lcsm (version 0.3.2)

plot_lcsm: Plot simplified path diagram of univariate and bivariate latent change score models

Description

Note that the following three arguments are needed to create a plot (see below for more details):

• lavaan_object: the lavaan fit object needs to be specified together with a

• lcsm: a string indicating whether the latent change score model is "univariate" or "bivariate", and

• lavaan_syntax: a separate object with the lavaan syntax as a string

Usage

plot_lcsm(
  lavaan_object,
  layout = NULL,
  lavaan_syntax = NULL,
  return_layout_from_lavaan_syntax = FALSE,
  lcsm = c("univariate", "bivariate"),
  lcsm_colours = FALSE,
  curve_covar = 0.5,
  what = "path",
  whatLabels = "est",
  edge.width = 1,
  node.width = 1,
  border.width = 1,
  fixedStyle = 1,
  freeStyle = 1,
  residuals = FALSE,
  label.scale = FALSE,
  sizeMan = 3,
  sizeLat = 5,
  intercepts = FALSE,
  fade = FALSE,
  nCharNodes = 0,
  nCharEdges = 0,
  edge.label.cex = 0.5,
  ...
)

Value

Plot

Arguments

lavaan_object

lavaan object of a univariate or bivariate latent change score model.

layout

Matrix, specifying number and location of manifest and latent variables of LCS model specified in lavaan_object.

lavaan_syntax

String, lavaan syntax of the lavaan object specified in lavaan_object. If lavaan_syntax is provided a layout matrix will be generated automatically.

return_layout_from_lavaan_syntax

Logical, if TRUE and lavaan_syntax is provided, the layout matrix generated for semPaths will be returned for inspection of further customisation.

lcsm

String, specifying whether lavaan_object represent a "univariate" or "bivariate" LCS model.

lcsm_colours

Logical, if TRUE the following colours will be used to highlight different parts of the model: Observed variables (White); Latent true scores (Green); Latent change scores (Blue) ; Change factors (Yellow).

curve_covar

See semPaths.

what

See semPlot. "path" to show unweighted grey edges, "par" to show parameter estimates as weighted (green/red) edges

whatLabels

See semPaths. "label" to show edge names as label, "est" for parameter estimates, "hide" to hide edge labels.

edge.width

See semPaths.

node.width

See semPaths.

border.width

See semPaths.

fixedStyle

See semPaths.

freeStyle

See semPaths.

residuals

See semPaths.

label.scale

See semPaths.

sizeMan

See semPaths.

sizeLat

See semPaths.

intercepts

See semPaths.

fade

See semPaths.

nCharNodes

See semPaths.

nCharEdges

See semPaths.

edge.label.cex

See semPaths.

...

Other arguments passed on to semPaths.

References

Sacha Epskamp (2019). semPlot: Path Diagrams and Visual Analysis of Various SEM Packages' Output. R package version 1.1.1. https://CRAN.R-project.org/package=semPlot/

Examples

# Simplified plot of univariate lcsm
lavaan_syntax_uni <- fit_uni_lcsm(
  data = data_bi_lcsm,
  var = c("x1", "x2", "x3", "x4", "x5"),
  model = list(
    alpha_constant = TRUE,
    beta = TRUE,
    phi = TRUE
  ),
  return_lavaan_syntax = TRUE,
  return_lavaan_syntax_string = TRUE
)

lavaan_object_uni <- fit_uni_lcsm(
  data = data_bi_lcsm,
  var = c("x1", "x2", "x3", "x4", "x5"),
  model = list(
    alpha_constant = TRUE,
    beta = TRUE,
    phi = TRUE
  )
)

plot_lcsm(
  lavaan_object = lavaan_object_uni,
  what = "cons", whatLabels = "invisible",
  lavaan_syntax = lavaan_syntax_uni,
  lcsm = "univariate"
)
if (FALSE) {
# Simplified plot of bivariate lcsm
lavaan_syntax_bi <- fit_bi_lcsm(
  data = data_bi_lcsm,
  var_x = c("x1", "x2", "x3", "x4", "x5"),
  var_y = c("y1", "y2", "y3", "y4", "y5"),
  model_x = list(
    alpha_constant = TRUE,
    beta = TRUE,
    phi = TRUE
  ),
  model_y = list(
    alpha_constant = TRUE,
    beta = TRUE,
    phi = TRUE
  ),
  coupling = list(
    delta_lag_xy = TRUE,
    delta_lag_yx = TRUE
  ),
  return_lavaan_syntax = TRUE,
  return_lavaan_syntax_string = TRUE
)

lavaan_object_bi <- fit_bi_lcsm(
  data = data_bi_lcsm,
  var_x = c("x1", "x2", "x3", "x4", "x5"),
  var_y = c("y1", "y2", "y3", "y4", "y5"),
  model_x = list(
    alpha_constant = TRUE,
    beta = TRUE,
    phi = TRUE
  ),
  model_y = list(
    alpha_constant = TRUE,
    beta = TRUE,
    phi = TRUE
  ),
  coupling = list(
    delta_lag_xy = TRUE,
    delta_lag_yx = TRUE
  )
)

plot_lcsm(
  lavaan_object = lavaan_object_bi,
  what = "cons", whatLabels = "invisible",
  lavaan_syntax = lavaan_syntax_bi,
  lcsm = "bivariate"
)
}