# 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"))
# Keep only points where we have info
myExpl <- raster::extract(myExpl, myRespXY)
# 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','RF'),
models.options = myBiomodOption,
NbRunEval=1,
DataSplit=70,
Yweights=NULL,
VarImport=0,
models.eval.meth = c('ROC'),
rescal.all.models = FALSE,
do.full.models = FALSE)
# 4 Projection on current environemental conditions
myBiomodProjection <- BIOMOD_Projection(modeling.output = myBiomodModelOut,
new.env = data.frame(myExpl),
proj.name = 'current',
selected.models = 'all')
# 5. Get projection under data.frame format
myProjDF <- get_predictions(myBiomodProjection, as.data.frame=TRUE)
class(myProjDF)
dim(myProjDF)
dimnames(myProjDF)
# 6. Transform data.frame into array
myProjArray <- DF_to_ARRAY(myProjDF)
class(myProjArray)
dim(myProjArray)
dimnames(myProjArray)
Run the code above in your browser using DataLab