enmpa (version 0.2.1)

predict_selected: Predictions for the models selected after calibration

Description

Wrapper function that facilitates the prediction of those models selected as the most robust. In addition, it allows the calculation of consensus models, when more than one model are selected.

Usage

predict_selected(fitted, newdata, extrapolation_type = "E",
                 restricted_vars = NULL, type = "response", consensus = TRUE)

Value

A list with predictions of selected models on the newdata and fitted selected model(s). Consensus predictions are added if multiple selected models exits and if newdata is a SpatRaster object.

Arguments

fitted

an enmpa-classfitted models object obtained using the functions fit_selected.

newdata

a SpatRaster, data.frame, or matrix with the new data on which to predict.

extrapolation_type

(character) to indicate extrapolation type of model. Models can be transferred with three options: free extrapolation ('E'), extrapolation with clamping ('EC'), and no extrapolation ('NE'). Default = 'E'.

restricted_vars

(character) a vector containing the names of the variables that will undergo clamping or no extrapolation. For clamping, these variables are set to minimum and maximum values established for the max and min values within calibration values. For no extrapolation, the variables outside calibration limits became NA. If no specific names are provided, the value is set to NULL by default, indicating that clamping (EC) or no extrapolation (NE) will be applied to all variables. Ignore if extrapolation_type = 'E'.

type

(character) the type of prediction required. For a default binomial model the default predictions are of log-odds (probabilities on logit scale). The default, "response", returns predicted probabilities.

consensus

(logical) valid if newdata is a SpatRaster, whether to produce consensus results obtained by combining the predictions from the collection of selected models. By default consensuses are calculated using the mean, median, variance, and weighted average using the AIC weights. Default = TRUE.

Examples

# Load a fitted selected model
data(sel_fit, package = "enmpa")

# Load raster layers to be projected
env_vars <- terra::rast(system.file("extdata", "vars.tif", package = "enmpa"))

# Predictions (only one selected mode, no consensus required)
preds <- predict_selected(sel_fit, newdata = env_vars, consensus = FALSE)

# Plot prediction
terra::plot(preds$predictions)