Description Usage Arguments Details Examples
Generates recursive partitioning trees using Mplus models. MplusTrees()
takes an
Mplus model written in the form of an MplusAutomation
script, uses
MplusAutomation
to fit the model in Mplus, and performs recursive partitioning
using rpart
.
1 2  MplusTrees(script, data, rPartFormula, catvars = NULL, group = ~id,
control = rpart.control(), se = F, psplit = F, palpha = 0.05)

script 
An 
data 
Dataset that is specified in the script 
rPartFormula 
Formula of the form ~ variable names 
catvars 
Vector of names of categorical covariates 
group 
id variable. If not specified an id variable is created for each row. 
control 
Control object for 
se 
Whether to print standard errors and p values. In general should be set to FALSE. 
psplit 
Whether to use likelihood ratio p values as a splitting criterion 
palpha 
Type I error rate (alpha level) for rejecting with likelihood ratio test when

In the fitted object, rpart_out
provides the tree structure, terminal
gives a
vector of terminal nodes, where
shows the terminal node of each id, and estimates
gives
the parameter estimates for each terminal node.
By default MplusTrees()
only splits on the criteria specified in the control
argument, the most important of which is the cp
parameter. However, the user can also split on the
p value generated from the likelihood ratio test comparing the parent node to a multiple group
model consisting of 2 groups (the daughter nodes). This p value criterion is used in addition
to the cp
criterion in that both must be met for a split to be made. The psplit
argument
turns this option on, palpha
sets the alpha level criterion for rejection.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## Not run:
library(lavaan)
script = mplusObject(
TITLE = "Example #1  Factor Model;",
MODEL = "f1 BY x1x3; f2 BY x4x6; f3 BY x7x9;",
usevariables = c('x1','x2','x3','x4','x5','x6','x7','x8','x9'),
rdata = HolzingerSwineford1939)
fit = MplusTrees(script, HolzingerSwineford1939, group=~id,
rPartFormula=~sex+school+grade, control=rpart.control(cp=.01))
fit
## End(Not run)

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