ItemData

Master

The item for which scale values are being up-dated

ItemID

Current estimate conditional covariance matrix
(i.e., association paramters)

Phi.mat

npersons

nitems

ntraits

ncat

Number of unique lambdas and unique nus

nless

TraitByTrait

One dimemsinal array used to get rest and totals scores

pq.mat

Name of lambdas for in item regression

LambdaName

NuName

This function creates a data frame, 'item data', to be used in the item
regressions for nominal models. It computes weighted rest scores and totals,
including correlated traits. This function is internal to 'ItemLoop' and
it is unlikely to be run outside of 'fit.nominal' or 'ple.lma'.

Log-multiplicative association models (LMA) are
models for cross-classifications of categorical variables
where interactions are represented by products of category
scale values and an association parameter. Maximum
likelihood estimation (MLE) fails for moderate to large
numbers of categorical variables. The 'pleLMA' package
overcomes this limitation of MLE by using pseudo-likelihood
estimation to fit the models to small or large
cross-classifications dichotomous or multi-category variables.
Originally proposed by Besag (1974,
<doi:10.1111/j.2517-6161.1974.tb00999.x>), pseudo-likelihood
estimation takes large complex models and breaks it down
into smaller ones. Rather than maximizing the likelihood
of the joint distribution of all the variables, a
pseudo-likelihood function, which is the product likelihoods
from conditional distributions, is maximized. LMA models can
be derived from a number of different frameworks including
(but not limited to) graphical models and uni-dimensional
and multi-dimensional item response theory models. More
details about the models and estimation can be found in
the vignette.

ItemData: Prepares data for up-dating scale value parameters of nominal model

Description

Usage

Arguments

Value