Extract model-estimated facet interaction effects

interaction_effect_table() returns the fixed-effect interaction block estimated by fit_mfrm() when facet_interactions is supplied. These are model-estimated deviations from the additive main-effects MFRM, not the residual screening statistics returned by estimate_bias().

Usage

interaction_effect_table(fit)

Arguments

fit

An mfrm_fit object returned by fit_mfrm().

Value

A tibble with one row per interaction cell. Returns an empty tibble when the fit has no model-estimated facet interactions.

Details

mfrmr supports two-way interactions between non-person facets, for example facet_interactions = "Rater:Criterion". Each interaction matrix is identified by zero marginal sums across both participating facets, so the interaction estimates are separable from the two main effects. Positive values indicate higher-than-expected scores for the facet-level combination under the additive model; negative values indicate lower-than-expected scores.

Use this table for confirmatory model review after specifying the facet pair of substantive interest. For exploratory screening without adding parameters to the fitted model, use estimate_bias() or estimate_all_bias().

See also

fit_mfrm(), estimate_bias(), compare_mfrm()

Examples

toy <- load_mfrmr_data("example_operational")
fit <- fit_mfrm(
  toy, person = "Person", facets = c("Rater", "Criterion"),
  score = "Score", method = "MML", model = "RSM",
  facet_interactions = "Rater:Criterion",
  quad_points = 7, maxit = 30
)
head(interaction_effect_table(fit))
#> # A tibble: 6 × 10
#>   Interaction   FacetA FacetA_Level FacetB FacetB_Level Estimate     N WeightedN
#>   <chr>         <chr>  <chr>        <chr>  <chr>           <dbl> <int>     <dbl>
#> 1 Rater:Criter… Rater  R01          Crite… Content         0.130    15        15
#> 2 Rater:Criter… Rater  R02          Crite… Content        -0.123    19        19
#> 3 Rater:Criter… Rater  R03          Crite… Content        -0.382    17        17
#> 4 Rater:Criter… Rater  R04          Crite… Content        -0.308    15        15
#> 5 Rater:Criter… Rater  R05          Crite… Content         0.543    15        15
#> 6 Rater:Criter… Rater  R06          Crite… Content         0.140    13        13
#> # ℹ 2 more variables: Sparse <lgl>, Identification <chr>