StackData

Master data frame from which stacked data is created

Master

Last row contains current scale values (item.history)

item.log

Last row contains current estimates of phi

phi.log

Summing array to get rest scores and totals

pq.mat

npersons

nitems

ncat

Number of categories less 1 (i.e., unique lambdas and unique nus)

nless

ntraits

Maxnphi

PhiNames

Names of lambdas that correspond to those in Master

LambdaNames

Prepares data frame for input to 'mnlogit' for the stacked regression 
to obtain association parameters of the multidimensional LMA models
corresponding to the Nominal item response model. This function is
called from 'fit.nominal'. It generally would not run outside of 
either '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.

StackData: Prepares data for up-dating association parameters of a multidimensional nominal LMA

Description

Usage

Arguments

Value