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Plot the Estimates of a Multiple-Factor CFA

Source: R/plot_cfa_k.R
plot_cfa_k.Rd

Displays the item-level estimates of a cfa_k fit, one panel per factor, with each estimate's confidence interval. The display is built to make the equality questions behind the classical measurement structures visible: a dashed vertical line marks, per factor, either the common (equated) estimate when the plotted parameter was constrained equal, or the mean of the free estimates as an informal anchor for the question "could these plausibly be one value?". Confidence intervals that all cover the anchor are what equal loadings (or equal error variances, or equal intercepts) would look like; an interval far from it shows which item resists the constraint, and the likelihood ratio test of the two nested cfa_k() fits is the formal companion (see the examples in cfa_k).

Usage

plot_cfa_k(
  x,
  what = c("loadings", "errors", "intercepts"),
  show_equal_reference = TRUE,
  xlab = NULL,
  title = NULL,
  palette = "okabe_ito"
)

Arguments

x

A dmar_cfa_k object from cfa_k with the default output = "verbose".

what

Which parameter to display: "loadings" (default, the lambda terms), "errors" (the psi terms), or "intercepts" (the nu terms; requires a fit with the mean structure).

show_equal_reference

Logical. If TRUE (default), draw the dashed per-factor reference line described above. When the parameter was constrained equal the line is the common estimate and is always drawn.

xlab

Label for the horizontal axis. Defaults to a description of the plotted parameter.

title

Optional plot title.

palette

Character string naming the color palette. Defaults to "okabe_ito", base R's colorblind-safe Okabe-Ito palette; "tableau" is also available.

Value

A ggplot2 object.

Note

Requires ggplot2 (listed in Suggests).

See also

cfa_k for the fit; plot_ci for the general forest-style confidence interval display.

Other plotting: plot_R2(), plot_ci(), plot_equivalence(), plot_forest(), plot_irt_information(), plot_mediation_mbco(), plot_randomization_test(), plot_regions_of_significance(), plot_smd(), plot_trajectories(), plot_trajectories_fitted(), power_equivalence_md_plot()

Author

Ken Kelley kkelley@nd.edu

Examples

data(holzinger_swineford)
hs_factors <- list(
  verbal = c("t6_paragraph_comprehension",
             "t7_sentence", "t9_word_meaning"),
  deduction = c("t20_deduction", "t22_problem_reasoning",
             "t23_series_completion"))
res <- cfa_k(holzinger_swineford, hs_factors)

# Are equal loadings plausible? Compare each interval with the anchor.
plot_cfa_k(res)


# Two further displays are shown but not run here, since each draws
# another figure and the second refits the model as well. The same
# question for the error variances, the additional constraint that
# separates essentially parallel from essentially tau-equivalent:
# plot_cfa_k(res, what = "errors")
#
# After imposing the constraint, every item in a factor sits at the
# common estimate and the dashed line is that estimate rather than
# the mean of the free ones:
# res_equal <- cfa_k(holzinger_swineford, hs_factors,
#                    equal_loading = TRUE)
# plot_cfa_k(res_equal)