Nothing
skip_on_cran()
set.seed(789)
require("semtree")
data(lgcm)
lgcm$agegroup <- ordered(lgcm$agegroup)
lgcm$training <- factor(lgcm$training)
lgcm$noise <- factor(lgcm$noise)
lgcm$noise2 <- factor(sample(c(0,1,2), nrow(lgcm),TRUE))
# LOAD IN OPENMX MODEL.
# A SIMPLE LINEAR GROWTH MODEL WITH 5 TIME POINTS FROM SIMULATED DATA
manifests <- names(lgcm)[1:5]
lgcModel <- mxModel("Linear Growth Curve Model Path Specification",
type="RAM",
manifestVars=manifests,
latentVars=c("intercept","slope"),
# residual variances
mxPath(
from=manifests,
arrows=2,
free=TRUE,
values = c(1, 1, 1, 1, 1),
labels=c("residual1","residual2","residual3","residual4","residual5")
),
# latent variances and covariance
mxPath(
from=c("intercept","slope"),
connect="unique.pairs",
arrows=2,
free=TRUE,
values=c(1, 1, 1),
labels=c("vari", "cov", "vars")
),
# intercept loadings
mxPath(
from="intercept",
to=manifests,
arrows=1,
free=FALSE,
values=c(1, 1, 1, 1, 1)
),
# slope loadings
mxPath(
from="slope",
to=manifests,
arrows=1,
free=FALSE,
values=c(0, 1, 2, 3, 4)
),
# manifest means
mxPath(
from="one",
to=manifests,
arrows=1,
free=FALSE,
values=c(0, 0, 0, 0, 0)
),
# latent means
mxPath(
from="one",
to=c("intercept", "slope"),
arrows=1,
free=TRUE,
values=c(1, 1),
labels=c("meani", "means")
),
mxData(lgcm,type="raw")
)
ctrl <- semforest.control(num.trees = 10)
ctrl$semtree.control$method <- "score"
fr <- semforest(lgcModel, lgcm, control = ctrl)
vimp <- varimp(fr)
print(vimp)
print(vimp, na.omit=TRUE)
# reorder dataset
lgcm <- lgcm[, sample(1:ncol(lgcm),ncol(lgcm),FALSE)]
fr2 <- semforest(lgcModel, lgcm, control = ctrl)
vimp2 <- varimp(fr2)
print(vim, scale="relative.baseline")
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