wald.test {eba} | R Documentation |
Tests linear hypotheses of the form Cp = 0 in EBA models using the Wald test.
wald.test(object, C, u.scale = TRUE)
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 u-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 (rk(C)) |
pval |
the p-value of the test |
eba
, group.test
, 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 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)