R/cfa.h.R
In jamovi/Rjamovi: The 'jamovi' Analyses
Defines functions
cfa
# This file is automatically generated, you probably don't want to edit this
cfaOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"cfaOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
factors = list(
list(label="Factor 1", vars=list())),
resCov = NULL,
miss = "fiml",
constrain = "facVar",
estTest = TRUE,
ci = FALSE,
ciWidth = 95,
stdEst = FALSE,
factCovEst = TRUE,
factInterceptEst = FALSE,
resCovEst = FALSE,
resInterceptEst = FALSE,
fitMeasures = list(
"cfi",
"tli",
"rmsea"),
modelTest = TRUE,
pathDiagram = FALSE,
corRes = FALSE,
hlCorRes = 0.1,
mi = FALSE,
hlMI = 3, ...) {
super$initialize(
package="jmv",
name="cfa",
requiresData=TRUE,
...)
private$..factors <- jmvcore::OptionArray$new(
"factors",
factors,
default=list(
list(label="Factor 1", vars=list())),
template=jmvcore::OptionGroup$new(
"factors",
NULL,
elements=list(
jmvcore::OptionString$new(
"label",
NULL),
jmvcore::OptionVariables$new(
"vars",
NULL,
suggested=list(
"continuous"),
permitted=list(
"numeric")))))
private$..resCov <- jmvcore::OptionPairs$new(
"resCov",
resCov)
private$..miss <- jmvcore::OptionList$new(
"miss",
miss,
options=list(
"listwise",
"fiml"),
default="fiml")
private$..constrain <- jmvcore::OptionList$new(
"constrain",
constrain,
options=list(
"facVar",
"facInd"),
default="facVar")
private$..estTest <- jmvcore::OptionBool$new(
"estTest",
estTest,
default=TRUE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..stdEst <- jmvcore::OptionBool$new(
"stdEst",
stdEst,
default=FALSE)
private$..factCovEst <- jmvcore::OptionBool$new(
"factCovEst",
factCovEst,
default=TRUE)
private$..factInterceptEst <- jmvcore::OptionBool$new(
"factInterceptEst",
factInterceptEst,
default=FALSE)
private$..resCovEst <- jmvcore::OptionBool$new(
"resCovEst",
resCovEst,
default=FALSE)
private$..resInterceptEst <- jmvcore::OptionBool$new(
"resInterceptEst",
resInterceptEst,
default=FALSE)
private$..fitMeasures <- jmvcore::OptionNMXList$new(
"fitMeasures",
fitMeasures,
options=list(
"cfi",
"tli",
"srmr",
"rmsea",
"aic",
"bic"),
default=list(
"cfi",
"tli",
"rmsea"))
private$..modelTest <- jmvcore::OptionBool$new(
"modelTest",
modelTest,
default=TRUE)
private$..pathDiagram <- jmvcore::OptionBool$new(
"pathDiagram",
pathDiagram,
default=FALSE)
private$..corRes <- jmvcore::OptionBool$new(
"corRes",
corRes,
default=FALSE)
private$..hlCorRes <- jmvcore::OptionNumber$new(
"hlCorRes",
hlCorRes,
default=0.1)
private$..mi <- jmvcore::OptionBool$new(
"mi",
mi,
default=FALSE)
private$..hlMI <- jmvcore::OptionNumber$new(
"hlMI",
hlMI,
default=3)
self$.addOption(private$..factors)
self$.addOption(private$..resCov)
self$.addOption(private$..miss)
self$.addOption(private$..constrain)
self$.addOption(private$..estTest)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..stdEst)
self$.addOption(private$..factCovEst)
self$.addOption(private$..factInterceptEst)
self$.addOption(private$..resCovEst)
self$.addOption(private$..resInterceptEst)
self$.addOption(private$..fitMeasures)
self$.addOption(private$..modelTest)
self$.addOption(private$..pathDiagram)
self$.addOption(private$..corRes)
self$.addOption(private$..hlCorRes)
self$.addOption(private$..mi)
self$.addOption(private$..hlMI)
}),
active = list(
factors = function() private$..factors$value,
resCov = function() private$..resCov$value,
miss = function() private$..miss$value,
constrain = function() private$..constrain$value,
estTest = function() private$..estTest$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
stdEst = function() private$..stdEst$value,
factCovEst = function() private$..factCovEst$value,
factInterceptEst = function() private$..factInterceptEst$value,
resCovEst = function() private$..resCovEst$value,
resInterceptEst = function() private$..resInterceptEst$value,
fitMeasures = function() private$..fitMeasures$value,
modelTest = function() private$..modelTest$value,
pathDiagram = function() private$..pathDiagram$value,
corRes = function() private$..corRes$value,
hlCorRes = function() private$..hlCorRes$value,
mi = function() private$..mi$value,
hlMI = function() private$..hlMI$value),
private = list(
..factors = NA,
..resCov = NA,
..miss = NA,
..constrain = NA,
