item.gpcm: Estimates item parameters of LMA with linear restrictions on...

In pleLMA: Pseudo-Likelihood Estimation of Log-Multiplicative Association Models

Description

This function is internal to the function 'fit.gpcm' and performs the item regressions. It is a core function of the pseudo-likelihood algorithm for items of the GPCM. The function calls function 'itemGPCM.data' to create the data for input into 'mlogit', which is use to fit a conditional multinomial model for each item. The up-dated scale values are put into the Master data frame and the 'item.log' array. It generally would not run outside of 'fit.gpcm' or 'ple.lma'.

Usage

item.gpcm(
  Master,
  item.log,
  Phi.mat,
  fitem,
  TraitByTrait,
  PersonByItem,
  npersons,
  nitems,
  ncat,
  nless,
  ntraits,
  Maxnphi,
  pq.mat,
  starting.sv,
  LambdaName
)

Arguments

Master	Master data frame
item.log	History over iterations of items' log likelihood and estimates of lambda, and item parameters
Phi.mat	Starting value of matrix of association parameters (optional)
fitem	Formula for item regressions
TraitByTrait	Trait adjacency matrix (same as inTraitAdj)
PersonByItem	Same as inData
npersons	Number of persons
nitems	Number of items
ncat	Number of categories per item
nless	Number of unique lambdas and unique nus per item
ntraits	Number of latent traits
Maxnphi	Number of phi parameters to bet estimated (NULL for 1 dimensional)
pq.mat	Used to compute rest-scores and totals
starting.sv	Fixed category scores
LambdaName	Lambda names for formula for items item regressions

Value

Master Master data frame with up-dated category scores for items

item.log Up-dated history array over iterations of the algorithm of items' log likelihood and estimated lambda and alpha parameters

pleLMA documentation built on Oct. 6, 2021, 1:08 a.m.