autoSEM: This function houses a number of different heuristic...
In autoSEM: Performs Specification Search in Structural Equation Models

Description Usage Arguments Value Examples

View source: R/autoSEM.R

This function houses a number of different heuristic optimization algorithms for specification search.

autoSEM(method = "GA", data = NULL, nfac = NULL, varList = NULL,
  criterion = "BIC", minInd = 3, niter = "default", parallel = "no",
  missing = "listwise", CV = "boot", R = 100, min.improve = niter,
  seed = NULL, std.lv = TRUE, ...)

`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.
`data`	a required dataset to search with.
`nfac`	the number of factors to test.
`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","AIC", "BIC", and "BIC2", which is the sample size adjusted 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.
`parallel`	Whether to use the snowfall package for parallelization. Note that this is only applicable for the GA package at this time.
`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
`CV`	Whether to use cross-validation for choosing the best model. The default is to use fit indices without CV.It is currently recommended to either use FALSE or "boot". Note that "boot" will take significantly longer.
`R`	If using bootstrap, how many samples to take? Default is 100
`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.

fit the fit index

solution the solution with the best fit

out returned object from optimization algorithm

library(autoSEM)
myData =  HolzingerSwineford1939[,7:15]

f1.vars <- c("x1","x2","x3","x4","x5","x6","x7","x8","x9")

out = autoSEM(method="GA",data=myData,nfac=1,
             varList=list(f1.vars),CV=FALSE,
             criterion="RMSEA",minInd=3,niter=1)
summary(out)