ensemble {SSDM}

Methods to assemble multiple algorithms in an ensemble SDM

Description

This is a method to assemble several algorithms in an ensemble SDM. The function takes as inputs several S4 Algorithm.SDM class objects returned by the modelling function. The function returns an S4 Ensemble.SDM class object containing the habitat suitability map, the binary map, and the uncertainty map (based on the between-algorithm variance) and the associated evaluation tables (model evaluation, algorithm evaluation, algorithm correlation matrix and variable importance).

Usage

ensemble(
  x,
  ...,
  name = NULL,
  ensemble.metric = c("AUC"),
  ensemble.thresh = c(0.75),
  weight = TRUE,
  thresh = 1001,
  uncertainty = TRUE,
  SDM.projections = FALSE,
  cores = 0,
  verbose = TRUE,
  GUI = FALSE
)

## S4 method for signature 'Algorithm.SDM'
ensemble(
  x,
  ...,
  name = NULL,
  ensemble.metric = c("AUC"),
  ensemble.thresh = c(0.75),
  weight = TRUE,
  thresh = 1001,
  uncertainty = TRUE,
  SDM.projections = FALSE,
  cores = 0,
  verbose = TRUE,
  GUI = FALSE
)

## S4 method for signature 'Algorithm.SDM'
sum(
  x,
  ...,
  name = NULL,
  ensemble.metric = c("AUC"),
  ensemble.thresh = c(0.75),
  weight = TRUE,
  thresh = 1001,
  format = TRUE,
  verbose = TRUE,
  na.rm = TRUE
)

Arguments

x, ...

SDMs. SDMs to be assembled.

name

character. Optional name given to the final Ensemble.SDM produced (by default 'Ensemble.SDM').

ensemble.metric

character. Metric(s) used to select the best SDMs that will be included in the ensemble SDM (see details below).

ensemble.thresh

numeric. Threshold(s) associated with the metric(s) used to compute the selection.

weight

logical. If TRUE, SDMs are weighted using the ensemble metric or, alternatively, the mean of the selection metrics.

thresh

numeric. A integer value specifying the number of equal interval threshold values between 0 and 1.

uncertainty

logical. If TRUE, generates an uncertainty map and an algorithm correlation matrix.

SDM.projections

logical. If FALSE (default), the Algorithm.SDMs inside the 'sdms' slot will not contain projections (for memory saving purposes).

cores

integer. Specify the number of CPU cores used to do the computing. You can use detectCores) to automatically

verbose

logical. If set to true, allows the function to print text in the console.

GUI, format, na.rm

logical. Do not take those arguments into account (parameters for the user interface and sum function).

Details

ensemble.metric (metric(s) used to select the best SDMs that will be included in the ensemble SDM) can be chosen from among:

AUC

Area under the receiver operating characteristic (ROC) curve

Kappa

Kappa from the confusion matrix

sensitivity

Sensitivity from the confusion matrix

specificity

Specificity from the confusion matrix

prop.correct

Proportion of correctly predicted occurrences from the confusion matrix

calibration

Calibration metric (Naimi & Araujo 2016)

Value

an S4 Ensemble.SDM class object viewable with the plot.model function.

See Also

ensemble_modelling to build an ensemble SDM from multiple algorithms.

Examples

## Not run: 
# Loading data
data(Env)
data(Occurrences)
Occurrences <- subset(Occurrences, Occurrences$SPECIES == 'elliptica')

# ensemble SDM building
CTA <- modelling('CTA', Occurrences, Env, Xcol = 'LONGITUDE', Ycol = 'LATITUDE')
SVM <- modelling('SVM', Occurrences, Env, Xcol = 'LONGITUDE', Ycol = 'LATITUDE')
ESDM <- ensemble(CTA, SVM, ensemble.thresh = c(0.6))

# Results plotting
plot(ESDM)

## End(Not run)