extractFromModel: Extrac objects from lists returned by function 'mopaTrain'
In mopa: Species Distribution MOdeling with Pseudo-Absences

Description Usage Arguments Details Value Author(s) Examples

Extract values returned by function mopaTrain

1 2	extractFromModel(models, value = c("model", "auc", "kappa", "tss", "fold.models", "ObsPred"))

`models`	Object returned by `mopaTrain`.
`value`	Any character of the following: "model", "auc", "kappa", "tss", "fold.models", "ObsPred"

ObsPred allows to calculate further accuracy measures.

Depending on the specified value:

`"model"`	fitted model using all data for training
`"auc"`	AUC statistic in the cross validation
`"kappa"`	kappa statistic in the cross validation
`"tss"`	true skill statistic in the cross validation
`"fold.models"`	fitted model with partitioned data
`"ObsPred"`	Observed and prediced (cross model prediction) values

M. Iturbide

## 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")
## Extract observed and predicted values
ObsPred <- extractFromModel(models = fittedRS, value = "ObsPred")