extract_vc: Extract variance components

In m-clark/gammit: Using Generalized Additive Mixed Models

Description

This function extracts the variance components from a gam or bam object, where one is using the associated functions from the mgcv package for mixed models.

Usage

extract_vc(model, ci_level = 0.95, ci_scale = "sd", digits = 3)

Arguments

model	A gam or bam model
ci_level	Level for the confidence interval. Must be between 0 and 1.
ci_scale	Whether the confidence interval should be on the variance or standard deviation scale.
digits	Rounding for the output.

Details

This is essentially a pretty way to print gam.vcomp. Note that if you do something like a random slope, the gam approach does not estimate the intercept-slope correlation, so none will be printed. Should work fine on standard smooth terms also, but the model must be estimated with method = REML or method = ML. This is the default for non-exponential families, but otherwise you'll have to change the value or will receive an error. Have not tested much with interactions, using by, etc., but it has worked on some default mgcv examples.

Value

A tibble or data frame with the standard deviation, its lower and upper bounds, the variance, and the relative proportion of total variance for each variance component.

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"
)

VarCorr(lmer_model)
extract_vc(ga_model)

m-clark/gammit documentation built on Oct. 22, 2020, 6:53 p.m.