pleLMA (version 0.2.1)

theta.estimates: Computes estimates of theta (values on latent trait(s)) for all LMA models

Description

The final estimates of the item scale values and the conditional covariance matrix (i.e, Phi.mat) are used to compute values on latent traits for each individual or case. The estimated thetas are the (conditinal) mean values of response patterns. The correlations between the estimated thetas equal the marginal correlations.

Usage

theta.estimates(inData, model.fit)

Arguments

inData

Matrix of response patterns

model.fit

Object containing output from running ple.lma

Value

theta.est A person by trait matrix of values on the latent traits

Examples

# NOT RUN {
 data(dass)
 inData <- dass[1:250,c("d1", "d2", "d3", "a1","a2","a3","s1","s2","s3")]
 inTraitAdj  <- matrix(1, nrow=1, ncol=1)
 inItemTraitAdj <- matrix(1, nrow=9, ncol=1)

 r1 <- ple.lma(inData, model.type="rasch", inItemTraitAdj, inTraitAdj)
 theta.r1 <- theta.estimates(inData, r1)

# }
# NOT RUN {
g1 <- ple.lma(inData, model.type="gpcm", inItemTraitAdj, inTraitAdj)
theta.g1 <- theta.estimates(inData, g1)

n1 <- ple.lma(inData, model.type="nominal", inItemTraitAdj,inTraitAdj)
theta.n1 <- theta.estimates(inData, n1)
# }
# NOT RUN {
# }