mopaPredict: Model prediction

In mopa: Species Distribution MOdeling with Pseudo-Absences

Description

Model projection into a RasterStack

Usage

mopaPredict(models, newClim)

Arguments

models	Model class object (e.g. "glm") or list of model class objects, e.g. as returned by function extractFromModel.
newClim	RasterStack or list of RasterStack objects with variables for projecting

Value

RasterStack of the projected probabilities

Author(s)

M. Iturbide

References

Iturbide, M., Bedia, J., Herrera, S., del Hierro, O., Pinto, M., Gutierrez, J.M., 2015. A framework for species distribution modelling with improved pseudo-absence generation. Ecological Modelling. DOI:10.1016/j.ecolmodel.2015.05.018.

See Also

mopaTrain, extractFromPrediction

Examples

# SHORT EXAMPLE
destfile <- tempfile()
data.url <- "https://raw.githubusercontent.com/SantanderMetGroup/mopa/master/data/biostack.rda"
download.file(data.url, destfile)
load(destfile, verbose = TRUE)

## Fitted models
data(mods)
?mods

## Model prediction
newClim <- lapply(1:4, function(x){
crop(biostack$future[[x]], extent(-10, 10, 35, 65))
})

prdRS.fut <- mopaPredict(models = mods, newClim = newClim)


# FULL WORKED EXAMPLE
## Load presence data
data(Oak_phylo2)

## Load climate data
destfile <- tempfile()
data.url <- "https://raw.githubusercontent.com/SantanderMetGroup/mopa/master/data/biostack.rda"
download.file(data.url, destfile)
load(destfile, verbose = TRUE)

## Spatial reference
r <- biostack$baseline[[1]]
## Create background grid
bg <- backgroundGrid(r)

## Generate pseudo-absences
RS_random <-pseudoAbsences(xy = Oak_phylo2, background = bg$xy, 
                           exclusion.buffer = 0.083*5, prevalence = -0.5, kmeans = FALSE)
## Model training
fittedRS <- mopaTrain(y = RS_random, x = biostack$baseline, 
                      k = 10, algorithm = "glm", weighting = TRUE)
## Extract fitted models
mods <- extractFromModel(models = fittedRS, value = "model")

## Model prediction
preds <- mopaPredict(models = mods, newClim = biostack$future)

mopa documentation built on May 2, 2019, 6:47 a.m.