inbound.pca_warper: Methods for forward (outbound) and backward (inbound) feature...

In alexanderbrenning/wiml: Interpreting machine-learning models in transformed space

Methods for forward (outbound) and backward (inbound) feature space transformation using warper objects

Description

Outbound or forward transformation refers to the mapping T from feature space into a transformed space (e.g., principal components space), and inbound or backward transformation is its inverse transformation T^{1} back into feature space.

Usage

## S3 method for class 'pca_warper'
inbound(object, wdata = object$pca$x, ...)

Arguments

object	A warper object, e.g. created by calling pca_warper() for a principal component transformation.
wdata	A data frame containing data in transformed space, i.e. coordinates with respect to principal axes.
...	Additional arguments to be passed to inbound.rotation_warper()

Details

These functions are mainly intended for internal use. The warp() and unwarp() methods are more user-oriented interfaces.

Value

A transformed data frame of class warped_df (for outbound) or data.frame (for inbound transformations).

alexanderbrenning/wiml documentation built on Sept. 29, 2023, 4:45 a.m.