Identify log-multiplicative association scores from over-parameterized gnm models.
# S3 method for rc
assoc(model, weighting = c("marginal", "uniform", "none"),
rowsup = NULL, colsup = NULL, ...)# S3 method for rc.symm
assoc(model, weighting = c("marginal", "uniform", "none"),
rowsup = NULL, colsup = NULL, ...)
# S3 method for hmskew
assoc(model, weighting = c("marginal", "uniform", "none"),
rowsup = NULL, colsup = NULL, ...)
# S3 method for yrcskew
assoc(model, weighting = c("marginal", "uniform", "none"), ...)
# S3 method for rcL
assoc(model, weighting = c("marginal", "uniform", "none"), ...)
# S3 method for rcL.symm
assoc(model, weighting = c("marginal", "uniform", "none"), ...)
# S3 method for rcL.trans
assoc(model, weighting = c("marginal", "uniform", "none"), ...)
# S3 method for hmskewL
assoc(model, weighting = c("marginal", "uniform", "none"), ...)
# S3 method for rcL.trans.symm
assoc(model, weighting = c("marginal", "uniform", "none"), ...)
the weights to be used when normalizing scores (see ‘Details’).
a matrix with the same columns as the model data giving supplementary (passive) rows to include in the result.
a matrix with the same rows as the model data giving supplementary (passive) columns to include in the result.
currently unused.
An assoc object with the following components:
The intrisic association parameters, one per dimension.
Row scores, normalized so that their (weighted) sum is 0, their (weighted) sum of squares is 1, and their (weighted) cross-dimensional correlation is null.
Column scores, normalized so that their (weighted) sum is 0, their (weighted) sum of squares is 1, and their (weighted) cross-dimensional correlation is null.
The name of the weighting method used, reflected by row.weights
and col.weights.
The row weights used for the identification of scores, as specified by the
weighting argument.
The column weights used for the identification of scores, as specified by the
weighting argument.
These functions extract parameters from gnm log-multiplicative models
and make them identifiable by imposing the required constraints on them. The general pattern
is that row and column scores are separately centered around 0 and scaled so that they sum to 1,
and so that their cross-dimensional correlation is null. From this operation result two series of
scores (rows and columns) plus an intrinsic association coefficient (phi) for each dimension.
Most users do not need to call these directly, but they are still made public since they may be
useful for advanced uses, notably when combining log-multiplicative association components with
other model specifications. assoc can be used to identify the scores, the rest of the
coefficients being extracted manually by the caller.