biomod2 modelling outputs evaluation
This function will evaluate biomod2 modelling output for given metrics (e.g 'TSS', 'ROC'...) for a given dataset.
evaluate(model, data, stat, as.array=FALSE)
model |
the model you want evaluate (either |
data |
the |
stat |
vector of statistic metrics names (e.g 'TSS','ROC') you want to perform. (see |
as.array |
logical, (FALSE by default) if FALSE a list of evaluation tables is returned (one item by models). If TRUE, the output will be return under 'classical' biomod2 array objects (see |
Given model predictive score is evaluated on the new data set. It is done comparing binary transformed model predictions (on data set) to species occurrences (first column of data arg). A list of available evaluation metrics is given in BIOMOD_Modeling help file. For metrics that compared binary/binary data, a set of threshold will be test to transform continuous model prediction within binary ones. The return scores are the ones obtained for the threshold optimizing tested metric (best score).
a list or an array containing for each evaluation metric the score, the threshold considered to transform continuous data into binary ones (for all metrics excepted 'ROC') and associated sensibility and specificity.
Damien Georges
# species occurrences
DataSpecies <- read.csv(system.file("external/species/mammals_table.csv",
package="biomod2"), row.names = 1)
head(DataSpecies)
# the name of studied species
myRespName <- 'GuloGulo'
# the presence/absences data for our species
myResp <- as.numeric(DataSpecies[,myRespName])
# the XY coordinates of species data
myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")]
# Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myExpl = raster::stack( system.file( "external/bioclim/current/bio3.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio4.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio7.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio11.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio12.grd",
package="biomod2"))
# 1. Formatting Data
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
# 2. Defining Models Options using default options.
myBiomodOption <- BIOMOD_ModelingOptions()
# 3. Doing Modelisation
myBiomodModelOut <- BIOMOD_Modeling( myBiomodData,
models = c('SRE','CTA','RF'),
models.options = myBiomodOption,
NbRunEval=1,
DataSplit=80,
Yweights=NULL,
VarImport=3,
models.eval.meth = c('TSS'),
SaveObj = TRUE,
rescal.all.models = FALSE,
do.full.models = FALSE,
modeling.id='test')
# 4. Evaluate model over another dataset (here the full one)
## creation of suitable dataset
data <- cbind(GuloGulo=get_formal_data(myBiomodModelOut,'resp.var'),
get_formal_data(myBiomodModelOut,'expl.var'))
## evaluation
evaluate(myBiomodModelOut, data=data, stat=c('ROC','TSS'))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.