Report a Fitted Mixed Model

Create an easystats-style narrative report for a fitted linear mixed model.

Usage

report_mixed_model(model, ...)

Arguments

model: A fitted model object, typically from lme4::lmer(), lmerTest::lmer(), or nlme::lme().
...: Additional arguments passed to report::report().

Value

A report object returned by report::report().

Details

This helper keeps the report package optional. It checks that a fitted model was supplied, verifies that report is installed, and then delegates the model interpretation to report::report(). This provides a stable package-level entry point for readers who want easystats-style interpretation of the fitted mixed models used throughout the book examples.

The helper does not change the fitted model, refit the model, or alter any estimates. It only formats and interprets the model object produced by the modelling package.

References

Duchateau, L., Janssen, P., and Rowlands, G. J. (1998). Linear Mixed Models: An Introduction with Applications in Veterinary Research. International Livestock Research Institute.

See utils::citation("report") for the citation for the optional easystats reporting package.

Examples

if (requireNamespace("lme4", quietly = TRUE) &&
    requireNamespace("report", quietly = TRUE)) {
  data(ex127, package = "VetResearchLMM")
  fit <- lme4::lmer(Ww ~ 1 + (1 | sire), data = ex127, REML = TRUE)
  report_mixed_model(fit)
}
#>  We fitted a constant (intercept-only) linear mixed model (estimated using REML
#> and nloptwrap optimizer) to predict Ww (formula: Ww ~ 1). The model included
#> sire as random effect (formula: ~1 | sire). The model's intercept is at 13.97
#> (95% CI [12.47, 15.47], t(40) = 18.79, p < .001).
#> 
#> Standardized parameters were obtained by fitting the model on a standardized
#> version of the dataset. 95% Confidence Intervals (CIs) and p-values were
#> computed using a Wald t-distribution approximation.