptionNumber$new(
"minEigen",
minEigen,
default=1)
private$..rotation <- jmvcore::OptionList$new(
"rotation",
rotation,
options=list(
"none",
"varimax",
"quartimax",
"promax",
"oblimin",
"simplimax"),
default="varimax")
private$..hideLoadings <- jmvcore::OptionNumber$new(
"hideLoadings",
hideLoadings,
default=0.3)
private$..sortLoadings <- jmvcore::OptionBool$new(
"sortLoadings",
sortLoadings,
default=FALSE)
private$..screePlot <- jmvcore::OptionBool$new(
"screePlot",
screePlot,
default=FALSE)
private$..eigen <- jmvcore::OptionBool$new(
"eigen",
eigen,
default=FALSE)
private$..factorCor <- jmvcore::OptionBool$new(
"factorCor",
factorCor,
default=FALSE)
private$..factorSummary <- jmvcore::OptionBool$new(
"factorSummary",
factorSummary,
default=FALSE)
private$..kmo <- jmvcore::OptionBool$new(
"kmo",
kmo,
default=FALSE)
private$..bartlett <- jmvcore::OptionBool$new(
"bartlett",
bartlett,
default=FALSE)
private$..factorScoresOV <- jmvcore::OptionOutput$new(
"factorScoresOV")
self$.addOption(private$..vars)
self$.addOption(private$..nFactorMethod)
self$.addOption(private$..nFactors)
self$.addOption(private$..minEigen)
self$.addOption(private$..rotation)
self$.addOption(private$..hideLoadings)
self$.addOption(private$..sortLoadings)
self$.addOption(private$..screePlot)
self$.addOption(private$..eigen)
self$.addOption(private$..factorCor)
self$.addOption(private$..factorSummary)
self$.addOption(private$..kmo)
self$.addOption(private$..bartlett)
self$.addOption(private$..factorScoresOV)
}),
active = list(
vars = function() private$..vars$value,
nFactorMethod = function() private$..nFactorMethod$value,
nFactors = function() private$..nFactors$value,
minEigen = function() private$..minEigen$value,
rotation = function() private$..rotation$value,
hideLoadings = function() private$..hideLoadings$value,
sortLoadings = function() private$..sortLoadings$value,
screePlot = function() private$..screePlot$value,
eigen = function() private$..eigen$value,
factorCor = function() private$..factorCor$value,
factorSummary = function() private$..factorSummary$value,
kmo = function() private$..kmo$value,
bartlett = function() private$..bartlett$value,
factorScoresOV = function() private$..factorScoresOV$value),
private = list(
..vars = NA,
..nFactorMethod = NA,
..nFactors = NA,
..minEigen = NA,
..rotation = NA,
..hideLoadings = NA,
..sortLoadings = NA,
..screePlot = NA,
..eigen = NA,
..factorCor = NA,
..factorSummary = NA,
..kmo = NA,
..bartlett = NA,
..factorScoresOV = NA)
)
pcaResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"pcaResults",
inherit = jmvcore::Group,
active = list(
loadings = function() private$.items[["loadings"]],
factorStats = function() private$.items[["factorStats"]],
modelFit = function() private$.items[["modelFit"]],
assump = function() private$.items[["assump"]],
eigen = function() private$.items[["eigen"]],
factorScoresOV = function() private$.items[["factorScoresOV"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Principal Component Analysis")
self$add(jmvcore::Table$new(
options=options,
name="loadings",
title="Component Loadings",
rows="(vars)",
refs="psych",
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"hideLoadings",
"rotation"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text",
`content`="($key)"),
list(
`name`="pc1",
`title`="1",
`type`="number",
`superTitle`="Component"),
list(
`name`="uniq",
`title`="Uniqueness",
`type`="number"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
factorSummary = function() private$.items[["factorSummary"]],
factorCor = function() private$.items[["factorCor"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="factorStats",
title="Component Statistics")
self$add(jmvcore::Table$new(
options=options,
name="factorSummary",
title="Summary",
visible="(factorSummary)",
