Glance at a MAIHDA model or analysis

glance methods that return the MAIHDA headline as a one-row tibble: the variance partition coefficient (VPC/ICC), and – for a maihda_analysis – the proportional change in variance (PCV), plus the additive/interaction shares and the discriminatory accuracy (AUC, MOR) for a binomial outcome. The layout is uniform across the lme4, brms, WeMix and ordinal engines. No other package emits this row from the underlying fit, because PCV needs the null+adjusted pair that only a maihda_analysis holds.

Usage

# S3 method for class 'maihda_summary'
glance(x, ...)

# S3 method for class 'maihda_model'
glance(x, ...)

# S3 method for class 'maihda_analysis'
glance(x, ...)

Arguments

x

A maihda_summary, maihda_model, or maihda_analysis.

...

Unused, for S3 consistency.

Value

A one-row tibble. glance.maihda_analysis adds pcv (with pcv.conf.low/pcv.conf.high when bootstrapped or from a brms posterior), the adjusted-model auc.adjusted, nobs, family and mode to the columns produced for a single summary.

See also

maihda_tidiers for the per-estimate tidy() methods.

Examples

data("maihda_health_data")
a <- maihda(BMI ~ Age + Gender + Race + Education + (1 | Gender:Race:Education),
            data = maihda_health_data)
glance(a)
#> # A tibble: 1 × 16
#>      vpc vpc.conf.low vpc.conf.high   pcv pcv.conf.low pcv.conf.high
#>    <dbl>        <dbl>         <dbl> <dbl>        <dbl>         <dbl>
#> 1 0.0627           NA            NA 0.766           NA            NA
#> # ℹ 10 more variables: additive.share <dbl>, interaction.share <dbl>,
#> #   auc <dbl>, auc.adjusted <dbl>, mor <dbl>, n_strata <int>, nobs <int>,
#> #   engine <chr>, family <chr>, mode <chr>