Likelihood Ratio Test Between Nested Models

Performs a likelihood ratio test (LRT) between two nested models. Supports models of class lm, lmerMod, glmerMod, lme, and gls.

getLRT(fit1, fit0)

Arguments

fit1

A model object representing the more complex (full) model.

fit0

A model object representing the simpler (nested) model.

Value

A named numeric vector with:

tobs

Test statistic (twice the difference in log-likelihoods).

df

Degrees of freedom (difference in number of parameters).

p.value

P-value from the chi-squared distribution.

Examples

## lm
fit1 <- lm(mpg ~ wt + hp, data = mtcars)
fit0 <- lm(mpg ~ wt, data = mtcars)
getLRT(fit1, fit0)
#>         tobs           df      p.value 
#> 1.137709e+01 1.000000e+00 7.435551e-04 

## lmerMod
if (requireNamespace("lme4", quietly = TRUE)) {
  library(lme4)
  fit1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy, REML = FALSE)
  fit0 <- lmer(Reaction ~ 1 + (Days | Subject), sleepstudy, REML = FALSE)
  getLRT(fit1, fit0)
}
#>         tobs           df      p.value 
#> 2.353654e+01 1.000000e+00 1.225640e-06 

## glmerMod
if (requireNamespace("lme4", quietly = TRUE)) {
  library(lme4)
  data(cbpp)
  fit1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
                data = cbpp, family = binomial)
  fit0 <- glmer(cbind(incidence, size - incidence) ~ 1 + (1 | herd),
                data = cbpp, family = binomial)
  getLRT(fit1, fit0)
}
#>         tobs           df      p.value 
#> 2.560994e+01 3.000000e+00 1.151006e-05 

## lme
if (requireNamespace("nlme", quietly = TRUE)) {
  library(nlme)
  fit1 <- lme(distance ~ age + Sex, random = ~1 | Subject,
              data = Orthodont, method = "ML")
  fit0 <- lme(distance ~ age, random = ~1 | Subject,
              data = Orthodont, method = "ML")
  getLRT(fit1, fit0)
}
#>        tobs          df     p.value 
#> 8.533057005 1.000000000 0.003487534 

## gls
if (requireNamespace("nlme", quietly = TRUE)) {
  library(nlme)
  fit1 <- gls(mpg ~ wt + hp, data = mtcars, method = "ML")
  fit0 <- gls(mpg ~ wt, data = mtcars, method = "ML")
  getLRT(fit1, fit0)
}
#>         tobs           df      p.value 
#> 1.137709e+01 1.000000e+00 7.435551e-04