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"rotation"),
columns=list(
list(
`name`="comp",
`title`="Component",
`type`="text"),
list(
`name`="loadings",
`title`="SS Loadings",
`type`="number"),
list(
`name`="varProp",
`title`="% of Variance",
`type`="number"),
list(
`name`="varCum",
`title`="Cumulative %",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="factorCor",
title="Inter-Component Correlations",
visible="(factorCor)",
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"hideLoadings",
"rotation"),
columns=list(
list(
`name`="comp",
`title`="",
`type`="text",
`format`="narrow"),
list(
`name`="pc1",
`title`="1",
`type`="number"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
fit = function() private$.items[["fit"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modelFit",
title="Model Fit")
self$add(jmvcore::Table$new(
options=options,
name="fit",
title="Model Fit Measures",
rows=1,
visible=FALSE,
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"rotation"),
columns=list(
list(
`name`="rmsea",
`title`="RMSEA",
`type`="number"),
list(
`name`="rmseaLower",
`title`="Lower",
`type`="number",
`superTitle`="RMSEA 90% CI"),
list(
`name`="rmseaUpper",
`title`="Upper",
`type`="number",
`superTitle`="RMSEA 90% CI"),
list(
`name`="tli",
`title`="TLI",
`type`="number"),
list(
`name`="bic",
`title`="BIC",
`type`="number"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number",
`superTitle`="Model Test"),
list(
`name`="df",
`title`="df",
`type`="integer",
`superTitle`="Model Test"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`superTitle`="Model Test"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
bartlett = function() private$.items[["bartlett"]],
kmo = function() private$.items[["kmo"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="bartlett",
title="Bartlett's Test of Sphericity",
visible="(bartlett)",
rows=1,
clearWith=list(
"vars"),
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="kmo",
title="KMO Measure of Sampling Adequacy",
visible="(kmo)",
clearWith=list(
"vars"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text"),
list(
`name`="msa",
`title`="MSA",
`type`="number",
`format`="zto"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
initEigen = function() private$.items[["initEigen"]],
screePlot = function() private$.items[["screePlot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="eigen",
title="Eigenvalues")
self$add(jmvcore::Table$new(
options=options,
name="initEigen",
title="Initial Eigenvalues",
visible="(eigen)",
clearWith=list(
"vars"),
columns=list(
list(
`name`="comp",
`title`="Component",
`type`="text"),
list(
`name`="eigen",
`title`="Eigenvalue",
`type`="number"),
list(
`name`="varProp",
`title`="% of Variance",
`type`="number"),
list(
`name`="varCum",
`title`="Cumulative %",
`type`="number"))))
self$add(jmvcore::Image$new(
options=options,
name="screePlot",
title="Scree Plot",
visible="(screePlot)",
width=500,
height=300,
renderFun=".screePlot",
clearWith=list(
"vars",
"screePlot",
"nFactorMethod",
"minEigen")))}))$new(options=options))
self$add(jmvcore::Output$new(
options=options,
name="factorScoresOV",
title="Factor scores",
initInRun=TRUE,
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"rotation",
"factorScoreMethod")))}))
pcaBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"pcaBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "pca",
version = c(1,0,0),
options = options,
results = pcaResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'none')
}))
#' Principal Component Analysis
#'
#' Principal Component Analysis
#'
#' @examples
#' data('iris')
#'
#' pca(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))
#'
#' #
#' # PRINCIPAL COMPONENT ANALYSIS
#' #
#' # Component Loadings
#' # ----------------------------------------
