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Plot fit indices from psymetrics fit objects

Source: R/plot_model_fit.R
plot_model_fit.Rd

plot_model_fit() is the public plotting entrypoint for fit-index objects created by model_fit() and compare_model_fit(). In v0.5.0, it supports exactly two classes:

  • model_fit for one fitted model, including multi-row test summaries

  • compare_model_fit for two or more fitted models, including multi-row test summaries

The default plot style is chosen automatically after applying test_mode. For model_fit, the default is a single-fit bullet chart when one row remains, and a threshold-aware dot plot otherwise. For compare_model_fit, the default is always the threshold-aware dot plot.

Usage

plot_model_fit(
  x,
  type = "default",
  metrics = NULL,
  test_mode = "all",
  verbose = TRUE,
  ...
)

Arguments

x

A model_fit or compare_model_fit object.

type

Character string indicating the plot style. Supported values are "default", "bullet", "dots", "bars", and "heatmap".

metrics

Optional character vector selecting which fit indices to plot. Supported metrics are "CFI", "TLI", "RMSEA", and "SRMR". Defaults to NULL, which uses those metrics in canonical order when they are present in the object.

test_mode

Character string controlling which test rows are plotted when the input contains multiple rows per model. Supported values are "all", "non_standard", "standard_only", and "primary". Defaults to "all".

verbose

Logical. If TRUE, non-fatal informational messages are shown when requested metrics are unavailable and dropped.

...

Reserved for future extensions. Currently ignored.

Value

A ggplot object.

See also

model_fit(), compare_model_fit(), and plot_factor_loadings().

Examples

if (requireNamespace("lavaan", quietly = TRUE)) {
  library(lavaan)
  library(psymetrics)

  hs_model <- 'visual  =~ x1 + x2 + x3
               textual =~ x4 + x5 + x6
               speed   =~ x7 + x8 + x9'

  fit_mlr <- cfa(hs_model, data = HolzingerSwineford1939, estimator = "MLR")
  fit_ulsm <- cfa(hs_model, data = HolzingerSwineford1939, estimator = "ULSM")

  single_fit <- model_fit(fit_mlr)
  plot_model_fit(single_fit)

  compared_fits <- compare_model_fit(MLR = fit_mlr, ULSM = fit_ulsm)
  plot_model_fit(compared_fits)
  plot_model_fit(compared_fits, type = "bars")
}