multFac: Tests multiple factors

In Rjacobucci/autoSEM: Performs Specification Search in Structural Equation Models

Description

Tests multiple factors

Usage

multFac(facList, parallel = "no", ncore = 1, method = "GA",
  missing = "listwise", data = NULL, varList = NULL, criterion = "BIC",
  minInd = 3, niter = "default", CV = FALSE, min.improve = niter,
  seed = NULL, std.lv = TRUE, ...)

Arguments

facList	A vector containing the number of factors to test. Ex: ll = c(1,2,3)
parallel	Whether to use the snowfall package for parallelization. Note that this is different than in autoSEM. Parallelization with multFac runs the different factor models separately, not in the actual search algorithm.
ncore	Number of cores to use.
method	which optimization algorithm to use. Currently, it is only recommended to use "GA" for the genetic algorithm from the GA package, "aco", an implementation of the ant colony algorithm by Ross Jacobucci, and "tabu", an implementation of the Tabu search procedure by Ross Jacobucci. The latter two algorithms are based on the book chapter by Marcoulides & Leite, 2013.
missing	Argument to be passed to cfa() as to what to do with missing values. Note: missing="fiml" can't be paired with CV=TRUE
data	a required dataset to search with.
varList	list containing the names of the variables to use from the dataset.
criterion	The fit index to use as a criterion for choosing the best model. Current options are "NCP", "RMSEA", and "BIC".
minInd	The minimum number of indicators per factor.
niter	The maximum number of iterations to use. "default" changes the number of iterations based on the algorithm used.
CV	Whether to use cross-validation for choosing the best model. The default is to use fit indices without CV.
min.improve	Number of iterations to wait for improvement before breaking.
seed	random seed number.
std.lv	Defaults to true. So lavaan uses all variables for each factor
...	Additional arguments to pass to cfa(). An example is is setting orth=FALSE,std.lv=TRUE.

Examples

## Not run: 
library(autoSEM)
myData =  HolzingerSwineford1939[,7:15]

f1.vars <- c("x1","x2","x3","x4","x5","x6","x7","x8","x9")
rrr = list(f1.vars)
facs <- 1:4

out = multFac(facList=facs,parallel="yes",ncore=4,method="GA",
            data=myData,orth=FALSE,CV=FALSE,std.lv=TRUE,
            varList=rrr,criterion="RMSEA",niter="default")

## End(Not run)

Rjacobucci/autoSEM documentation built on May 9, 2019, 10:05 a.m.