ensemble | R Documentation |
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).
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
)
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 |
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). |
ensemble.metric (metric(s) used to select the best SDMs that will be included in the ensemble SDM) can be chosen from among:
Area under the receiver operating characteristic (ROC) curve
Kappa from the confusion matrix
Sensitivity from the confusion matrix
Specificity from the confusion matrix
Proportion of correctly predicted occurrences from the confusion matrix
Calibration metric (Naimi & Araujo 2016)
an S4 Ensemble.SDM class object viewable with the
plot.model
function.
ensemble_modelling
to build an ensemble SDM from
multiple algorithms.
## 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)
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