Description Usage Arguments Value
Scaling is internal to the function 'fit.nominal', which corresponds the the nominal item response theory model. It imposes the required scaling identification constraint by transforming the conditional covariance matrix 'Phi.mat' to a conditional correlation matrix (i.e., set phi_mm=1 for all m). The inverse transformation is applied to the current category scale value estimates and these are put back into the Master data frame so that data are ready for the next iteration of the algorithm.
1 2 3 4 5 6 7 8 9 10 11 12 | Scale(
Master,
item.log,
Phi.mat,
PersonByItem,
npersons,
nitems,
ncat,
nless,
ntraits,
item.by.trait
)
|
Master |
Current Master data frame. |
item.log |
Iteration history of LogLike, lambda, and item parameters |
Phi.mat |
Current phi matrix |
PersonByItem |
inData |
npersons |
Number of persons |
nitems |
Number of items |
ncat |
Number of categories |
nless |
Number of unique nus (ncat-1) |
ntraits |
Number of (latent) dimensions |
item.by.trait |
Indicates the trait an item load on. |
Master Master frame with re-scaled scale values
Phi.mat Re-scaled matrix of association parameters
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