Computes Individual Likelihood from Classical Test Theory Estimates
Computes individual likelihood from classical test theory estimates under a unidimensional normal distribution of measurement errors.
IRTLikelihood.ctt(y, errvar, theta=NULL)
y |
Vector of observed scores |
errvar |
Vector of error variances |
theta |
Optional vector for θ grid. |
Object of class IRT.likelihood
############################################################################# # EXAMPLE 1: Individual likelihood and latent regression in CTT ############################################################################# set.seed(75) #--- simulate data N <- 2000 x1 <- stats::rnorm(N) x2 <- .7 * x1 + stats::runif(N) # simulate true score theta <- 1.2 + .6*x1 + .3 *x2 + stats::rnorm(N, sd=sqrt(.50) ) var(theta) # simulate measurement error variances errvar <- stats::runif( N, min=.6, max=.9 ) # simulate observed scores y <- theta + stats::rnorm( N, sd=sqrt( errvar) ) #--- create likelihood object like1 <- TAM::IRTLikelihood.ctt( y=y, errvar=errvar, theta=NULL ) #--- estimate latent regression X <- data.frame(x1,x2) mod1 <- TAM::tam.latreg( like=like1, Y=X ) ## Not run: #--- draw plausible values pv1 <- TAM::tam.pv( mod1, normal.approx=TRUE ) #--- create datalist datlist1 <- TAM::tampv2datalist( pv1, pvnames="thetaPV", Y=X ) #--- statistical inference on plausible values using mitools package library(mitools) datlist1a <- mitools::imputationList(datlist1) # fit linear regression and apply Rubin formulas mod2 <- with( datlist1a, stats::lm( thetaPV ~ x1 + x2 ) ) summary( mitools::MIcombine(mod2) ) ## End(Not run)
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