fsRDA: Reduce Dimensions by RDA

In tpq/exprso: Rapid Deployment of Machine Learning Algorithms

Description

fsRDA runs an RDA using the rda function from the vegan package, partialling out colBy. The RDA model is saved and deployed automatically by the predict method during test set validation.

Usage

fsRDA(object, top = 0, colBy = NULL)

Arguments

object	An ExprsArray object to undergo feature selection.
top	A numeric scalar or character vector. A numeric scalar indicates the number of top features that should undergo feature selection. A character vector indicates specifically which features by name should undergo feature selection. Set top = 0 to include all features. A numeric vector can also be used to indicate specific features by location, similar to a character vector.
colBy	A character vector. Lists the columns in @annot to use as the constraining matrix. Passed to vegan::rda. Optional argument. Skip with colBy = NULL.

Details

When colBy is provided, it serves as the constraining matrix. However, fsRDA always returns the unconstrained scores. As such, fsRDA effectively partials out the contribution of colBy to the training set, then uses this rule to partial out the contribution to the test set too.

Value

Returns an ExprsArray object.

tpq/exprso documentation built on July 27, 2019, 8:44 a.m.