..estTest = NA,
..ci = NA,
..ciWidth = NA,
..stdEst = NA,
..factCovEst = NA,
..factInterceptEst = NA,
..resCovEst = NA,
..resInterceptEst = NA,
..fitMeasures = NA,
..modelTest = NA,
..pathDiagram = NA,
..corRes = NA,
..hlCorRes = NA,
..mi = NA,
..hlMI = NA)
)
cfaResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"cfaResults",
inherit = jmvcore::Group,
active = list(
factorLoadings = function() private$.items[["factorLoadings"]],
factorEst = function() private$.items[["factorEst"]],
resEst = function() private$.items[["resEst"]],
modelFit = function() private$.items[["modelFit"]],
modelPerformance = function() private$.items[["modelPerformance"]],
pathDiagram = function() private$.items[["pathDiagram"]],
modelSyntax = function() private$..modelSyntax),
private = list(
..modelSyntax = NA),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Confirmatory Factor Analysis")
self$add(jmvcore::Table$new(
options=options,
name="factorLoadings",
title="Factor Loadings",
refs="lavaan",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="factor",
`title`="Factor",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="indicator",
`title`="Indicator",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
factorCov = function() private$.items[["factorCov"]],
factorIntercept = function() private$.items[["factorIntercept"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="factorEst",
title="Factor Estimates")
self$add(jmvcore::Table$new(
options=options,
name="factorCov",
title="Factor Covariances",
visible="(factCovEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="factor1",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="factor2",
`title`="",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))
self$add(jmvcore::Table$new(
options=options,
name="factorIntercept",
title="Factor Intercepts",
visible="(factInterceptEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="factor",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
resCov = function() private$.items[["resCov"]],
resIntercept = function() private$.items[["resIntercept"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="resEst",
title="Residual Estimates")
self$add(jmvcore::Table$new(
options=options,
name="resCov",
title="Residual Covariances",
visible="(resCovEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var1",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="var2",
`title`="",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))
self$add(jmvcore::Table$new(
options=options,
name="resIntercept",
title="Residual Intercepts",
visible="(resInterceptEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
test = function() private$.items[["test"]],
fitMeasures = function() private$.items[["fitMeasures"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modelFit",
title="Model Fit")
self$add(jmvcore::Table$new(
options=options,
name="test",
title="Test for Exact Fit",
rows=1,
visible="(modelTest)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
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="fitMeasures",
title="Fit Measures",
rows=1,
visible="(fitMeasures:cfi || fitMeasures:tli || fitMeasures:aic || fitMeasures:bic || fitMeasures:srmr || fitMeasures:rmsea)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="cfi",
`title`="CFI",
`type`="number",
`visible`="(fitMeasures:cfi)"),
list(
`name`="tli",
`title`="TLI",
`type`="number",
`visible`="(fitMeasures:tli)"),
list(
`name`="srmr",
`title`="SRMR",
`type`="number",
`visible`="(fitMeasures:srmr)"),
list(
`name`="rmsea",
`title`="RMSEA",
`type`="number",
`visible`="(fitMeasures:rmsea)"),
list(
`name`="rmseaLower",
`title`="Lower",
`type`="number",
`superTitle`="RMSEA 90% CI",
`visible`="(fitMeasures:rmsea)"),
list(
`name`="rmseaUpper",
`title`="Upper",
`type`="number",
`superTitle`="RMSEA 90% CI",
`visible`="(fitMeasures:rmsea)"),
list(
`name`="aic",
`title`="AIC",
`type`="number",
`visible`="(fitMeasures:aic)"),
list(
`name`="bic",
`title`="BIC",
`type`="number",
`visible`="(fitMeasures:bic)"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
corRes = function() private$.items[["corRes"]],
modIndices = function() private$.items[["modIndices"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modelPerformance",
