wald.test {eba} R Documentation

## Testing Linear Hypotheses in Elimination-by-Aspects (EBA) Models

### Description

Tests linear hypotheses of the form Cp = 0 in elimination-by-aspects (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

eba, group.test, uscale, 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 elimination-by-aspects model

## Test whether JU, CY, and AJF have equal utility scale values
C1 <- rbind(c(0,0,0,1,-1, 0,0,0,0),
c(0,0,0,1, 0,-1,0,0,0))
wald.test(eba1, C1)

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


[Package eba version 1.10-0 Index]