pa: Converting probability of occurrence to Presence-absence

In babaknaimi/sdm: Species Distribution Modelling

Converting probability of occurrence to Presence-absence

Description

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).

Usage

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.

Value

a SpatRaster with presence-absence values

Author(s)

Babak Naimi [e-mail]

https://www.r-gis.net/

https://www.biogeoinformatics.org/

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

Examples

## 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)

babaknaimi/sdm documentation built on April 4, 2024, 1:45 p.m.