tests/vim.R

In brandmaier/semtree: Recursive Partitioning for Structural Equation Models

set.seed(789)
require("semtree")
data(lgcm)

lgcm$agegroup <- ordered(lgcm$agegroup)
lgcm$training <- factor(lgcm$training)
lgcm$noise <- factor(lgcm$noise)

# LOAD IN OPENMX MODEL.
# A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA

manifests <- names(lgcm)[1:5]
lgcModel <- mxModel("Linear Growth Curve Model Path Specification",
                            type="RAM",
                            manifestVars=manifests,
                            latentVars=c("intercept","slope"),
                            # residual variances
                            mxPath(
                              from=manifests,
                              arrows=2,
                              free=TRUE,
                              values = c(1, 1, 1, 1, 1),
                              labels=c("residual1","residual2","residual3","residual4","residual5")
                            ),
                            # latent variances and covariance
                            mxPath(
                              from=c("intercept","slope"),
                              connect="unique.pairs",
                              arrows=2,
                              free=TRUE,
                              values=c(1, 1, 1),
                              labels=c("vari", "cov", "vars")
                            ),
                            # intercept loadings
                            mxPath(
                              from="intercept",
                              to=manifests,
                              arrows=1,
                              free=FALSE,
                              values=c(1, 1, 1, 1, 1)
                            ),
                            # slope loadings
                            mxPath(
                              from="slope",
                              to=manifests,
                              arrows=1,
                              free=FALSE,
                              values=c(0, 1, 2, 3, 4)
                            ),
                            # manifest means
                            mxPath(
                              from="one",
                              to=manifests,
                              arrows=1,
                              free=FALSE,
                              values=c(0, 0, 0, 0, 0)
                            ),
                            # latent means
                            mxPath(
                              from="one",
                              to=c("intercept", "slope"),
                              arrows=1,
                              free=TRUE,
                              values=c(1, 1),
                              labels=c("meani", "means")
                            ),
                    mxData(lgcm,type="raw")
                    )


fr <- semforest(lgcModel, 
                lgcm,
                control = semforest.control(num.trees = 3, 
                                            control=semtree.control(method="score",alpha = 1)))

  
vimp <- varimp(fr)

print(vimp)
print(vimp, na.omit=TRUE)

varimpConvergencePlot(vimp, aggregate="mean")
varimpConvergencePlot(vimp, aggregate="median")