RMdimCFAPlot: Plot observed CFA fit and loadings against the simulated null

In easyRasch2: Psychometric Analysis with Rasch Measurement Theory

Plot observed CFA fit and loadings against the simulated null

Description

Returns two figures (in a list) comparing the observed one-factor CFA to the simulated null distribution from RMdimCFACutoff: a per-item standardized-loadings plot (observed marker against each item's simulated distribution and expected range, in the style of RMitemInfitPlot), and a faceted plot of the CFI / RMSEA / SRMR distributions with the observed value overlaid.

Usage

RMdimCFAPlot(simfit, data, percentile = NULL)

Arguments

simfit	The list returned by RMdimCFACutoff.
data	The item-response data the CFA was run on (the same items used for the cutoff). Required: the observed values are computed from it.
percentile	Numeric in (50, 100) or NULL. When supplied, the cutoffs and flags are recomputed at this percentile from the stored simulated distributions (no re-simulation). When NULL (default), the percentile from the original RMdimCFACutoff() call is reused.

Value

A named list of two ggplot objects:

loadings

Per-item standardized loadings: simulated distribution (dots), expected interval, and the observed loading as a diamond (red when flagged).

fit

Faceted CFI / RMSEA / SRMR simulated distributions with the observed value and cutoff overlaid.

See Also

RMdimCFACutoff, RMdimCFA

Examples

if (requireNamespace("lavaan", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("eRm", quietly = TRUE)) {
  data("raschdat1", package = "eRm")
  sim <- RMdimCFACutoff(raschdat1[, 1:8], iterations = 50,
                        parallel = FALSE, seed = 1)
  plots <- RMdimCFAPlot(sim, data = raschdat1[, 1:8])
  plots$loadings
  plots$fit
}

easyRasch2 documentation built on Sept. 13, 2026, 1:07 a.m.