Summarize a mixed model

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

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.

Value

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

Details

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

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)
#> Computing profile confidence intervals ...
#> 
#> Variance Components:
#>     Group    Effect Variance    SD SD_2.5 SD_97.5 Var_prop
#>   Subject Intercept   612.10 24.74  14.38   37.72     0.47
#>   Subject      Days    35.07  5.92   3.80    8.75     0.03
#>  Residual             654.94 25.59  22.90   28.86     0.50
#> 
#> Fixed Effects:
#>       Term  Value   SE     t P_value Lower_2.5 Upper_97.5
#>  Intercept 251.41 6.82 36.84    0.00    238.03     264.78
#>       Days  10.47 1.55  6.77    0.00      7.44      13.50