ecospat (version 3.0)

ecospat.CCV.communityEvaluation.prob: Evaluates community predictions directly on the probabilities (i.e., threshold independent)

Description

This function generates a number of community evaluation metrics directly based on the probability returned by the individual models. Instead of thresholding the predictions (ecospat.CCV.communityEvaluation.bin this function directly uses the probability and compares its outcome to null models or average expectations.)

Usage

ecospat.CCV.communityEvaluation.prob(
      ccv.modeling.data,
      community.metrics=c('SR.deviation','community.AUC','probabilistic.Sorensen'),
      se.th=0.01,
      parallel = TRUE,
      cpus = 4)

Arguments

ccv.modeling.data

a 'ccv.modeling.data' object returned by ecospat.CCV.modeling

community.metrics

a selection of community metrics to calculate ('SR.deviation', 'community.AUC', 'probabilistic.Sorensen', 'probabilistic.Jaccard', 'probabilistic.Simpson')

se.th

the desired precission for the community metrics (standard error of the mean)

parallel

should parallel computing be allowed (TRUE/FALSE)

cpus

number of cpus to use in parallel computing

Value

DataSplitTable

a matrix with TRUE/FALSE for each model run (TRUE=Calibration point, FALSE=Evaluation point)

CommunityEvaluationMetrics.CalibrationSites

a 3-dimensional array containing the community evaluation metrics for the calibartion sites of each run (NA means that the site was used for evaluation)

CommunityEvaluationMetrics.EvaluationSites

a 3-dimensional array containing the community evaluation metrics for the evaluation sites of each run (NA means that the site was used for calibaration)

See Also

ecospat.CCV.createDataSplitTable; ecospat.CCV.communityEvaluation.bin;

Examples

# NOT RUN {
#Loading species occurence data and remove empty communities
data(ecospat.testData)
testData <- ecospat.testData[,c(24,34,43,45,48,53,55:58,60:63,65:66,68:71)]
sp.data <- testData[which(rowSums(testData)>0), sort(colnames(testData))]

#Loading environmental data
env.data <- ecospat.testData[which(rowSums(testData)>0),4:8]

#Coordinates for all sites
xy <- ecospat.testData[which(rowSums(testData)>0),2:3]

#Running all the models for all species
myCCV.Models <- ecospat.CCV.modeling(sp.data = sp.data,
                                     env.data = env.data,
                                     xy = xy,
                                     NbRunEval = 5,
                                     minNbPredictors = 10,
                                     VarImport = 3)
                                     
#Calculating the probabilistic community metrics
myCCV.communityEvaluation.prob <- ecospat.CCV.communityEvaluation.prob(
      ccv.modeling.data = myCCV.Models,
      community.metrics = c('SR.deviation','community.AUC','probabilistic.Sorensen'),
      se.th = 0.02, 
      parallel = FALSE,
      cpus = 4)
# }

Run the code above in your browser using DataCamp Workspace