predData.transform: Performs SCCA transformation of environmental data

In steppebird/sparsegdm: Package for performing sparse generalized dissimilarity model including tools for gdm

Description

This function transforms a predictor dataset into sparse canonical components, based on the sparse canonical vectors extracted by function sgdm.best using output = "v".

The input data can either be provided as dataframe or raster object, which must have the same number of predictors as used for deriving the sparse canonical vectors. The output data will be delivered in the same format (dataframe or raster object) as the input.

If predictor dataset is a raster object, this function requires the previous instalation of the raster package.

For more details relating to "predData" and "spData" data formats, check gdm package.

Usage

predData.transform(predData, v)

Arguments

predData	Predictor dataset (predData format) or as a raster object.
v	Sparse canonical vectors as extracted by sgdm.best using output = "v".

Value

Returns environmental data transformed into sparse canonical components for further use with GDM. This dataset is delivered in the same format (data frame or raster object) as the input data.

steppebird/sparsegdm documentation built on May 16, 2019, 2:55 a.m.