title="Post-Hoc Model Performance")
self$add(jmvcore::Table$new(
options=options,
name="corRes",
title="Residuals for Observed Correlation Matrix",
visible="(corRes)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`format`="narrow"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
factorLoadingsMod = function() private$.items[["factorLoadingsMod"]],
resCovMod = function() private$.items[["resCovMod"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modIndices",
title="Modification Indices")
self$add(jmvcore::Table$new(
options=options,
name="factorLoadingsMod",
title="Factor Loadings \u2013 Modification Indices",
visible="(mi)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`format`="narrow"))))
self$add(jmvcore::Table$new(
options=options,
name="resCovMod",
title="Residual Covariances \u2013 Modification Indices",
visible="(mi)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`format`="narrow"))))}))$new(options=options))}))$new(options=options))
self$add(jmvcore::Image$new(
options=options,
name="pathDiagram",
title="Path Diagram",
width=450,
height=600,
renderFun=".pathDiagram",
visible="(pathDiagram)",
refs="semPlot",
clearWith=list(
"factors",
"resCov",
"constrain")))
private$..modelSyntax <- NULL},
.setModelSyntax=function(x) private$..modelSyntax <- x))
cfaBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"cfaBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "cfa",
version = c(1,0,0),
options = options,
results = cfaResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Confirmatory Factor Analysis
#'
#' Confirmatory Factor Analysis
#'
#' @examples
#' data <- lavaan::HolzingerSwineford1939
#'
#' jmv::cfa(
#' data = data,
#' factors = list(
#' list(label="Visual", vars=c("x1", "x2", "x3")),
#' list(label="Textual", vars=c("x4", "x5", "x6")),
#' list(label="Speed", vars=c("x7", "x8", "x9"))),
#' resCov = NULL)
#'
#' #
#' # CONFIRMATORY FACTOR ANALYSIS
#' #
#' # Factor Loadings
#' # -----------------------------------------------------------------
#' # Factor Indicator Estimate SE Z p
#' # -----------------------------------------------------------------
#' # Visual x1 0.900 0.0832 10.81 < .001
#' # x2 0.498 0.0808 6.16 < .001
#' # x3 0.656 0.0776 8.46 < .001
#' # Textual x4 0.990 0.0567 17.46 < .001
#' # x5 1.102 0.0626 17.60 < .001
#' # x6 0.917 0.0538 17.05 < .001
#' # Speed x7 0.619 0.0743 8.34 < .001
#' # x8 0.731 0.0755 9.68 < .001
#' # x9 0.670 0.0775 8.64 < .001
#' # -----------------------------------------------------------------
#' #
#' #
#' # FACTOR ESTIMATES
#' #
#' # Factor Covariances
#' # --------------------------------------------------------------
#' # Estimate SE Z p
#' # --------------------------------------------------------------
#' # Visual Visual 1.000 a
#' # Textual 0.459 0.0635 7.22 < .001
#' # Speed 0.471 0.0862 5.46 < .001
#' # Textual Textual 1.000 a
#' # Speed 0.283 0.0715 3.96 < .001
#' # Speed Speed 1.000 a
#' # --------------------------------------------------------------
#' # a fixed parameter
#' #
#' #
#' # MODEL FIT
#' #
#' # Test for Exact Fit
#' # ------------------------
#' # X² df p
#' # ------------------------
#' # 85.3 24 < .001
#' # ------------------------
#' #
#' #
#' # Fit Measures
#' # -----------------------------------------------
#' # CFI TLI RMSEA Lower Upper
#' # -----------------------------------------------
#' # 0.931 0.896 0.0921 0.0714 0.114
#' # -----------------------------------------------
#' #
#'
#' @param data the data as a data frame
#' @param factors a list containing named lists that define the \code{label}
#' of the factor and the \code{vars} that belong to that factor
#' @param resCov a list of lists specifying the residual covariances that need
#' to be estimated
#' @param miss \code{'listwise'} or \code{'fiml'}, how to handle missing
#' values; \code{'listwise'} excludes a row from all analyses if one of its
#' entries is missing, \code{'fiml'} uses a full information maximum
#' likelihood method to estimate the model.
#' @param constrain \code{'facVar'} or \code{'facInd'}, how to contrain the
#' model; \code{'facVar'} fixes the factor variances to one, \code{'facInd'}
#' fixes each factor to the scale of its first indicator.
