wald.test {eba}R Documentation

Testing Linear Hypotheses

Description

Tests linear hypotheses of the form Cp = 0 in EBA models using the Wald test.

Usage

wald.test(object, C, u.scale = TRUE)

Arguments

object an object of class eba, typically the result of a call to eba
C a matrix of contrasts, specifying the linear hypotheses
u.scale logical, if TRUE the test is performed on the utility scale, if FALSE the test is performed on the EBA parameters directly

Details

The Wald test statistic,

W = (Cp)' [C cov(p) C']^{-1} (Cp),

is approximately chi-square distributed with rk(C) degrees of freedom.

C is usually of full rank and must have as many columns as there are parameters in p.

Value

C the matrix of contrasts, specifying the linear hypotheses
W the Wald test statistic
df the degrees of freedom (rk(C))
pval the p-value of the test

See Also

eba, group.test, cov.u.

Examples

data(celebrities)  # absolute choice frequencies
A <- list(c(1,10), c(2,10), c(3,10), c(4,11), c(5,11), c(6,11),
          c(7,12), c(8,12), c(9,12))  # the structure of aspects
eba1 <- eba(celebrities, A)  # fit a preference tree

## Test whether JU, CY, and AJF have equal preference scale values
C1 <- matrix(c(0,0,0,1,-1,0,0,0,0,
               0,0,0,1,0,-1,0,0,0), 2, 9, TRUE)
wald.test(eba1, C1)

## Test whether the three branch parameters are different
C2 <- matrix(c(0,0,0,0,0,0,0,0,0,1,-1,0,
               0,0,0,0,0,0,0,0,0,1,0,-1), 2, 12, TRUE)
wald.test(eba1, C2, u.scale = FALSE)

[Package eba version 1.5-6 Index]