#' # 1 Uniqueness
#' # ----------------------------------------
#' # Sepal.Length 0.890 0.2076
#' # Sepal.Width -0.460 0.7883
#' # Petal.Length 0.992 0.0168
#' # Petal.Width 0.965 0.0688
#' # ----------------------------------------
#' # Note. 'varimax' rotation was used
#' #
#'
#' @param data the data as a data frame
#' @param vars a vector of strings naming the variables of interest in
#' \code{data}
#' @param nFactorMethod \code{'parallel'} (default), \code{'eigen'} or
#' \code{'fixed'}, the way to determine the number of factors
#' @param nFactors an integer (default: 1), the number of components in the
#' model
#' @param minEigen a number (default: 1), the minimal eigenvalue for a
#' component to be included in the model
#' @param rotation \code{'none'}, \code{'varimax'} (default),
#' \code{'quartimax'}, \code{'promax'}, \code{'oblimin'}, or
#' \code{'simplimax'}, the rotation to use in estimation
#' @param hideLoadings a number (default: 0.3), hide loadings below this value
#' @param sortLoadings \code{TRUE} or \code{FALSE} (default), sort the factor
#' loadings by size
#' @param screePlot \code{TRUE} or \code{FALSE} (default), show scree plot
#' @param eigen \code{TRUE} or \code{FALSE} (default), show eigenvalue table
#' @param factorCor \code{TRUE} or \code{FALSE} (default), show inter-factor
#' correlations
#' @param factorSummary \code{TRUE} or \code{FALSE} (default), show factor
#' summary
#' @param kmo \code{TRUE} or \code{FALSE} (default), show Kaiser-Meyer-Olkin
#' (KMO) measure of sampling adequacy (MSA) results
#' @param bartlett \code{TRUE} or \code{FALSE} (default), show Bartlett's test
#' of sphericity results
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$loadings} \tab \tab \tab \tab \tab a table \cr
#' \code{results$factorStats$factorSummary} \tab \tab \tab \tab \tab a table \cr
#' \code{results$factorStats$factorCor} \tab \tab \tab \tab \tab a table \cr
#' \code{results$modelFit$fit} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$bartlett} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$kmo} \tab \tab \tab \tab \tab a table \cr
#' \code{results$eigen$initEigen} \tab \tab \tab \tab \tab a table \cr
#' \code{results$eigen$screePlot} \tab \tab \tab \tab \tab an image \cr
#' \code{results$factorScoresOV} \tab \tab \tab \tab \tab an output \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$loadings$asDF}
#'
#' \code{as.data.frame(results$loadings)}
#'
#' @export
pca <- function(
data,
vars,
nFactorMethod = "parallel",
nFactors = 1,
minEigen = 1,
rotation = "varimax",
hideLoadings = 0.3,
sortLoadings = FALSE,
screePlot = FALSE,
eigen = FALSE,
factorCor = FALSE,
factorSummary = FALSE,
kmo = FALSE,
bartlett = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("pca requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL))
vars <- `if`( ! missing(vars), vars, colnames(data))
options <- pcaOptions$new(
vars = vars,
nFactorMethod = nFactorMethod,
nFactors = nFactors,
minEigen = minEigen,
rotation = rotation,
hideLoadings = hideLoadings,
sortLoadings = sortLoadings,
screePlot = screePlot,
eigen = eigen,
factorCor = factorCor,
factorSummary = factorSummary,
kmo = kmo,
bartlett = bartlett)
analysis <- pcaClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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Defines functions pca
Documented in pca
# This file is automatically generated, you probably don't want to edit this
pcaOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"pcaOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
nFactorMethod = "parallel",
nFactors = 1,
minEigen = 1,
rotation = "varimax",
hideLoadings = 0.3,
sortLoadings = FALSE,
screePlot = FALSE,
eigen = FALSE,
factorCor = FALSE,
factorSummary = FALSE,
kmo = FALSE,
bartlett = FALSE, ...) {
super$initialize(
package="jmv",
