mirt (version 0.4.2)

wald: Wald test for mirt models

Description

Compute a Wald test given an L vector or matrix of contrasts. Requires that the information matrix already be computed (using SE = TRUE when using EM estimation).

Usage

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

Arguments

L

a coefficient matrix with dimensions nconstrasts x npars. Use pars = 'values' on the initially estimated model to obtain the parameter indicators

object

estimated object from mirt, confmirt, or multipleGroup

C

a constant vector/matrix to be compared along side L

x

an object of class 'wald'

...

additional arguments to be passed

Examples

#View parnumber index
data(LSAT7)
data <- expand.table(LSAT7)
cmodel <- confmirt.model()
   F1 = 1,4,5
   F2 = 2,3


mod <- mirt(data, cmodel, SE = TRUE)
coef(mod, allpars = TRUE)
index <- mirt(data, cmodel, pars = 'values')
index


#second factor slopes equal to 0?
L <- rep(0, 30)
L[c(7, 12)] <- 1
wald(L, mod)

#simultaniously test equal factor slopes for item 2 and 3, and 4 and 5
L <- matrix(0, 2, 30)
L[1,16] <- L[2, 7] <- 1
L[1,21] <- L[2, 12] <- -1
wald(L, mod)

#logLiklihood tests (requires estimating a new model)
mod2 <- mirt(data, cmodel, constrain = list(c(7,12), c(16,21)))
anova(mod2, mod)