ecospat (version 2.0)

ecospat.binary.model: Generate Binary Models

Description

Generate a binary map from a continuous model prediction (i.e., values from 0 to 1000).

Usage

ecospat.binary.model (Pred, Sp.occ.xy, Percentage)

Arguments

Pred

Predicted suitability values (from 0 to 1000). A RasterStack object containing models predictions ( Output from biomod2 in raster format).

Sp.occ.xy

Ocurrences of the species. A dataframe object with two columns: longitude and latitude. Coordinate systems other than longitude and latitude can be used, for example "x" and "y".

Percentage

The percentage of omission error used to generate the binary model.

Value

Details

This function generates a binary model prediction (presence/absence) from an original model (continuous values from 0 to 1000) applying a threshold of maximum acceptable error of false negatives (i.e. percentage of the presence predicted as absences, omission error).

References

Fielding, A.H. and J.F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24: 38-49.

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, 41, 263-274.

Guisan, A., O. Broennimann, R. Engler, M. Vust, N.G. Yoccoz, A. Lehmann and N.E. Zimmermann. 2006. Using niche-based models to improve the sampling of rare species. Conservation Biology, 20, 501-511.

Examples

## Not run: 
# 
# #Run biomod2 to produce a model prediction 
# DataSpecies <- read.csv(system.file("external/species/mammals_table.csv", package="biomod2"))
# 
# 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")]
# # load the environmental raster layers (could be .img, ArcGIS
# # rasters or any supported format by the raster package)
# # Environmental variables extracted from Worldclim (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"))
# 
# 
# myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
#                                      expl.var = myExpl,
#                                      resp.xy = myRespXY,
#                                      resp.name = myRespName)
# myBiomodData
# myBiomodOption <- BIOMOD_ModelingOptions()
# 
# myBiomodModelOut <- BIOMOD_Modeling(
#   myBiomodData,
#   models = c('GLM'),
#   models.options = myBiomodOption,
#   NbRunEval=1,
#   DataSplit=80,
#   Prevalence=0.5,
#   VarImport=3,
#   models.eval.meth = c('TSS','ROC'),
#   SaveObj = TRUE,
#   rescal.all.models = TRUE,
#   do.full.models = FALSE,
#   modeling.id = paste(myRespName,"FirstModeling",sep=""))
# 
# myBiomodModelOut
# myBiomodModelEval <- get_evaluations(myBiomodModelOut)
# 
# myBiomodEM <- BIOMOD_EnsembleModeling(
#   modeling.output = myBiomodModelOut,
#   chosen.models = 'all',
#   em.by='all',
#   eval.metric = c('TSS'),
#   eval.metric.quality.threshold = c(0.7),
#   prob.mean = TRUE,
#   prob.cv = TRUE,
#   prob.ci = TRUE,
#   prob.ci.alpha = 0.05,
#   prob.median = TRUE,
#   committee.averaging = TRUE,
#   prob.mean.weight = TRUE,
#   prob.mean.weight.decay = 'proportional' )
# 
# myBiomodEM
# 
# myBiomodProj <- BIOMOD_Projection(
#   modeling.output = myBiomodModelOut,
#   new.env = myExpl,
#   proj.name = 'current',
#   selected.models = 'all',
#   binary.meth = 'TSS',
#   compress = 'xz',
#   clamping.mask = FALSE,
#   output.format = '.grd')
# 
# myBiomodProj
# plot(myBiomodProj, str.grep = 'GLM')
# #
# Pred <-get_predictions(myBiomodProj)
# Sp.occ.xy <- DataSpecies[DataSpecies[,5]==1,2:3]
# Percentage <- 7
# 
# binary.model<-ecospat.binary.model (Pred, Sp.occ.xy, Percentage)
# plot(binary.model)
# ## End(Not run)