Calculates the ranking proportions from the fold-wise predictor rankings in a cross-validation (CV) with fold-wise searches. For a given predictor \(x\) and a given submodel size \(j\), the ranking proportion is the proportion of CV folds which have predictor \(x\) at position \(j\) of their predictor ranking. While these ranking proportions are helpful for investigating variability in the predictor ranking, they can also be cumulated across submodel sizes. The cumulated ranking proportions are more helpful when it comes to model selection.
cv_proportions(object, ...)
# S3 method for ranking
cv_proportions(object, cumulate = FALSE, ...)
# S3 method for vsel
cv_proportions(object, ...)For cv_proportions.ranking(): an object of class ranking
(returned by ranking()). For cv_proportions.vsel(): an object of class
vsel (returned by varsel() or cv_varsel()) that ranking() will be
applied to internally before then calling cv_proportions.ranking().
For cv_proportions.vsel(): arguments passed to ranking.vsel()
and cv_proportions.ranking(). For cv_proportions.ranking(): currently
ignored.
A single logical value indicating whether the ranking
proportions should be cumulated across increasing submodel sizes (TRUE)
or not (FALSE).
A numeric matrix containing the ranking proportions. This matrix has
nterms_max rows and nterms_max columns, with nterms_max as specified
in the (possibly implicit) ranking() call. The rows correspond to the
submodel sizes and the columns to the predictor terms (sorted according to
the full-data predictor ranking). If cumulate is FALSE, then the
returned matrix is of class cv_proportions. If cumulate is TRUE, then
the returned matrix is of classes cv_proportions_cumul and
cv_proportions (in this order).
Note that if cumulate is FALSE, then the values in the returned matrix
only need to sum to 1 (column-wise and row-wise) if nterms_max (see
above) is equal to the full model size. Likewise, if cumulate is TRUE,
then the value 1 only needs to occur in each column of the returned
matrix if nterms_max is equal to the full model size.
The cv_proportions() function is only applicable if the ranking object
includes fold-wise predictor rankings (i.e., if it is based on a vsel object created by cv_varsel() with validate_search = TRUE). If the
ranking object contains only a full-data predictor ranking (i.e., if it
is based on a vsel object created by varsel() or by cv_varsel(), but
the latter with validate_search = FALSE), then an error is thrown because
in that case, there are no fold-wise predictor rankings from which to
calculate ranking proportions.
# For an example, see `?plot.cv_proportions`.