#' @param estTest \code{TRUE} (default) or \code{FALSE}, provide 'Z' and 'p'
#' values for the model estimates
#' @param ci \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model estimates
#' @param ciWidth a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width that is used as \code{'ci'}
#' @param stdEst \code{TRUE} or \code{FALSE} (default), provide a standardized
#' estimate for the model estimates
#' @param factCovEst \code{TRUE} (default) or \code{FALSE}, provide estimates
#' for the factor (co)variances
#' @param factInterceptEst \code{TRUE} or \code{FALSE} (default), provide
#' estimates for the factor intercepts
#' @param resCovEst \code{TRUE} (default) or \code{FALSE}, provide estimates
#' for the residual (co)variances
#' @param resInterceptEst \code{TRUE} or \code{FALSE} (default), provide
#' estimates for the residual intercepts
#' @param fitMeasures one or more of \code{'cfi'}, \code{'tli'},
#' \code{'srmr'}, \code{'rmsea'}, \code{'aic'}, or \code{'bic'}; use CFI, TLI,
#' SRMR, RMSEA + 90\% confidence interval, adjusted AIC, and BIC model fit
#' measures, respectively
#' @param modelTest \code{TRUE} (default) or \code{FALSE}, provide a
#' chi-square test for exact fit that compares the model with the perfect
#' fitting model
#' @param pathDiagram \code{TRUE} or \code{FALSE} (default), provide a path
#' diagram of the model
#' @param corRes \code{TRUE} or \code{FALSE} (default), provide the residuals
#' for the observed correlation matrix (i.e., the difference between the
#' expected correlation matrix and the observed correlation matrix)
#' @param hlCorRes a number (default: 0.1), highlight values in the
#' \code{'corRes'} table above this value
#' @param mi \code{TRUE} or \code{FALSE} (default), provide modification
#' indices for the parameters not included in the model
#' @param hlMI a number (default: 3), highlight values in the
#' \code{'modIndices'} tables above this value
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$factorLoadings} \tab \tab \tab \tab \tab a table containing the factor loadings \cr
#' \code{results$factorEst$factorCov} \tab \tab \tab \tab \tab a table containing factor covariances estimates \cr
#' \code{results$factorEst$factorIntercept} \tab \tab \tab \tab \tab a table containing factor intercept estimates \cr
#' \code{results$resEst$resCov} \tab \tab \tab \tab \tab a table containing residual covariances estimates \cr
#' \code{results$resEst$resIntercept} \tab \tab \tab \tab \tab a table containing residual intercept estimates \cr
#' \code{results$modelFit$test} \tab \tab \tab \tab \tab a table containing the chi-square test for exact fit \cr
#' \code{results$modelFit$fitMeasures} \tab \tab \tab \tab \tab a table containing fit measures \cr
#' \code{results$modelPerformance$corRes} \tab \tab \tab \tab \tab a table containing residuals for the observed correlation matrix \cr
#' \code{results$modelPerformance$modIndices} \tab \tab \tab \tab \tab a group \cr
#' \code{results$pathDiagram} \tab \tab \tab \tab \tab an image containing the model path diagram \cr
#' \code{results$modelSyntax} \tab \tab \tab \tab \tab the lavaan syntax used to fit the model \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$factorLoadings$asDF}
#'
#' \code{as.data.frame(results$factorLoadings)}
#'
#' @export
cfa <- function(
data,
factors = list(
list(label="Factor 1", vars=list())),
resCov,
miss = "fiml",
constrain = "facVar",
estTest = TRUE,
ci = FALSE,
ciWidth = 95,
stdEst = FALSE,
factCovEst = TRUE,
factInterceptEst = FALSE,
resCovEst = FALSE,
resInterceptEst = FALSE,
fitMeasures = list(
"cfi",
"tli",
"rmsea"),
modelTest = TRUE,
pathDiagram = FALSE,
corRes = FALSE,
hlCorRes = 0.1,
mi = FALSE,
hlMI = 3) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("cfa requires jmvcore to be installed (restart may be required)")
if (missing(data))
data <- jmvcore::marshalData(
parent.frame())
options <- cfaOptions$new(
factors = factors,
resCov = resCov,
miss = miss,
constrain = constrain,
estTest = estTest,
ci = ci,
ciWidth = ciWidth,
stdEst = stdEst,
factCovEst = factCovEst,
factInterceptEst = factInterceptEst,
resCovEst = resCovEst,
resInterceptEst = resInterceptEst,
fitMeasures = fitMeasures,
modelTest = modelTest,
pathDiagram = pathDiagram,
corRes = corRes,
hlCorRes = hlCorRes,
mi = mi,
hlMI = hlMI)
analysis <- cfaClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
jamovi/Rjamovi documentation built on April 13, 2024, 10:01 p.m.
