augdat-internals: Augmented-data projection: Internals

In paasim/glmproj: Projection Predictive Feature Selection

Augmented-data projection: Internals

Description

The augmented-data projection makes extensive use of augmented-rows matrices and augmented-length vectors. In the following, N, C_{\mathrm{cat}}, C_{\mathrm{lat}}, S_{\mathrm{ref}}, and S_{\mathrm{prj}} from help topic refmodel-init-get are used. Furthermore, let C denote either C_{\mathrm{cat}} or C_{\mathrm{lat}}, whichever is appropriate in the context where it is used (e.g., for ref_predfun's output, C = C_{\mathrm{lat}}). Similarly, let S denote either S_{\mathrm{ref}} or S_{\mathrm{prj}}, whichever is appropriate in the context where it is used. Then an augmented-rows matrix is a matrix with N \cdot 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.

paasim/glmproj documentation built on April 14, 2024, 5:30 p.m.