mirt (version 1.3)

wald: Wald test for mirt models

Description

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).

Usage

wald(object, L, C = 0)
## S3 method for class 'wald':
print(x, ...)

Arguments

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

object

estimated object from mirt, bfactor, multipleGroup, or mixedmirt

C

a constant vector of population parameters to be compared along side L, where length(C) == ncol(L). By default a vector of 0's is constructed

x

an object of class 'wald'

...

additional arguments to be passed

Examples

#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 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 <- mirt.model('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)