augdat-internals function

Augmented-data projection: Internals

The augmented-data projection makes extensive use of augmented-rows matrices and augmented-length vectors. In the following, $N$, $C_cat$, $C_lat$, $S_ref$, and $S_prj$ from help topic refmodel-init-get are used. Furthermore, let $C$ denote either $C_cat$ or $C_lat$, whichever is appropriate in the context where it is used (e.g., for ref_predfun's output, $C = C_lat$). Similarly, let $S$ denote either $S_ref$ or $S_prj$, whichever is appropriate in the context where it is used. Then an augmented-rows matrix is a matrix with $N * C$ rows in $C$

blocks of $N$ rows, i.e., with the $N$ observations nested in the $C$ (possibly latent) response categories. For ordered response categories, the $C$ (possibly latent) response categories (i.e., the row blocks) have to be sorted increasingly. The columns of an augmented-rows matrix have to correspond to the $S$ parameter draws, just like for the traditional projection. An augmented-rows matrix is of class augmat

(inheriting from classes matrix and array) and needs to have the value of $C$ stored in an attribute called ndiscrete. An augmented-length vector (class augvec) is the vector resulting from subsetting an augmented-rows matrix to extract a single column and thereby dropping dimensions.