# sdrPredict

From spatstat v1.60-1
by Adrian Baddeley

##### 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

##### Examples

```
# NOT RUN {
A <- sdr(bei, bei.extra)
Y <- sdrPredict(bei.extra, A)
Y
# }
```

*Documentation reproduced from package spatstat, version 1.60-1, License: GPL (>= 2)*

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