fit.rasch {pleLMA} | R Documentation |
Fits an LMA using fixed category scores
Description
The LMA model with fixed category scores is fit by this function and the model corresponds to models in the Rasch family of item response models. The category scores can be set by either the user or the package defaults. The default category scores are equally spaced, sum to zero, and sum of squares equal 1. Scores can be set by user by specifying them in the item by category matrix of 'starting.sv'. The pseudo-likelihood algorithm only runs a single stacked regression. This functionis called from' ple.lma' but can also be run outside of the main wrapper function.
Usage
fit.rasch(
Master,
npersons,
nitems,
ncat,
nless,
Maxnphi,
pq.mat,
starting.sv,
LambdaNames,
PhiNames,
ItemNames,
LambdaName,
ntraits
)
Arguments
Master |
Master data set in long format |
npersons |
Number of persons |
nitems |
Number of items |
ncat |
Number of categories |
nless |
Number of unique Lambdas (i.e., ncat-1) |
Maxnphi |
Number of phi parameters |
pq.mat |
One dimensional array to compute rest-scores |
starting.sv |
Fixed category scores |
LambdaNames |
Names of lambda paramters in Master and formula for stacked regression |
PhiNames |
Names of association parameters |
ItemNames |
Names of items |
LambdaName |
Names of lambdas used in output |
ntraits |
Number of traits |
Value
estimates An item by parameter matrix of the maximum of the log likelihood, estimated item parameters (i.e., Lambdas), and the values of the fixed category scores.
fstack Formula for stacked regression
phi.mlogit Results from mlogit for stacked regression
estimates An item x parameter estimate matrix and fixed category scores used
Phi.mat Estimated phi parameters
mlpl.phi Value of maximum of log pseudo-likelihood function from the stacked regression
AIC Akaike information criterion for pseudo-likelihood (smaller is better)
BIC Bayesian information criterion for pseudo-likelihood (smaller is better)
Examples
#--- data(dass)
inData <- dass[1:250,c("d1", "d2", "d3", "a1","a2","a3","s1","s2","s3")]
#--- unidimensional
inTraitAdj <- matrix(1, nrow=1, ncol=1)
inItemTraitAdj <- matrix(1, nrow=9, ncol=1)
s <- set.up(inData, model.type='rasch', inTraitAdj, inItemTraitAdj)
r <- fit.rasch(s$Master, s$npersons, s$nitems, s$ncat, s$nless, s$Maxnphi,
s$pq.mat, s$starting.sv, s$LambdaNames, s$PhiNames, s$ItemNames,
s$LambdaName, s$ntraits)