wald.test {eba} | R Documentation |
Tests linear hypotheses of the form Cp = 0
in elimination-by-aspects
(EBA) models using the Wald test.
wald.test(object, C, u.scale = TRUE)
object |
an object of class |
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 |
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
.
C |
the matrix of contrasts, specifying the linear hypotheses |
W |
the Wald test statistic |
df |
the degrees of freedom ( |
pval |
the p-value of the test |
eba
, group.test
, uscale
,
cov.u
.
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)