augdat-internals | R Documentation |
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
(latent) response categories. For ordered response categories, the
C
(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 N
stored in an attribute
called nobs_orig
. 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.
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