| wald {mirt} | R Documentation |
Compute a Wald test given an L vector or matrix of numeric contrasts. Requires that the
model information matrix be computed (including SE = TRUE when using the EM method). Use
wald(model) to observe how the information matrix columns are named, especially if
the estimated model contains constrained parameters (e.g., 1PL).
wald(object, L, C = 0)
object |
estimated object from |
L |
a coefficient matrix with dimensions nconstrasts x npars. Omitting this value will return the column names of the information matrix used to identify the (potentially constrained) parameters |
C |
a constant vector of population parameters to be compared along side L, where
|
Phil Chalmers rphilip.chalmers@gmail.com
## Not run:
#View parnumber index
data(LSAT7)
data <- expand.table(LSAT7)
mod <- mirt(data, 1, SE = TRUE)
coef(mod)
# see how the information matrix relates to estimated parameters, and how it lines up
# with the parameter index
(infonames <- wald(mod))
index <- mod2values(mod)
index[index$est, ]
#second item slope equal to 0?
L <- matrix(0, 1, 10)
L[1,3] <- 1
wald(mod, L)
#simultaneously test equal factor slopes for item 1 and 2, and 4 and 5
L <- matrix(0, 2, 10)
L[1,1] <- L[2, 7] <- 1
L[1,3] <- L[2, 9] <- -1
L
wald(mod, L)
#logLiklihood tests (requires estimating a new model)
cmodel <- 'theta = 1-5
CONSTRAIN = (1,2, a1), (4,5, a1)'
mod2 <- mirt(data, cmodel)
#or, eqivalently
#mod2 <- mirt(data, 1, constrain = list(c(1,5), c(13,17)))
anova(mod2, mod)
## End(Not run)