r2glmm (version 0.1.2)

r2dt: R Squared Difference Test (R2DT). Test for a statistically significant difference in generalized explained variance between two candidate models.

Description

R Squared Difference Test (R2DT). Test for a statistically significant difference in generalized explained variance between two candidate models.

Usage

r2dt(x, y = NULL, cor = TRUE, fancy = FALSE, onesided = TRUE,
  clim = 95, nsims = 2000, mu = NULL)

Value

A confidence interval for the difference in R Squared statistics and a p-value corresponding to the null hypothesis of no difference.

Arguments

x

An R2 object from the r2beta function.

y

An R2 object from the r2beta function. If y is not specified, Ho: E[x] = mu is tested (mu is specified by the user).

cor

if TRUE, the R squared statistics are assumed to be positively correlated and a simulation based approach is used. If FALSE, the R squared are assumed independent and the difference of independent beta distributions is used. This only needs to be specified when two R squared measures are being considered.

fancy

if TRUE, the output values are rounded and changed to characters.

onesided

if TRUE, the alternative hypothesis is that one model explains a larger proportion of generalized variance. If false, the alternative is that the amount of generalized variance explained by the two candidate models is not equal.

clim

Desired confidence level for interval estimates regarding the difference in generalized explained variance.

nsims

number of samples to draw when simulating correlated non-central beta random variables. This parameter is only relevant if cor=TRUE.

mu

Used to test Ho: E[x] = mu.

Examples

library(nlme)
library(lme4)
library(r2glmm)

data(Orthodont)

# Comparing two linear mixed models
m1 = lmer(distance ~ age*Sex+(1|Subject), Orthodont)
m2 = lmer(distance ~ age*Sex+(1+age|Subject), Orthodont)

m1r2 = r2beta(model=m1,partial=FALSE)
m2r2 = r2beta(model=m2,partial=FALSE)

# Accounting for correlation can make a substantial difference.

r2dt(x=m1r2, y = m2r2, cor = TRUE)
r2dt(x=m1r2, y = m2r2, cor = FALSE)