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,
...
)
```

- 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.

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

This basically does pretty printing of the results of `extract_vc()`

and `extract_fixed_effects()`

.

Not tested yet for complicated `stanreg`

objects like multivariate or
joint models.

```
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
```