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Plot a multigroup factor analysis

Source: R/plot.efa_group.R
plot.efa_group.Rd

Two views of an efa_group() result, selected by type:

Usage

# S3 method for class 'efa_group'
plot(x, type = c("congruence", "differences"), ...)

Arguments

x

An object of class efa_group (output from efa_group()).

type

character. Which plot to draw: "congruence" (per-factor congruence with confidence intervals) or "differences" (a per-item loading-difference heatmap).

...

Not used; for consistency with the generic.

Value

A ggplot2::ggplot object.

Details

  • "congruence" (the default) plots the matched Tucker congruence of each factor between every group pair, with a percentile bootstrap confidence interval when one was computed (b_boot > 0). The Lorenzo-Seva and ten Berge (2006) reference bands (.95 "equal", .85 "fair") are drawn so a factor's cross-group similarity can be read against them at a glance.

  • "differences" draws a heatmap of the signed cross-group loading differences (item by factor, one panel per group pair). Cells whose absolute difference reaches the salience threshold delta are outlined.

References

Lorenzo-Seva, U., and ten Berge, J. M. F. (2006). Tucker's congruence coefficient as a meaningful index of factor similarity. Methodology, 2, 57-64. doi: 10.1027/1614-2241.2.2.57

See also

Other factor analysis: efa_average(), efa_fit(), efa_group(), efa_mi(), print.efa_group()

Examples

g <- rep(c("g1", "g2"), length.out = nrow(GRiPS_raw))
mg <- efa_group(GRiPS_raw, groups = g, n_factors = 1)
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.
#> ℹ `x` is not a correlation matrix; computing correlations from the raw data.

# Per-factor congruence against the Lorenzo-Seva & ten Berge bands
plot(mg)


# Per-item cross-group loading-difference heatmap
plot(mg, type = "differences")