name="pca",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
takeFromDataIfMissing=TRUE,
suggested=list(
"ordinal",
"continuous"),
permitted=list(
"numeric"),
rejectInf=FALSE)
private$..nFactorMethod <- jmvcore::OptionList$new(
"nFactorMethod",
nFactorMethod,
options=list(
"parallel",
"eigen",
"fixed"),
default="parallel")
private$..nFactors <- jmvcore::OptionInteger$new(
"nFactors",
nFactors,
min=1,
default=1)
private$..minEigen <- jmvcore::OptionNumber$new(
"minEigen",
minEigen,
default=1)
private$..rotation <- jmvcore::OptionList$new(
"rotation",
rotation,
options=list(
"none",
"varimax",
"quartimax",
"promax",
"oblimin",
"simplimax"),
default="varimax")
private$..hideLoadings <- jmvcore::OptionNumber$new(
"hideLoadings",
hideLoadings,
default=0.3)
private$..sortLoadings <- jmvcore::OptionBool$new(
"sortLoadings",
sortLoadings,
default=FALSE)
private$..screePlot <- jmvcore::OptionBool$new(
"screePlot",
screePlot,
default=FALSE)
private$..eigen <- jmvcore::OptionBool$new(
"eigen",
eigen,
default=FALSE)
private$..factorCor <- jmvcore::OptionBool$new(
"factorCor",
factorCor,
default=FALSE)
private$..factorSummary <- jmvcore::OptionBool$new(
"factorSummary",
factorSummary,
default=FALSE)
private$..kmo <- jmvcore::OptionBool$new(
"kmo",
kmo,
default=FALSE)
private$..bartlett <- jmvcore::OptionBool$new(
"bartlett",
bartlett,
default=FALSE)
private$..factorScoresOV <- jmvcore::OptionOutput$new(
"factorScoresOV")
self$.addOption(private$..vars)
self$.addOption(private$..nFactorMethod)
self$.addOption(private$..nFactors)
self$.addOption(private$..minEigen)
self$.addOption(private$..rotation)
self$.addOption(private$..hideLoadings)
self$.addOption(private$..sortLoadings)
self$.addOption(private$..screePlot)
self$.addOption(private$..eigen)
self$.addOption(private$..factorCor)
self$.addOption(private$..factorSummary)
self$.addOption(private$..kmo)
self$.addOption(private$..bartlett)
self$.addOption(private$..factorScoresOV)
}),
active = list(
vars = function() private$..vars$value,
nFactorMethod = function() private$..nFactorMethod$value,
nFactors = function() private$..nFactors$value,
minEigen = function() private$..minEigen$value,
rotation = function() private$..rotation$value,
hideLoadings = function() private$..hideLoadings$value,
sortLoadings = function() private$..sortLoadings$value,
screePlot = function() private$..screePlot$value,
eigen = function() private$..eigen$value,
factorCor = function() private$..factorCor$value,
factorSummary = function() private$..factorSummary$value,
kmo = function() private$..kmo$value,
bartlett = function() private$..bartlett$value,
factorScoresOV = function() private$..factorScoresOV$value),
private = list(
..vars = NA,
..nFactorMethod = NA,
..nFactors = NA,
..minEigen = NA,
..rotation = NA,
..hideLoadings = NA,
..sortLoadings = NA,
..screePlot = NA,
..eigen = NA,
..factorCor = NA,
..factorSummary = NA,
..kmo = NA,
..bartlett = NA,
..factorScoresOV = NA)
)
pcaResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"pcaResults",
inherit = jmvcore::Group,
active = list(
loadings = function() private$.items[["loadings"]],
factorStats = function() private$.items[["factorStats"]],
modelFit = function() private$.items[["modelFit"]],
assump = function() private$.items[["assump"]],
eigen = function() private$.items[["eigen"]],
factorScoresOV = function() private$.items[["factorScoresOV"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Principal Component Analysis")
self$add(jmvcore::Table$new(
options=options,
name="loadings",
title="Component Loadings",
rows="(vars)",
refs="psych",
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"hideLoadings",
"rotation"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text",
`content`="($key)"),
list(
`name`="pc1",
`title`="1",
`type`="number",
