AddRandomUniformPredictors: Process module: AddRandomUniformPredictors

In zoonproject/modules: Species Distribution Modelling Code Modules for the ZOON Package

Description

Process module which adds a random uniform covariate to the dataset. This new covariate can be scaled to an existing covariate.

Usage

AddRandomUniformPredictors(.data, name = "RandUnif", scaleTo = NULL)

Arguments

.data	Internal parameter, do not use in the workflow function. .data is a list of a data frame and a raster object returned from occurrence modules and covariate modules respectively. .data is passed automatically in workflow from the occurrence and covariate modules to the process module(s) and should not be passed by the user.
name	Optional argument specifying the name of the new covariate layer. If not specified, it will automatically named.
scaleTo	Optional argument specifying the name of an existing covariate layer which the new random uniform covariate will be scaled to.

Value

a Raster object with the appended random uniform covariate.

Version

1.01

Date submitted

2016-06-15

Data type

presence-only, presence/absence, abundance, proportion

Author(s)

James Campbell, [e-mail]

See Also

Other process: BackgroundAndCrossvalid, Background, Bootstrap, CarolinaWrenValidation, Clean, Crossvalidate, JitterOccurrence, LonLatToCovariates, MESSMask, NoProcess, OneHundredBackground, OneThousandBackground, PartitionDisc, RemoveNAs, StandardiseCov, SubsampleOccurrence, TargetGroupBackground, Transform, addInteraction, spThin

Examples

work1 <- workflow(occurrence = UKAnophelesPlumbeus,
   covariate  = UKAir,
   process    = Chain(OneHundredBackground,
                   AddRandomUniformPredictors(scaleTo = 'layer', name = 'Random.layer'),
                   AddRandomUniformPredictors(scaleTo = 'layer'),
                   AddRandomUniformPredictors),
   model      = LogisticRegression,
   output     = PerformanceMeasures)
   
### Display resulting covariate maps from each workflow
spplot(work1$process.output[[1]]$ras$layer)
spplot(work1$process.output[[1]]$ras$Random.layer.1)
spplot(work1$process.output[[1]]$ras$RandUnif.1)
spplot(work1$process.output[[1]]$ras$RandUnif.2)

### Show resulting model
work1$model.output[[1]]$model$model

zoonproject/modules documentation built on May 4, 2019, 11:25 p.m.