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Defines functions cfa
# This file is automatically generated, you probably don't want to edit this
cfaOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"cfaOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
factors = list(
list(label="Factor 1", vars=list())),
resCov = NULL,
miss = "fiml",
constrain = "facVar",
estTest = TRUE,
ci = FALSE,
ciWidth = 95,
stdEst = FALSE,
factCovEst = TRUE,
factInterceptEst = FALSE,
resCovEst = FALSE,
resInterceptEst = FALSE,
fitMeasures = list(
"cfi",
"tli",
"rmsea"),
modelTest = TRUE,
pathDiagram = FALSE,
corRes = FALSE,
hlCorRes = 0.1,
mi = FALSE,
hlMI = 3, ...) {
super$initialize(
package="jmv",
name="cfa",
requiresData=TRUE,
...)
private$..factors <- jmvcore::OptionArray$new(
"factors",
factors,
default=list(
list(label="Factor 1", vars=list())),
template=jmvcore::OptionGroup$new(
"factors",
NULL,
elements=list(
jmvcore::OptionString$new(
"label",
NULL),
jmvcore::OptionVariables$new(
"vars",
NULL,
suggested=list(
"continuous"),
permitted=list(
"numeric")))))
private$..resCov <- jmvcore::OptionPairs$new(
"resCov",
resCov)
private$..miss <- jmvcore::OptionList$new(
"miss",
miss,
options=list(
"listwise",
"fiml"),
default="fiml")
private$..constrain <- jmvcore::OptionList$new(
"constrain",
constrain,
options=list(
"facVar",
"facInd"),
default="facVar")
private$..estTest <- jmvcore::OptionBool$new(
"estTest",
estTest,
default=TRUE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..stdEst <- jmvcore::OptionBool$new(
"stdEst",
stdEst,
default=FALSE)
private$..factCovEst <- jmvcore::OptionBool$new(
"factCovEst",
factCovEst,
default=TRUE)
private$..factInterceptEst <- jmvcore::OptionBool$new(
"factInterceptEst",
factInterceptEst,
default=FALSE)
private$..resCovEst <- jmvcore::OptionBool$new(
"resCovEst",
resCovEst,
default=FALSE)
private$..resInterceptEst <- jmvcore::OptionBool$new(
"resInterceptEst",
resInterceptEst,
default=FALSE)
private$..fitMeasures <- jmvcore::OptionNMXList$new(
"fitMeasures",
fitMeasures,
options=list(
"cfi",
"tli",
"srmr",
"rmsea",
"aic",
"bic"),
default=list(
"cfi",
"tli",
"rmsea"))
private$..modelTest <- jmvcore::OptionBool$new(
"modelTest",
modelTest,
default=TRUE)
private$..pathDiagram <- jmvcore::OptionBool$new(
"pathDiagram",
pathDiagram,
default=FALSE)
private$..corRes <- jmvcore::OptionBool$new(
"corRes",
corRes,
default=FALSE)
private$..hlCorRes <- jmvcore::OptionNumber$new(
"hlCorRes",
hlCorRes,
default=0.1)
private$..mi <- jmvcore::OptionBool$new(
"mi",
mi,
default=FALSE)
private$..hlMI <- jmvcore::OptionNumber$new(
"hlMI",
hlMI,
default=3)
self$.addOption(private$..factors)
self$.addOption(private$..resCov)
self$.addOption(private$..miss)
self$.addOption(private$..constrain)
self$.addOption(private$..estTest)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..stdEst)
self$.addOption(private$..factCovEst)
self$.addOption(private$..factInterceptEst)
self$.addOption(private$..resCovEst)
self$.addOption(private$..resInterceptEst)
self$.addOption(private$..fitMeasures)
self$.addOption(private$..modelTest)
self$.addOption(private$..pathDiagram)
self$.addOption(private$..corRes)
self$.addOption(private$..hlCorRes)
self$.addOption(private$..mi)
self$.addOption(private$..hlMI)
}),
active = list(
factors = function() private$..factors$value,
resCov = function() private$..resCov$value,
miss = function() private$..miss$value,
constrain = function() private$..constrain$value,
estTest = function() private$..estTest$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
stdEst = function() private$..stdEst$value,
factCovEst = function() private$..factCovEst$value,
factInterceptEst = function() private$..factInterceptEst$value,
resCovEst = function() private$..resCovEst$value,
resInterceptEst = function() private$..resInterceptEst$value,
fitMeasures = function() private$..fitMeasures$value,
modelTest = function() private$..modelTest$value,
pathDiagram = function() private$..pathDiagram$value,
corRes = function() private$..corRes$value,
hlCorRes = function() private$..hlCorRes$value,
mi = function() private$..mi$value,
hlMI = function() private$..hlMI$value),
private = list(
..factors = NA,
..resCov = NA,
..miss = NA,
..constrain = NA,
..estTest = NA,
..ci = NA,
..ciWidth = NA,
..stdEst = NA,
..factCovEst = NA,
