Description Usage Arguments Value
Scaling is internal to the function 'fit.gpcm', which fits the GPCM version of the LMA. It imposes the required scaling identification constraint by transforming the conditional covariance matrix 'Phi.mat' to a conditional correlation matrix. The inverse transformation is applied to the current estimates of the slope or 'a' parameters. Category scale values are recomputed using the re-scale slopes (i.e., nu= a*x) and these are put back into the Master data set so that data are ready for the next iteration of the algorithm.
1 2 3 4 5 6 7 8 9 10 11 12 13 | ScaleGPCM(
Master,
item.log,
Phi.mat,
PersonByItem,
npersons,
nitems,
ncat,
nless,
ntraits,
starting.sv,
item.by.trait
)
|
Master |
Master/main data set |
item.log |
Iteration history array, last row are current parameters |
Phi.mat |
Current phi matrix |
PersonByItem |
inData (response patterns) |
npersons |
Number of persons |
nitems |
Number of items |
ncat |
Number of categories |
nless |
Number of unique lambdas (ncat-1) |
ntraits |
Number of latent traits |
starting.sv |
Matrix of fixed category scores (nitems x ncat) |
item.by.trait |
Object that indicates which trait item loads on |
Master Master data set with re-scaled scale values
Phi.mat Re-scaled matrix of association parameters
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