aefa: doing automated exploratory factor analysis (aefa) for...

In seonghobae/kaefa: kwangwoon automated exploratory factor analysis

doing automated exploratory factor analysis (aefa) for research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment

Description

doing automated exploratory factor analysis (aefa) for research capability to identify unexplained factor structure with complexly cross-classified multilevel structured data in R environment

Usage

aefa(data, model = NULL, minExtraction = 1, maxExtraction = if
  (is.data.frame(data)) if (ncol(data) <= 10) max(c(1,
  (round(sqrt(ncol(data))) + 2))) else max(c(1, round(sqrt(ncol(data)))))
  else if (class(data) %in% c("SingleGroupClass", "MixedClass",
  "DiscreteClass", "MultipleGroupClass")) max(c(1,
  round(sqrt(ncol(data@Data$data))))) else if (class(data) %in% "aefa")
  max(c(1,
  round(sqrt(ncol(data$estModelTrials[[NROW(data$estModelTrials)]]@Data$data)))))
  else stop("Please provide data correctly."),
  RemoteClusters = getOption("kaefaServers"), sshKeyPath = NULL,
  GenRandomPars = T, NCYCLES = 4000, BURNIN = 1500,
  SEMCYCLES = 1000, covdata = NULL,
  fixed = kaefa:::.covdataFixedEffectComb(covdata),
  random = lapply(c(c(kaefa:::.covdataClassifieder(covdata)$random,
  "items"), if (length(kaefa:::.covdataClassifieder(covdata)$random) != 0)
  paste0(kaefa:::.covdataClassifieder(covdata)$random, ":items")), FUN =
  function(X) {     eval(parse(text = paste0("as.formula(", paste0("~1|",
  X), ")"))) }), key = NULL, accelerate = "squarem", symmetric = F,
  saveModelHistory = T, filename = "aefa.RDS", printItemFit = T,
  rotate = c("bifactorQ", "geominQ", "geominT", "bentlerQ", "bentlerT",
  "oblimin", "simplimax", "tandemII", "tandemI", "entropy", "quartimax"),
  resampling = T, samples = 5000, printDebugMsg = F,
  modelSelectionCriteria = "DIC", saveRawEstModels = F,
  fitEMatUIRT = F, ranefautocomb = T, PV_Q1 = T, tryLCA = F,
  forcingQMC = F, turnOffMixedEst = F, fitIndicesCutOff = 0.005,
  anchor = colnames(data), skipggum = F, powertest = F, idling = 0,
  leniency = F)

Arguments

data	insert data.frame object.
model	specify the mirt model if you have want to calibrate. default is NULL to run exploratory models, but accepting mirt::mirt.model object.
minExtraction	specify the minimum number of factors to calibrate. defaults is 1 but can change this. if model is not NULL, aefa will ignoring this.
maxExtraction	specify the maximum number of factors to calibrate. defaults is 10 but can change this. if model is not NULL, aefa will ignoring this.
RemoteClusters	insert google computing engine virtual machine information.
sshKeyPath	provide the SSH key path, NA is the placeholder.
GenRandomPars	Try to generate Random Parameters? Default is TRUE
NCYCLES	N Cycles of Robbin Monroe stage (stage 3). Default is 4000.
BURNIN	N Cycles of Metro-hastings burnin stage (stage 1). Default is 1500.
SEMCYCLES	N Cycles of Metro-hastings burnin stage (stage 2). Default is 1000.
covdata	insert covariate data frame where use to fixed and random effect term. if not inserted, ignoring fixed and random effect estimation.
fixed	a right sided R formula for specifying the fixed effect (aka 'explanatory') predictors from covdata and itemdesign.
random	a right sided formula or list of formulas containing crossed random effects of the form v1 + ... v_n | G, where G is the grouping variable and v_n are random numeric predictors within each group. G may contain interaction terms, such as group:items to include cross or person-level interactions effects.
key	item key vector of multiple choices test.
accelerate	a character vector indicating the type of acceleration to use. Default is 'squarem' for the SQUAREM procedure (specifically, the gSqS3 approach)
symmetric	force S-EM/Oakes information matrix to be symmetric? Default is FALSE to detect solutions that have not reached the ML estimate.
saveModelHistory	Will you save the model calibration historys? default is TRUE
filename	If you want to save the model calibration historys, Which one want you save file name? default is aefa.RDS, However can change this.
printItemFit	Will you printing item fit indices during the calibrations? default is TRUE.
rotate	set the rotate critera if mirt model is exploratory model. default is bifactorQ, however you can change this what you want to, like 'geominQ', 'bifactorT', 'geominT'. In current, Target rotation not supporting.
resampling	Do you want to do resampling with replace? default is TRUE and activate nrow is over samples argument.
samples	Specify the number samples with resampling. default is 5000.
printDebugMsg	Do you want to see the debugging messeages? default is FALSE
modelSelectionCriteria	Which critera want to use model selection work? 'DIC' (default), 'AIC', 'AICc', 'BIC', 'saBIC' available. AIC and DIC will be identical when no prior parameter distributions are included.
saveRawEstModels	Do you want to save raw estimated models before model selection work? default is FALSE
fitEMatUIRT	Do you want to fit the model with EM at UIRT? default is FALSE
ranefautocomb	Do you want to find global-optimal random effect combination? default is TRUE
PV_Q1	Do you want to get PV_Q1 (Chalmers & Ng, 2017) if can get it? default is TRUE.
tryLCA	Do you want to try calibrate LCA model if avaliable? default is TRUE
forcingQMC	Do you want to forcing the use QMC estimation instead MHRM? default is FALSE
turnOffMixedEst	Do you want to turn off mixed effect (multilevel) estimation? default is FALSE
fitIndicesCutOff	Specify item assessment cutoff. default is p < .005
anchor	Set the anchor item names If you want to consider DIF detection. default is NULL.
skipggum	Set the skipping ggum fitting procedure to speed up. default is FALSE.
powertest	Set power test mode. default is FALSE.
idling	Set seconds to idle. default is 0.
leniency	skip second order test. default is FALSE

Value

automated exploratory factor analytic models

Examples

## Not run: 
testMod1 <- aefa(mirt::Science, minExtraction = 1, maxExtraction = 2)


## End(Not run)

seonghobae/kaefa documentation built on May 8, 2022, 3:31 p.m.