..factInterceptEst = NA,
..resCovEst = NA,
..resInterceptEst = NA,
..fitMeasures = NA,
..modelTest = NA,
..pathDiagram = NA,
..corRes = NA,
..hlCorRes = NA,
..mi = NA,
..hlMI = NA)
)
cfaResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"cfaResults",
inherit = jmvcore::Group,
active = list(
factorLoadings = function() private$.items[["factorLoadings"]],
factorEst = function() private$.items[["factorEst"]],
resEst = function() private$.items[["resEst"]],
modelFit = function() private$.items[["modelFit"]],
modelPerformance = function() private$.items[["modelPerformance"]],
pathDiagram = function() private$.items[["pathDiagram"]],
modelSyntax = function() private$..modelSyntax),
private = list(
..modelSyntax = NA),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Confirmatory Factor Analysis")
self$add(jmvcore::Table$new(
options=options,
name="factorLoadings",
title="Factor Loadings",
refs="lavaan",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="factor",
`title`="Factor",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="indicator",
`title`="Indicator",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
factorCov = function() private$.items[["factorCov"]],
factorIntercept = function() private$.items[["factorIntercept"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="factorEst",
title="Factor Estimates")
self$add(jmvcore::Table$new(
options=options,
name="factorCov",
title="Factor Covariances",
visible="(factCovEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="factor1",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="factor2",
`title`="",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))
self$add(jmvcore::Table$new(
options=options,
name="factorIntercept",
title="Factor Intercepts",
visible="(factInterceptEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="factor",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
resCov = function() private$.items[["resCov"]],
resIntercept = function() private$.items[["resIntercept"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="resEst",
title="Residual Estimates")
self$add(jmvcore::Table$new(
options=options,
name="resCov",
title="Residual Covariances",
visible="(resCovEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var1",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="var2",
`title`="",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))
self$add(jmvcore::Table$new(
options=options,
name="resIntercept",
title="Residual Intercepts",
visible="(resInterceptEst)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="z",
`title`="Z",
`type`="number",
`visible`="(estTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`visible`="(estTest)"),
list(
`name`="stdEst",
`title`="Stand. Estimate",
`type`="number",
`visible`="(stdEst)"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
test = function() private$.items[["test"]],
fitMeasures = function() private$.items[["fitMeasures"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modelFit",
title="Model Fit")
self$add(jmvcore::Table$new(
options=options,
name="test",
title="Test for Exact Fit",
rows=1,
visible="(modelTest)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="fitMeasures",
title="Fit Measures",
rows=1,
visible="(fitMeasures:cfi || fitMeasures:tli || fitMeasures:aic || fitMeasures:bic || fitMeasures:srmr || fitMeasures:rmsea)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="cfi",
`title`="CFI",
`type`="number",
`visible`="(fitMeasures:cfi)"),
list(
`name`="tli",
`title`="TLI",
`type`="number",
`visible`="(fitMeasures:tli)"),
list(
`name`="srmr",
`title`="SRMR",
`type`="number",
`visible`="(fitMeasures:srmr)"),
list(
`name`="rmsea",
`title`="RMSEA",
`type`="number",
`visible`="(fitMeasures:rmsea)"),
list(
`name`="rmseaLower",
`title`="Lower",
`type`="number",
`superTitle`="RMSEA 90% CI",
`visible`="(fitMeasures:rmsea)"),
list(
`name`="rmseaUpper",
`title`="Upper",
`type`="number",
`superTitle`="RMSEA 90% CI",
`visible`="(fitMeasures:rmsea)"),
list(
`name`="aic",
`title`="AIC",
`type`="number",
`visible`="(fitMeasures:aic)"),
list(
`name`="bic",
`title`="BIC",
`type`="number",
`visible`="(fitMeasures:bic)"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
corRes = function() private$.items[["corRes"]],
modIndices = function() private$.items[["modIndices"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modelPerformance",
