Description Usage Arguments Details Value Author(s) References See Also Examples
This function enables to evaluate BIOMOD.models.out and BIOMOD.EnsembleModeling.out object with presence-only evaluation methods (Boyce index and Minimal Predicted Area MPA)
1 2 | BIOMOD_presenceonly(modeling.output = NULL, EM.output = NULL,
save.output = T)
|
modeling.output |
"BIOMOD.models.out" object produced by a BIOMOD_Modeling run |
EM.output |
a "BIOMOD.EnsembleModeling.out" returned by BIOMOD_EnsembleModeling |
save.output |
logical. If TRUE (Default) the output is saved to the ".BIOMOD_DATA" folder |
'em.by' of 'BIOMOD.EnsembleModeling' must be 'PA_dataset+repet' to have an ensemble for each RUN of the 'NbRunEval' argument (BIOMOD_Modeling funtion) for evaluation. The Boyce index returns NA values for 'SRE' models because it is not possible to be calculated with binary predictions. This is also the reason why there are sometimes NA values for 'GLM' models if they don not converge.
data.frame containing evaluation scores for the evaluation metrics used for the BIOMOD_Modeling function and additional Boyce index and MPA
Frank Breiner
Engler, R., A. Guisan, and L. Rechsteiner. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology.
ecospat.boyce, ecospat.mpa, BIOMOD_Modeling, BIOMOD_EnsembleModeling
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require(PresenceAbsence)
# 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 = 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','ROC'),
SaveObj = TRUE,
rescal.all.models = FALSE,
do.full.models = FALSE)
# 4. Doing Ensemble Modelling
myBiomodEM <- BIOMOD_EnsembleModeling( modeling.output = myBiomodModelOut,
chosen.models = 'all',
em.by = 'PA_dataset+repet',
eval.metric = c('TSS'),
eval.metric.quality.threshold = c(0.7),
models.eval.meth = c('TSS','ROC'),
prob.mean = TRUE,
prob.cv = FALSE,
prob.ci = FALSE,
prob.ci.alpha = 0.05,
prob.median = FALSE,
committee.averaging = FALSE,
prob.mean.weight = TRUE,
prob.mean.weight.decay = 'proportional' )
# evaluate Biomod models with the Boyce index and MPA
pres.only.eval <- BIOMOD_presenceonly(myBiomodModelOut, myBiomodEM)
pres.only.eval$eval
## End(Not run)
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