`superTitle`="Component"),
list(
`name`="uniq",
`title`="Uniqueness",
`type`="number"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
factorSummary = function() private$.items[["factorSummary"]],
factorCor = function() private$.items[["factorCor"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="factorStats",
title="Component Statistics")
self$add(jmvcore::Table$new(
options=options,
name="factorSummary",
title="Summary",
visible="(factorSummary)",
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"rotation"),
columns=list(
list(
`name`="comp",
`title`="Component",
`type`="text"),
list(
`name`="loadings",
`title`="SS Loadings",
`type`="number"),
list(
`name`="varProp",
`title`="% of Variance",
`type`="number"),
list(
`name`="varCum",
`title`="Cumulative %",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="factorCor",
title="Inter-Component Correlations",
visible="(factorCor)",
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"hideLoadings",
"rotation"),
columns=list(
list(
`name`="comp",
`title`="",
`type`="text",
`format`="narrow"),
list(
`name`="pc1",
`title`="1",
`type`="number"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
fit = function() private$.items[["fit"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modelFit",
title="Model Fit")
self$add(jmvcore::Table$new(
options=options,
name="fit",
title="Model Fit Measures",
rows=1,
visible=FALSE,
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"rotation"),
columns=list(
list(
`name`="rmsea",
`title`="RMSEA",
`type`="number"),
list(
`name`="rmseaLower",
`title`="Lower",
`type`="number",
`superTitle`="RMSEA 90% CI"),
list(
`name`="rmseaUpper",
`title`="Upper",
`type`="number",
`superTitle`="RMSEA 90% CI"),
list(
`name`="tli",
`title`="TLI",
`type`="number"),
list(
`name`="bic",
`title`="BIC",
`type`="number"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number",
`superTitle`="Model Test"),
list(
`name`="df",
`title`="df",
`type`="integer",
`superTitle`="Model Test"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`superTitle`="Model Test"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
bartlett = function() private$.items[["bartlett"]],
kmo = function() private$.items[["kmo"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="bartlett",
title="Bartlett's Test of Sphericity",
visible="(bartlett)",
rows=1,
clearWith=list(
"vars"),
columns=list(
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="kmo",
title="KMO Measure of Sampling Adequacy",
visible="(kmo)",
clearWith=list(
"vars"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text"),
list(
`name`="msa",
`title`="MSA",
`type`="number",
`format`="zto"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
initEigen = function() private$.items[["initEigen"]],
screePlot = function() private$.items[["screePlot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="eigen",
title="Eigenvalues")
self$add(jmvcore::Table$new(
options=options,
name="initEigen",
title="Initial Eigenvalues",
visible="(eigen)",
clearWith=list(
"vars"),
columns=list(
list(
`name`="comp",
`title`="Component",
`type`="text"),
list(
`name`="eigen",
`title`="Eigenvalue",
`type`="number"),
list(
`name`="varProp",
`title`="% of Variance",
`type`="number"),
list(
`name`="varCum",
`title`="Cumulative %",
`type`="number"))))
self$add(jmvcore::Image$new(
options=options,
name="screePlot",
title="Scree Plot",
visible="(screePlot)",
width=500,
height=300,
renderFun=".screePlot",
clearWith=list(
"vars",
"screePlot",
"nFactorMethod",
"minEigen")))}))$new(options=options))
self$add(jmvcore::Output$new(
options=options,
name="factorScoresOV",
title="Factor scores",
initInRun=TRUE,
clearWith=list(
"vars",
"nFactorMethod",
"nFactors",
"rotation",
"factorScoreMethod")))}))
pcaBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"pcaBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "pca",
version = c(1,0,0),
options = options,
results = pcaResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'none')
}))
#' Principal Component Analysis
#'
#' Principal Component Analysis
#'
#' @examples
#' data('iris')
#'
#' pca(iris, vars = vars(Sepal.Length, Sepal.Width, Petal.Length, Petal.Width))
#'
#' #
#' # PRINCIPAL COMPONENT ANALYSIS
#' #
#' # Component Loadings
#' # ----------------------------------------
#' # 1 Uniqueness
#' # ----------------------------------------
#' # Sepal.Length 0.890 0.2076
#' # Sepal.Width -0.460 0.7883
#' # Petal.Length 0.992 0.0168
#' # Petal.Width 0.965 0.0688
#' # ----------------------------------------
#' # Note. 'varimax' rotation was used
#' #
#'
#' @param data the data as a data frame
#' @param vars a vector of strings naming the variables of interest in
#' \code{data}
#' @param nFactorMethod \code{'parallel'} (default), \code{'eigen'} or
#' \code{'fixed'}, the way to determine the number of factors
#' @param nFactors an integer (default: 1), the number of components in the
#' model
#' @param minEigen a number (default: 1), the minimal eigenvalue for a
#' component to be included in the model
#' @param rotation \code{'none'}, \code{'varimax'} (default),
#' \code{'quartimax'}, \code{'promax'}, \code{'oblimin'}, or
#' \code{'simplimax'}, the rotation to use in estimation
#' @param hideLoadings a number (default: 0.3), hide loadings below this value
#' @param sortLoadings \code{TRUE} or \code{FALSE} (default), sort the factor
#' loadings by size
#' @param screePlot \code{TRUE} or \code{FALSE} (default), show scree plot
#' @param eigen \code{TRUE} or \code{FALSE} (default), show eigenvalue table
#' @param factorCor \code{TRUE} or \code{FALSE} (default), show inter-factor
#' correlations
#' @param factorSummary \code{TRUE} or \code{FALSE} (default), show factor
#' summary
#' @param kmo \code{TRUE} or \code{FALSE} (default), show Kaiser-Meyer-Olkin
#' (KMO) measure of sampling adequacy (MSA) results
#' @param bartlett \code{TRUE} or \code{FALSE} (default), show Bartlett's test
#' of sphericity results
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$loadings} \tab \tab \tab \tab \tab a table \cr
#' \code{results$factorStats$factorSummary} \tab \tab \tab \tab \tab a table \cr
#' \code{results$factorStats$factorCor} \tab \tab \tab \tab \tab a table \cr
#' \code{results$modelFit$fit} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$bartlett} \tab \tab \tab \tab \tab a table \cr
#' \code{results$assump$kmo} \tab \tab \tab \tab \tab a table \cr
#' \code{results$eigen$initEigen} \tab \tab \tab \tab \tab a table \cr
#' \code{results$eigen$screePlot} \tab \tab \tab \tab \tab an image \cr
#' \code{results$factorScoresOV} \tab \tab \tab \tab \tab an output \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$loadings$asDF}
#'
#' \code{as.data.frame(results$loadings)}
#'
#' @export
pca <- function(
data,
vars,
nFactorMethod = "parallel",
nFactors = 1,
minEigen = 1,
rotation = "varimax",
hideLoadings = 0.3,
sortLoadings = FALSE,
screePlot = FALSE,
eigen = FALSE,
factorCor = FALSE,
factorSummary = FALSE,
kmo = FALSE,
bartlett = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("pca requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL))
vars <- `if`( ! missing(vars), vars, colnames(data))
options <- pcaOptions$new(
vars = vars,
nFactorMethod = nFactorMethod,
nFactors = nFactors,
minEigen = minEigen,
rotation = rotation,
hideLoadings = hideLoadings,
sortLoadings = sortLoadings,
screePlot = screePlot,
eigen = eigen,
factorCor = factorCor,
factorSummary = factorSummary,
kmo = kmo,
bartlett = bartlett)
analysis <- pcaClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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