title="Post-Hoc Model Performance")
self$add(jmvcore::Table$new(
options=options,
name="corRes",
title="Residuals for Observed Correlation Matrix",
visible="(corRes)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`format`="narrow"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
factorLoadingsMod = function() private$.items[["factorLoadingsMod"]],
resCovMod = function() private$.items[["resCovMod"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="modIndices",
title="Modification Indices")
self$add(jmvcore::Table$new(
options=options,
name="factorLoadingsMod",
title="Factor Loadings \u2013 Modification Indices",
visible="(mi)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`format`="narrow"))))
self$add(jmvcore::Table$new(
options=options,
name="resCovMod",
title="Residual Covariances \u2013 Modification Indices",
visible="(mi)",
clearWith=list(
"factors",
"resCov",
"constrain",
"miss"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`format`="narrow"))))}))$new(options=options))}))$new(options=options))
self$add(jmvcore::Image$new(
options=options,
name="pathDiagram",
title="Path Diagram",
width=450,
height=600,
renderFun=".pathDiagram",
visible="(pathDiagram)",
refs="semPlot",
clearWith=list(
"factors",
"resCov",
"constrain")))
private$..modelSyntax <- NULL},
.setModelSyntax=function(x) private$..modelSyntax <- x))
cfaBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"cfaBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "cfa",
version = c(1,0,0),
options = options,
results = cfaResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Confirmatory Factor Analysis
#'
#' Confirmatory Factor Analysis
#'
#' @examples
#' data <- lavaan::HolzingerSwineford1939
#'
#' jmv::cfa(
#' data = data,
#' factors = list(
#' list(label="Visual", vars=c("x1", "x2", "x3")),
#' list(label="Textual", vars=c("x4", "x5", "x6")),
#' list(label="Speed", vars=c("x7", "x8", "x9"))),
#' resCov = NULL)
#'
#' #
#' # CONFIRMATORY FACTOR ANALYSIS
#' #
#' # Factor Loadings
#' # -----------------------------------------------------------------
#' # Factor Indicator Estimate SE Z p
#' # -----------------------------------------------------------------
#' # Visual x1 0.900 0.0832 10.81 < .001
#' # x2 0.498 0.0808 6.16 < .001
#' # x3 0.656 0.0776 8.46 < .001
#' # Textual x4 0.990 0.0567 17.46 < .001
#' # x5 1.102 0.0626 17.60 < .001
#' # x6 0.917 0.0538 17.05 < .001
#' # Speed x7 0.619 0.0743 8.34 < .001
#' # x8 0.731 0.0755 9.68 < .001
#' # x9 0.670 0.0775 8.64 < .001
#' # -----------------------------------------------------------------
#' #
#' #
#' # FACTOR ESTIMATES
#' #
#' # Factor Covariances
#' # --------------------------------------------------------------
#' # Estimate SE Z p
#' # --------------------------------------------------------------
#' # Visual Visual 1.000 a
#' # Textual 0.459 0.0635 7.22 < .001
#' # Speed 0.471 0.0862 5.46 < .001
#' # Textual Textual 1.000 a
#' # Speed 0.283 0.0715 3.96 < .001
#' # Speed Speed 1.000 a
#' # --------------------------------------------------------------
#' # a fixed parameter
#' #
#' #
#' # MODEL FIT
#' #
#' # Test for Exact Fit
#' # ------------------------
#' # X² df p
#' # ------------------------
#' # 85.3 24 < .001
#' # ------------------------
#' #
#' #
#' # Fit Measures
#' # -----------------------------------------------
#' # CFI TLI RMSEA Lower Upper
#' # -----------------------------------------------
#' # 0.931 0.896 0.0921 0.0714 0.114
#' # -----------------------------------------------
#' #
#'
#' @param data the data as a data frame
#' @param factors a list containing named lists that define the \code{label}
#' of the factor and the \code{vars} that belong to that factor
#' @param resCov a list of lists specifying the residual covariances that need
#' to be estimated
#' @param miss \code{'listwise'} or \code{'fiml'}, how to handle missing
#' values; \code{'listwise'} excludes a row from all analyses if one of its
#' entries is missing, \code{'fiml'} uses a full information maximum
#' likelihood method to estimate the model.
#' @param constrain \code{'facVar'} or \code{'facInd'}, how to contrain the
#' model; \code{'facVar'} fixes the factor variances to one, \code{'facInd'}
#' fixes each factor to the scale of its first indicator.
