Residual PCA and Reporting
pca <- analyze_residual_pca(diag_pca, mode = "both")
plot_residual_pca(pca, mode = "overall", plot_type = "scree")
data("mfrmr_example_bias", package = "mfrmr")
bias_df <- mfrmr_example_bias
fit_bias <- fit_mfrm(
bias_df,
person = "Person",
facets = c("Rater", "Criterion"),
score = "Score",
method = "MML",
model = "RSM",
quad_points = 7
)
diag_bias <- diagnose_mfrm(fit_bias, residual_pca = "none")
bias <- estimate_bias(fit_bias, diag_bias, facet_a = "Rater", facet_b = "Criterion")
fixed <- build_fixed_reports(bias)
apa <- build_apa_outputs(fit_bias, diag_bias, bias_results = bias)
mfrm_threshold_profiles()
#> $profiles
#> $profiles$strict
#> $profiles$strict$n_obs_min
#> [1] 200
#>
#> $profiles$strict$n_person_min
#> [1] 50
#>
#> $profiles$strict$low_cat_min
#> [1] 15
#>
#> $profiles$strict$min_facet_levels
#> [1] 4
#>
#> $profiles$strict$misfit_ratio_warn
#> [1] 0.08
#>
#> $profiles$strict$missing_fit_ratio_warn
#> [1] 0.15
#>
#> $profiles$strict$zstd2_ratio_warn
#> [1] 0.08
#>
#> $profiles$strict$zstd3_ratio_warn
#> [1] 0.03
#>
#> $profiles$strict$expected_var_min
#> [1] 0.3
#>
#> $profiles$strict$pca_first_eigen_warn
#> [1] 1.5
#>
#> $profiles$strict$pca_first_prop_warn
#> [1] 0.1
#>
#>
#> $profiles$standard
#> $profiles$standard$n_obs_min
#> [1] 100
#>
#> $profiles$standard$n_person_min
#> [1] 30
#>
#> $profiles$standard$low_cat_min
#> [1] 10
#>
#> $profiles$standard$min_facet_levels
#> [1] 3
#>
#> $profiles$standard$misfit_ratio_warn
#> [1] 0.1
#>
#> $profiles$standard$missing_fit_ratio_warn
#> [1] 0.2
#>
#> $profiles$standard$zstd2_ratio_warn
#> [1] 0.1
#>
#> $profiles$standard$zstd3_ratio_warn
#> [1] 0.05
#>
#> $profiles$standard$expected_var_min
#> [1] 0.2
#>
#> $profiles$standard$pca_first_eigen_warn
#> [1] 2
#>
#> $profiles$standard$pca_first_prop_warn
#> [1] 0.1
#>
#>
#> $profiles$lenient
#> $profiles$lenient$n_obs_min
#> [1] 60
#>
#> $profiles$lenient$n_person_min
#> [1] 20
#>
#> $profiles$lenient$low_cat_min
#> [1] 5
#>
#> $profiles$lenient$min_facet_levels
#> [1] 2
#>
#> $profiles$lenient$misfit_ratio_warn
#> [1] 0.15
#>
#> $profiles$lenient$missing_fit_ratio_warn
#> [1] 0.3
#>
#> $profiles$lenient$zstd2_ratio_warn
#> [1] 0.15
#>
#> $profiles$lenient$zstd3_ratio_warn
#> [1] 0.08
#>
#> $profiles$lenient$expected_var_min
#> [1] 0.1
#>
#> $profiles$lenient$pca_first_eigen_warn
#> [1] 3
#>
#> $profiles$lenient$pca_first_prop_warn
#> [1] 0.2
#>
#>
#>
#> $pca_reference_bands
#> $pca_reference_bands$eigenvalue
#> critical_minimum caution common strong
#> 1.4 1.5 2.0 3.0
#>
#> $pca_reference_bands$proportion
#> minor caution strong
#> 0.05 0.10 0.20
#>
#>
#> attr(,"class")
#> [1] "mfrm_threshold_profiles" "list"
vis <- build_visual_summaries(fit_bias, diag_bias, threshold_profile = "standard")
vis$warning_map$residual_pca_overall
#> [1] "Threshold profile: standard (PC1 EV >= 2.0, variance >= 10%)."
#> [2] "Heuristic reference bands: EV >= 1.4 (critical minimum), >= 1.5 (caution), >= 2.0 (common), >= 3.0 (strong); variance >= 5% (minor), >= 10% (caution), >= 20% (strong)."
#> [3] "Current exploratory PC1 checks: EV>=1.5:Y, EV>=2.0:Y, EV>=3.0:Y, Var>=10%:Y, Var>=20%:Y."
#> [4] "Overall residual PCA PC1 exceeds the current heuristic eigenvalue band (3.22)."
#> [5] "Overall residual PCA PC1 explains 20.1% variance."The same example_bias dataset also carries a
Group variable so DIF-oriented examples can show a non-null
pattern instead of a fully clean result. It can be loaded either with
load_mfrmr_data("example_bias") or
data("mfrmr_example_bias", package = "mfrmr").
