knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

We first load the `semtree`

package and the `OpenMx`

package for specifying our SEM.

library(semtree) library(OpenMx)

Now, we simulate some data from a linear latent growth curve model (that is, a random intercept and random slope over time). The dataset will be called `growth.data`

. The dataset contains five observations for each individual (`X1`

to `X5`

) and one predictor `P1`

. The predictor is dichotomous and predicts a (quite large) difference in mean slope.

set.seed(23) N <- 1000 M <- 5 icept <- rnorm(N, 10, sd = 4) slope <- rnorm(N, 3, sd = 1.2) p1 <- sample(c(0, 1), size = N, replace = TRUE) loadings <- 0:4 x <- (slope + p1 * 5) %*% t(loadings) + matrix(rep(icept, each = M), byrow = TRUE, ncol = M) + rnorm(N * M, sd = .08) growth.data <- data.frame(x, factor(p1)) names(growth.data) <- c(paste0("X", 1:M), "P1")

Now, we specify a linear latent growth curve model using OpenMx's path specification. The model has five observed variables. Residual variances are assumed to be identical over time.

manifests <- names(growth.data)[1:5] growthCurveModel <- mxModel("Linear Growth Curve Model Path Specification", type="RAM", manifestVars=manifests, latentVars=c("intercept","slope"), mxData(growth.data, type="raw"), # residual variances mxPath( from=manifests, arrows=2, free=TRUE, values = c(.1, .1, .1, .1, .1), labels=c("residual","residual","residual","residual","residual") ), # latent variances and covariance mxPath( from=c("intercept","slope"), arrows=2, connect="unique.pairs", free=TRUE, values=c(2, 0, 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") ) ) # close model # fit the model to the entire dataset growthCurveModel <- mxRun(growthCurveModel)

Now, we grow a SEM tree using the `semtree`

function, which takes the model and the dataset as input. If not specified otherwise, SEM tree will assume that all variables in the dataset, which are not observed variables in the dataset are potential predictors.

tree <- semtree(model = growthCurveModel, data = growth.data)

Once the tree is grown, we can plot it:

```
plot(tree)
```

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