# Spatial adjustment of environmental predictors and raster stacks

Source:`R/utils-predictors.R`

`predictor_transform.Rd`

This function allows the transformation of provided
environmental predictors (in `SpatRaster`

format). A common use case is
for instance the standardization (or scaling) of all predictors prior to
model fitting. This function works both with `SpatRaster`

as well as with
`stars`

objects.

## Usage

```
predictor_transform(
env,
option,
windsor_props = c(0.05, 0.95),
pca.var = 0.8,
method = NULL,
...
)
```

## Arguments

- env
A

`SpatRaster`

object.- option
A

`vector`

stating whether predictors should be preprocessed in any way (Options:`'none'`

,`'scale'`

,`'norm'`

,`'windsor'`

,`'windsor_thresh'`

,`'percentile'`

`'pca'`

,`'revjack'`

). See Details.- windsor_props
A

`numeric`

vector specifying the proportions to be clipped for windsorization (Default:`c(.05,.95)`

).- pca.var
A

`numeric`

value between`>0`

and`1`

stating the minimum amount of variance to be covered (Default:`0.8`

).- method
As

`'option'`

for more intuitive method setting. Can be left empty (in this case option has to be set).- ...
other options (Non specified).

## Value

Returns a adjusted `SpatRaster`

object of identical resolution.

## Details

Available options are:

`'none'`

The original layer(s) are returned.`'scale'`

This run the`scale()`

function with default settings (1 Standard deviation) across all predictors. A sensible default to for most model fitting.`'norm'`

This normalizes all predictors to a range from`0-1`

.`'windsor'`

This applies a 'windsorization' to an existing raster layer by setting the lowest, respectively largest values to the value at a certain percentage level (e.g. 95%). Those can be set via the parameter`"windsor_props"`

.`'windsor_thresh'`

Same as option 'windsor', however in this case values are clamped to a thresholds rather than certain percentages calculated on the data.`'percentile'`

This converts and bins all values into percentiles, e.g. the top 10% or lowest 10% of values and so on.`'pca'`

This option runs a principal component decomposition of all predictors (via`prcomp()`

). It returns new predictors resembling all components in order of the most important ones. Can be useful to reduce collinearity, however note that this changes all predictor names to 'PCX', where X is the number of the component. The parameter`'pca.var'`

can be modified to specify the minimum variance to be covered by the axes.`'revjack'`

Removes outliers from the supplied stack via a reverse jackknife procedure. Identified outliers are by default set to`NA`

.