lmerTest (version 2.0-33)

calcSatterth: F-test based on the Satterthwaite's approximation for denominator degrees of freedom.

Description

Produces a list with the values for an approximate F-test based on the Satterthwaite's approximation.

Usage

calcSatterth(model, L)

Arguments

model

linear mixed effects model (lmer object).

L

hypothesis contrast matrix or a vector

…

other potential arguments.

Value

A list with the results from the F test

denom

numeric. Denominator degrees of freedom, calculated with the Satterthwaite's approximation

Fstat

numeric. F statistic

pvalue

numeric. p-value of the corresponding F test

ndf

numeric. Numerator degrees of freedom

Details

F test for the null hypothesis \(H_0: L \beta = 0\), where \(\beta\) is a vector of the same length as fixef(model)

References

Schaalje G.B., McBride J.B., Fellingham G.W. 2002 Adequacy of approximations to distributions of test Statistics in complex mixed linear models

See Also

anova

Examples

# NOT RUN {
## import lme4 package and lmerTest package
library(lmerTest)

## specify lmer model for the sleepstudy data from the lme4 package
m <- lmer(Reaction ~ Days + (1 + Days|Subject), sleepstudy)


L <- cbind(0,1) ## specify contrast vector
calcSatterth(m, L) ## calculate F test

## specify model for the ham data
m.ham <- lmer(Informed.liking ~ Product + (1|Consumer), data = ham)

## specify contrast vector for testing product effect
L <- matrix(0, ncol = 4, nrow = 3)
L[1,2] <- L[2,3] <- L[3,4] <- 1
calcSatterth(m.ham, L)

## by using anova function we get the same result
anova(m.ham)
# }