summarize_model: Summarize a mixed model

In m-clark/mixedup: Miscellaneous functions for mixed models

Summarize a mixed model

Description

This function prints fixed effects and variance components for a mixed model.

Usage

summarize_model(
  model,
  ci = TRUE,
  show_cor_re = FALSE,
  show_cor_fe = FALSE,
  exponentiate = FALSE,
  digits = 2,
  component = NULL,
  ...
)

summarise_model(
  model,
  ci = TRUE,
  show_cor_re = FALSE,
  show_cor_fe = FALSE,
  exponentiate = FALSE,
  digits = 2,
  component = NULL,
  ...
)

Arguments

model	A supported model.
ci	Whether to include a 95% uncertainty interval for the variance components. Default is TRUE.
show_cor_re	Whether to include the correlations of the random effects. Default is FALSE.
show_cor_fe	Whether to include the correlations of the fixed effects. Default is FALSE.
exponentiate	Exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression). If TRUE, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale.
digits	Digits to display.
component	For glmmTMB objects, which of the three components 'cond', 'zi' or 'other' to select. Default is cond. Minimal testing on other options.
...	Not used at present. May allow model-specific functionality.

Details

This basically does pretty printing of the results of extract_vc() and extract_fixed_effects().

Value

Prints the variance components, fixed effects, etc. Invisibly, a list of those.

Note

Not tested yet for complicated stanreg objects like multivariate or joint models.

See Also

extract_vc(), extract_fixed_effects()

Examples

library(lme4)
library(mixedup)

lmer_mod <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
summarize_model(lmer_mod)

m-clark/mixedup documentation built on Oct. 15, 2022, 8:55 a.m.