# Converting probability of occurrence to Presence-absence For many applications, the predicted probability of occurrence (habitat suitability) should be transformed to presence-absence given a threshold which can be selected based on some criteria through model evaluation. `pa` facilitates this transformation based on either the threshold specified by a user or extracted from the models (or ensemble of the mdoels). ```r pa(x,y,id,opt,...) ``` ## Arguments - `x`: a SpatRaster object contains predicted probability of occurrence generated by the predict or ensemble functions - `y`: eithe a sdmModels (outcome of the sdm function) or a numeric vector with threshold values - `id`: when `y` is sdmModels, `id` can be either a numeric vector specifying the modelIDs corresponding to the layers in `x`, or a single character "ensemble" to tell the function that the single layer in `x` is generated from the ensemble function, so the threshold should also be obtained based on ensemble - `opt`: when `y` is sdmModels, `opt` specifies the optimisation criterion based on which a threshold is identified; default is opt=2 (see `evaluates` for more details) - `...`: not implemented. ## Details if `id` is numeric, the number of layers in `x` (SpatRaster) should be the same as the length of `id`, but if `id = "ensemble"`, `nlyr(x)` should be 1. ## Returns a SpatRaster with presence-absence values ## References Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881 ## Author(s) Babak Naimi naimi.b@gmail.com [https://www.r-gis.net/](https://www.r-gis.net/) [https://www.biogeoinformatics.org/](https://www.biogeoinformatics.org/) ## Examples ```r ## Not run: # let's first fit a set of models and generate prediction and ensemble maps: # get the path to the species data file <- system.file("external/sp1.shp", package="sdm") sp <- vect(file) # read the species records path <- system.file("external", package="sdm") # path to the folder contains the data lst <- list.files(path=path,pattern='asc$',full.names = T) # list of predictor filenames preds <- rast(lst) # making a SpatRaster object (predictors) d <- sdmData(formula=Occurrence~., train=sp, predictors=preds) d # fit two models: m <- sdm(Occurrence~.,data=d,methods=c('glmp','brt'),replication='boot',n=1) m # predictions: pr <- predict(m, newdata=preds) plot(pr) # ensemble:: en <- ensemble(m, newdata=preds,setting=list(method='weighted',stat='AUC')) plot(en) ######################### # let's convert probabilities to Presence/absences: # threshold is extracted for both models based on opt=2 (max[se+sp]) pr.pa <- pa(pr, m, opt=2) plot(pr.pa) # if only one of them was needed: pr.pa1 <- pa(pr[[1]],m, id=1,opt=2) plot(pr.pa1) #--------------- # if you have threshold values, you can directly use them in y: th <- getEvaluation(m,stat='threshold',opt=1) # get threshold values th pr.pa <- pa(pr, y=th[,2]) plot(pr.pa) #-------------- # to convert to PA based on "ensemble": en.pa <- pa(en, m, id="ensemble", opt=2) plot(en.pa) ## End(Not run) ```

pa function