plotLTA: Plot latent transition model

In MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

Plot latent transition model

Description

Plots latent transition probabilities and classification probabilities for a single latent transition model (a model with multiple categorical latent variables, regressed on one another). Stroke thickness of nodes represents the proportion of cases most likely assigned to that class, with wider strokes representing greater probability. Edge thickness and transparency represent the probability of making a particular transition (left to right), with thicker/darker edges representing greater probability.

Usage

plotLTA(
  mplusModel,
  node_stroke = 2,
  max_edge_width = 2,
  node_labels = "variable.class",
  x_labels = "variable"
)

Arguments

mplusModel	A single Mplus model object, returned by . This function additionally requires the model to be a mixture model with multiple categorical latent variables.
node_stroke	Integer. Base stroke thickness for nodes. Set to NULL to give each node the same stroke thickness.
max_edge_width	Integer. Maximum width of edges.
node_labels	Character vector, defaults to "variable.class", which labels each node by the name of the variable, and the number of the class it represents. Set to "class" to display only class numbers, or provide a named character vector where the names correspond to original class labels, and the values correspond to their substitute values.
x_labels	Character vector, defaults to "variable", which labels the x-axis with the names of the categorical latent variables. Set to NULL to remove axis labels, or provide a named character vector where the names correspond to original x-axis labels, and the values correspond to their substitute values.

Value

An object of class 'ggplot'.

Author(s)

Caspar J. van Lissa

Examples

## Not run: 
mydat <- read.csv(
system.file("extdata", "ex8.13.csv", package = "MplusAutomation"))
createMixtures(
classes = 2,
filename_stem = "dating",
model_overall = "c2 ON c1;",
model_class_specific = c(
"[u11$1] (a{C});  [u12$1] (b{C});  [u13$1] (c{C});  [u14$1] (d{C});  [u15$1] (e{C});",
"[u21$1] (a{C});  [u22$1] (b{C});  [u23$1] (c{C});  [u24$1] (d{C});  [u25$1] (e{C});"
),
rdata = mydat,
ANALYSIS = "PROCESSORS IS 2;  LRTSTARTS (0 0 40 20);  PARAMETERIZATION = PROBABILITY;",
VARIABLE = "CATEGORICAL = u11-u15 u21-u25;"
)
runModels(filefilter = "dating")
results <- readModels(filefilter = "dating")
plotLTA(results)

## End(Not run)

MplusAutomation documentation built on May 29, 2024, 5:29 a.m.