Nothing
# skip parallel tests on CRAN
skip_on_cran()
if (require("future")) {
require("semtree")
data(lgcm)
future::plan(multisession, workers = 5)
lgcm$agegroup <- ordered(lgcm$agegroup)
lgcm$training <- factor(lgcm$training)
lgcm$noise <- as.numeric(lgcm$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")
)
#tree <- semtree(model=lgcModel, data=lgcm)
#lgcModel=mxRun(lgcModel)
# TREE CONTROL OPTIONS.
# TO OBTAIN BASIC/DEFAULT SMETREE OPTIONS, SIMPLY TPYE THE FOLLOWING:
ctrl <- semtree.control(method = "score", verbose = TRUE)
# RUN TREE.
forest <- semforest(
model = lgcModel,
data = lgcm,
control =
semforest.control(
num.trees = 30,
control = semtree.control(alpha =
1, method = "score")
),
constraints = semtree.constraints(focus.parameter =
"meani")
)
vim_naive <- varimp(forest)
vim <- varimp(forest, method = "permutationFocus")
#plot(vim)
#plot(vim_naive)
}
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