# NOT RUN {
# }
# 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)
# }
# NOT RUN {
# }
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