mopa (version 1.0.1)

extractFromPrediction: Extract values from objects or list of objects

Description

Extrac objects from lists returned by function mopaPredict.

Usage

extractFromPrediction(predictions, value)

Arguments

predictions

Listed lists of objects (e.g. as returned by mopaPredict)

value

Character pointing to the name of component/s in the list

Examples

# NOT RUN {
# 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$future[[x]], extent(-10, 10, 35, 65))
})
names(newClim) <- names(biostack$future)[1:4]
prdRS.fut <- mopaPredict(models = mods, newClim = newClim)
names(newClim)
## Extract predictions for climate projection CNRM
predsMPI <- extractFromPrediction(predictions = prdRS.fut, value = "CNRM")
spplot(predsMPI)

# }
# NOT RUN {
# 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)
## Extract predictions for species climate projection MPI
predsMPI <- extractFromPrediction(predictions = preds, value = "MPI")
spplot(predsMPI)
# }
# NOT RUN {
# }