ItemGPCM.data

Data frame of all data in long format

Master

Specifies the item for which a data frame is created to be
input into an item regression

ItemID

Starting value of matrix of association parameters

Phi.mat

Trait by trait adjacency matrix (same as inTraitAdj)

TraitByTrait

pq.mat

Matrix of item category scores that are fixed

starting.sv

npersons

nitems

ncat

nless

ntraits

Names for lambda for item regression

LambdaName

This function creates a data frame, 'gpcm.item.data', to be used in the item
regressions of LMA models where category scales values are fixed. Sets up data
for up-dating alpha parameters of the LMA that corresponds to the GPCM. This
function is internal to 'item.gpcm' and it is unlikely to be run outside of
'fit.gpcm' 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.

ItemGPCM.data: Creates data frame up-dating phi parameters of the gpcm.

Description

Usage

Arguments

Value