Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(semtree)
library(OpenMx)
## ----simdata------------------------------------------------------------------
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")
## -----------------------------------------------------------------------------
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)
## ----message=FALSE,warning=FALSE,results="hide"-------------------------------
tree <- semtree(model = growthCurveModel,
data = growth.data)
## -----------------------------------------------------------------------------
plot(tree)
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