Summarize a GAMM

Summarize a gam model in a clean mixed effects style.

summary_gamm(model, digits = 3)

Arguments

model	The mgcv model
digits	number of digits to display

Value

Invisibly returns a list with the variance components via extract_vc and fixed effects parameter, labeled vc and fe respectively.

Details

This displays the variance components and fixed effects from a gam model. Assumes an mgcv model of the form gam(... + s(g, bs='re')), but should work with just about any gam with a smooth term.

See also

extract_vc

Examples

library(mgcv)
library(lme4)

lmer_model <- lmer(Reaction ~ Days + (Days || Subject), data = sleepstudy)
ga_model <- gam(Reaction ~ Days + s(Subject, bs = "re") + s(Days, Subject, bs = "re"),
  data = sleepstudy,
  method = "REML"
)

summary(lmer_model)
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: Reaction ~ Days + ((1 | Subject) + (0 + Days | Subject))
#>    Data: sleepstudy
#> 
#> REML criterion at convergence: 1743.7
#> 
#> Scaled residuals: 
#>     Min      1Q  Median      3Q     Max 
#> -3.9626 -0.4625  0.0204  0.4653  5.1860 
#> 
#> Random effects:
#>  Groups    Name        Variance Std.Dev.
#>  Subject   (Intercept) 627.57   25.051  
#>  Subject.1 Days         35.86    5.988  
#>  Residual              653.58   25.565  
#> Number of obs: 180, groups:  Subject, 18
#> 
#> Fixed effects:
#>             Estimate Std. Error t value
#> (Intercept)  251.405      6.885  36.513
#> Days          10.467      1.560   6.712
#> 
#> Correlation of Fixed Effects:
#>      (Intr)
#> Days -0.184
summary_gamm(ga_model)
#> 
#> Variance components:
#>     group    effect variance     sd sd_2.5 sd_97.5 var_prop
#>   Subject Intercept  627.571 25.051 16.085  39.015    0.477
#>   Subject      Days   35.858  5.988  4.025   8.908    0.027
#>  Residual            653.582 25.565 22.792  28.676    0.496
#> 
#> 
#> Fixed Effects:
#>         Term Estimate Std. Error t value Pr(>|t|)
#>  (Intercept)  251.405      6.885  36.513    0.000
#>         Days   10.467      1.560   6.712    0.000