mirt (version 1.3)

PLCI.mirt: Compute profiled-likelihood confidence intervals

Description

Computes profiled-likelihood based confidence intervals. Supports the inclusion of prior parameter distributions as well as equality constraints. For multidimensional models, the CI's for the slopes are not estimated due to the possibility of signs flipping during estimation. In unidimensional models, the slope parameters are assumed to be greater than zero, and a lower bound is imposed to ensure that sign flipping does not occur.

Usage

PLCI.mirt(mod, alpha = 0.05, parnum = NULL)

Arguments

mod

a converged mirt model

alpha

two-tailed alpha critical level

parnum

a numeric vector indicating which parameters to estimate. Use mod2values to determine parameter numbers. If NULL, all possible parameters are used

See Also

boot.mirt

Examples

mirtCluster() #use all available cores to estimate CI's in parallel
dat <- expand.table(LSAT7)
mod <- mirt(dat, 1)

result <- PLCI.mirt(mod)
result

mod2 <- mirt(Science, 1)
result2 <- PLCI.mirt(mod2)
result2

#only estimate CI's slopes
sv <- mod2values(mod2)
parnum <- sv$parnum[sv$name == 'a1']
result3 <- PLCI.mirt(mod2, parnum=parnum)
result3