sdrPredict

0th

Percentile

Compute Predictors from Sufficient Dimension Reduction

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

Keywords
spatial, nonparametric
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.

See Also

sdr

Aliases
  • sdrPredict
Examples
# NOT RUN {
   A <- sdr(bei, bei.extra)
   Y <- sdrPredict(bei.extra, A)
   Y
# }
Documentation reproduced from package spatstat, version 1.57-1, License: GPL (>= 2)

Community examples

Looks like there are no examples yet.