R: Compute Predictors from Sufficient Dimension Reduction

sdrPredict {spatstat} | R Documentation

## Compute Predictors from Sufficient Dimension Reduction

### Description

Given the result of a Sufficient Dimension Reduction method, compute the new predictors.

### Usage

      sdrPredict(covariates, B)

### Arguments

`covariates` | A list of pixel images (objects of class `"im"`).
`B` | Either a matrix of coefficients for the covariates, or the result of a call to `sdr`.

### Details

This function assumes that `sdr` has already been used to find a minimal set of predictors based on the `covariates`. The argument `B` should be either the result of `sdr` or the coefficient matrix returned as one of the results of `sdr`. The columns of this matrix define linear combinations of the `covariates`. This function evaluates those linear combinations, and returns a list of pixel images containing the new predictors.

### Value

A list of pixel images (objects of class `"im"`) with one entry for each column of `B`.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### See Also

`sdr`

### Examples

       A <- sdr(bei, bei.extra)
       Y <- sdrPredict(bei.extra, A)
       Y

[Package spatstat version 1.63-3 Index]