MplusAutomation: plotLTA – R documentation

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plotLTA

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: 
data <- read.table("http://statmodel.com/usersguide/chap8/ex8.13.dat")[,c(1:10)]
names(data) <- c("u11", "u12", "u13", "u14", "u15", "u21", "u22", "u23", "u24", "u25")
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 = data,
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

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

v0.8

LGPL-3

Authors

Michael Hallquist [aut, cre], Joshua Wiley [aut], Caspar van Lissa [ctb], Daniel Morillo [ctb]

Initial release

2020-09-28