Probability Density Function
Using a variety of parameters in modelling will inevitably bring variability in predictions, especially when it comes to making future predictions. This function enables an overall viewing of the future predictions range per species and gives the likelihood of range shift estimations. It will calculate the optimal way for condensing a defined proportion (50, 75, 90 and 95% per default) of the data.
ProbDensFunc( initial, projections, groups = NULL, plothist = TRUE, cvsn = TRUE, resolution = 5, filename = NULL, ... )
initial |
a vector (resp. a |
projections |
a matrix (resp; a |
groups |
an option for ungrouping the projections enabling a separated visualisation of the prediction range per given group. A matrix is expected where each column is a single prediction and each line is giving details of one parameter (See the examples section). |
plothist |
set to TRUE to plot the range change histogram |
cvsn |
stands for "current vs new". If true, the range change calculations will be of two types: the percentage of cells currently occupied by the species to be lost, and the relative percentage of cells currently unoccupied but projected to be, namely 'new' cells, compared to current surface range. |
resolution |
the step used for classes of prediction in graphics. The default value is 5. |
filename |
the name of file (with extension) where plots will be
stored. If not |
... |
further arguments: - |
The future range changes are calculated as a percentage of the species' present state. For example, if a species currently occupies 100 cells and is estimated by a model to cover 120 cells in the future, the range change will be + 20%.
Resolution : Note that modifying the resolution will directly influence the probability scale. Bigger classes will accumulate a greater number of predictions and therefore represent a greater fraction of the total predictions. The probability is in fact that of the class and not of isolated events.
This is a plotting function, no objects are returned or created.
Wilfried Thuiller, Bruno Lafourcade, Damien Georges
## Not run:
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])
##' remove all 0 from response vector to work with
##' presence only data (Pseudo Absences selections)
rm_id <- which(myResp==0)
myResp <- myResp[-rm_id]
##' the XY coordinates of species data
myRespXY <- DataSpecies[-rm_id,c("X_WGS84","Y_WGS84")]
##' Environmental variables extracted from BIOCLIM
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,
PA.nb.rep=3)
##' 2. Defining Models Options using default options.
myBiomodOption <- BIOMOD_ModelingOptions()
##' 3. Doing Modelisation
myBiomodModelOut <- BIOMOD_Modeling( myBiomodData,
models = c('CTA','RF','GLM','GAM','ANN','MARS'),
models.options = myBiomodOption,
NbRunEval=5,
DataSplit=70,
Prevalence=0.5,
models.eval.meth = c('TSS'),
do.full.models = FALSE,
rescal.all.models=T,
modeling.id='test')
##' 4. Build ensemble-models that will be taken as reference
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.median = TRUE)
##' 5. Projection on future environmental conditions
###' load future environmental conditions from biomod2 package
myExpl_fut <- raster::stack( system.file( "external/bioclim/future/bio3.grd",
package="biomod2"),
system.file( "external/bioclim/future/bio4.grd",
package="biomod2"),
system.file( "external/bioclim/future/bio7.grd",
package="biomod2"),
system.file( "external/bioclim/future/bio11.grd",
package="biomod2"),
system.file( "external/bioclim/future/bio12.grd",
package="biomod2"))
myBiomodProjection <- BIOMOD_Projection(modeling.output = myBiomodModelOut,
new.env = myExpl_fut,
proj.name = 'future',
selected.models = 'all',
binary.meth = 'TSS',
compress = FALSE,
build.clamping.mask = TRUE)
BIOMOD_EnsembleForecasting(projection.output=myBiomodProjection,
EM.output=myBiomodEM,
binary.meth='TSS')
##' 6. load binary projections
consensusBin <- raster::stack('GuloGulo/proj_future/proj_future_GuloGulo_ensemble_TSSbin.grd')
projectionsBin <- raster::stack('GuloGulo/proj_future/proj_future_GuloGulo_TSSbin.grd')
##' 7. build a ref state based on ensemble-models
ref <- sampleRandom(subset(consensusBin, 1, drop=T), size=5000, sp=T, na.rm=T)
##' 8. autoatic creation of groups matrix
find_groups <- function(diff_by_pix){
data.set <- sapply(names(diff_by_pix),biomod2:::.extractModelNamesInfo,info='data.set')
run.eval <- sapply(names(diff_by_pix),biomod2:::.extractModelNamesInfo,info='run.eval')
models <- sapply(names(diff_by_pix),biomod2:::.extractModelNamesInfo,info='models')
return(rbind(data.set,run.eval,models))
}
groups <- find_groups(projectionsBin)
##' 9. plot ProbDensFunct graphs
ProbDensFunc(initial = ref,
projections = projectionsBin,
plothist=TRUE,
cvsn=TRUE,
groups=groups,
resolution=2,
filename=NULL,
lim=c(0.5,0.8,0.95))
###' 3 plots should be produced.. Should be convenient to save it within a device
###' supporting multiple plots.
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.