semTools (version 0.4-13)

standardizeMx: Find standardized estimates for OpenMx output

Description

Find standardized estimates for OpenMx output. This function is applicable for the MxRAMObjective only.

Usage

standardizeMx(object, free = TRUE)

Arguments

object

Target OpenMx output using MxRAMObjective

free

If TRUE, the function will show only standardized values of free parameters. If FALSE, the function will show the results for fixed and free parameters.

Value

A vector of standardized estimates

See Also

saturateMx, nullMx, fitMeasuresMx

Examples

## Not run: 
# library(OpenMx)
# data(myFADataRaw)
# myFADataRaw <- myFADataRaw[,c("x1","x2","x3","x4","x5","x6")]
# oneFactorModel <- mxModel("Common Factor Model Path Specification", 
# 	type="RAM",
# 	mxData(
# 		observed=myFADataRaw, 
# 		type="raw"
# 	),
# 	manifestVars=c("x1","x2","x3","x4","x5","x6"),
# 	latentVars="F1",
# 	mxPath(from=c("x1","x2","x3","x4","x5","x6"),
# 		arrows=2,
# 		free=TRUE,
# 		values=c(1,1,1,1,1,1),
# 		labels=c("e1","e2","e3","e4","e5","e6")
# 	), 
# 	# residual variances
# 	# -------------------------------------
# 	mxPath(from="F1",
# 		arrows=2,
# 		free=TRUE,
# 		values=1,
# 		labels ="varF1"
# 	), 
# 	# latent variance
# 	# -------------------------------------
# 	mxPath(from="F1",
# 		to=c("x1","x2","x3","x4","x5","x6"),
# 		arrows=1,
# 		free=c(FALSE,TRUE,TRUE,TRUE,TRUE,TRUE),
# 		values=c(1,1,1,1,1,1),
# 		labels =c("l1","l2","l3","l4","l5","l6")
# 	), 
# 	# factor loadings
# 	# -------------------------------------
# 	mxPath(from="one",
# 		to=c("x1","x2","x3","x4","x5","x6","F1"),
# 		arrows=1,
# 		free=c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,FALSE),
# 		values=c(1,1,1,1,1,1,0),
# 		labels =c("meanx1","meanx2","meanx3","meanx4","meanx5","meanx6",NA)
# 	) 
# 	# means
# 	# -------------------------------------
# ) # close model
# # Create an MxModel object
# # -----------------------------------------------------------------------------
# oneFactorFit <- mxRun(oneFactorModel)      
# standardizeMx(oneFactorFit)
# 
# # Compare with lavaan
# library(lavaan)
# script <- "f1 =~ x1 + x2 + x3 + x4 + x5 + x6"
# fit <- cfa(script, data=myFADataRaw, meanstructure=TRUE)
# standardizedSolution(fit)
# ## End(Not run)