#' @param estTest \code{TRUE} (default) or \code{FALSE}, provide 'Z' and 'p'
#' values for the model estimates
#' @param ci \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model estimates
#' @param ciWidth a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width that is used as \code{'ci'}
#' @param stdEst \code{TRUE} or \code{FALSE} (default), provide a standardized
#' estimate for the model estimates
#' @param factCovEst \code{TRUE} (default) or \code{FALSE}, provide estimates
#' for the factor (co)variances
#' @param factInterceptEst \code{TRUE} or \code{FALSE} (default), provide
#' estimates for the factor intercepts
#' @param resCovEst \code{TRUE} (default) or \code{FALSE}, provide estimates
#' for the residual (co)variances
#' @param resInterceptEst \code{TRUE} or \code{FALSE} (default), provide
#' estimates for the residual intercepts
#' @param fitMeasures one or more of \code{'cfi'}, \code{'tli'},
#' \code{'srmr'}, \code{'rmsea'}, \code{'aic'}, or \code{'bic'}; use CFI, TLI,
#' SRMR, RMSEA + 90\% confidence interval, adjusted AIC, and BIC model fit
#' measures, respectively
#' @param modelTest \code{TRUE} (default) or \code{FALSE}, provide a
#' chi-square test for exact fit that compares the model with the perfect
#' fitting model
#' @param pathDiagram \code{TRUE} or \code{FALSE} (default), provide a path
#' diagram of the model
#' @param corRes \code{TRUE} or \code{FALSE} (default), provide the residuals
#' for the observed correlation matrix (i.e., the difference between the
#' expected correlation matrix and the observed correlation matrix)
#' @param hlCorRes a number (default: 0.1), highlight values in the
#' \code{'corRes'} table above this value
#' @param mi \code{TRUE} or \code{FALSE} (default), provide modification
#' indices for the parameters not included in the model
#' @param hlMI a number (default: 3), highlight values in the
#' \code{'modIndices'} tables above this value
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$factorLoadings} \tab \tab \tab \tab \tab a table containing the factor loadings \cr
#' \code{results$factorEst$factorCov} \tab \tab \tab \tab \tab a table containing factor covariances estimates \cr
#' \code{results$factorEst$factorIntercept} \tab \tab \tab \tab \tab a table containing factor intercept estimates \cr
#' \code{results$resEst$resCov} \tab \tab \tab \tab \tab a table containing residual covariances estimates \cr
#' \code{results$resEst$resIntercept} \tab \tab \tab \tab \tab a table containing residual intercept estimates \cr
#' \code{results$modelFit$test} \tab \tab \tab \tab \tab a table containing the chi-square test for exact fit \cr
#' \code{results$modelFit$fitMeasures} \tab \tab \tab \tab \tab a table containing fit measures \cr
#' \code{results$modelPerformance$corRes} \tab \tab \tab \tab \tab a table containing residuals for the observed correlation matrix \cr
#' \code{results$modelPerformance$modIndices} \tab \tab \tab \tab \tab a group \cr
#' \code{results$pathDiagram} \tab \tab \tab \tab \tab an image containing the model path diagram \cr
#' \code{results$modelSyntax} \tab \tab \tab \tab \tab the lavaan syntax used to fit the model \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$factorLoadings$asDF}
#'
#' \code{as.data.frame(results$factorLoadings)}
#'
#' @export
cfa <- function(
data,
factors = list(
list(label="Factor 1", vars=list())),
resCov,
miss = "fiml",
constrain = "facVar",
estTest = TRUE,
ci = FALSE,
ciWidth = 95,
stdEst = FALSE,
factCovEst = TRUE,
factInterceptEst = FALSE,
resCovEst = FALSE,
resInterceptEst = FALSE,
fitMeasures = list(
"cfi",
"tli",
"rmsea"),
modelTest = TRUE,
pathDiagram = FALSE,
corRes = FALSE,
hlCorRes = 0.1,
mi = FALSE,
hlMI = 3) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("cfa requires jmvcore to be installed (restart may be required)")
if (missing(data))
data <- jmvcore::marshalData(
parent.frame())
options <- cfaOptions$new(
factors = factors,
resCov = resCov,
miss = miss,
constrain = constrain,
estTest = estTest,
ci = ci,
ciWidth = ciWidth,
stdEst = stdEst,
factCovEst = factCovEst,
factInterceptEst = factInterceptEst,
resCovEst = resCovEst,
resInterceptEst = resInterceptEst,
fitMeasures = fitMeasures,
modelTest = modelTest,
pathDiagram = pathDiagram,
corRes = corRes,
hlCorRes = hlCorRes,
mi = mi,
hlMI = hlMI)
analysis <- cfaClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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