ecospat.CCV.communityEvaluation.prob {ecospat} R Documentation

## 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','Max.Sorensen',
'Max.Jaccard','probabilistic.Sorensen',
'probabilistic.Jaccard'),
parallel = FALSE,
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','Max.Sorensen','Max.Jaccard', 'probabilistic.Sorensen','probabilistic.Jaccard')) 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)

### Note

If the community evaluation metric 'SR.deviation' is selected the returned tables will have the following columns:

• SR.obs = observed species richness,

• SR.mean = the predicted species richness (based on the probabilities assuming poission binomial distribution),

• SR.dev = the deviation of observed and predicted species richness,

• SR.sd = the standard deviation of the predicted species richness (based on the probabilities assuming poission binomial distribution),

• SR.prob = the probability that the observed species richness falls within the predicted species richness (based on the probabilities assuming poission binomial distribution),

• SR.imp.05 = improvement of species richness prediction over null-model 0.5,

• SR.imp.average.SR = improvement of species richness prediction over null-model average.SR and

• SR.imp.prevalence = improvement of species richness prediction over null-model prevalence.

If the community evalation metric community.AUC is selected the returned tables will have the following colums:

• Community.AUC = The AUC of ROC of a given site (in this case the ROC plot is community sensitiviy [percentage species predicted corretly present] vs 1 - community specificity [percentage of species predicted correctly absent])

If the community evaluation metrics ('Max.Sorensen', 'Max.Jaccard') is selected the returned tables will have the follwing colums:

• Max.Jaccard = The maximum possible Jaccard similarity based on an optimal site specific threshold.

• Max.Sorensen = The maximum possible Sorensen similarity based on an optimal site specific threshold.

If the community evaluation metrics ('probabilistic.Sorensen', 'probabilistic.Jaccard') is selected the returned tables will have the follwing colums:

• probabilistic.Jaccard = The probabilistic Jaccard similarity index based on Scherrer et al. 2019, Methods in Ecology and Evolution

• probabilistic.Sorensen = The probabilistic Sorensen similarity index based on Scherrer et al. 2019, Methods in Ecology and Evolution

• composition.imp.05 = improvement of species compostion prediction over the null-model 0.5.

• composition.imp.average.SR = improvement of the species composition prediction over the null-model average.SR.

• composition.imp.prevalence = improvement of the species composition prediction over the null-model prevalence.

For detailed descriptions of the null models see Scherrer et al. 2019

### Author(s)

Daniel Scherrer <daniel.j.a.scherrer@gmail.com>

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

### Examples


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))]

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 = 2,
minNbPredictors = 10,
VarImport = 2)

#Calculating the probabilistic community metrics
metrics = c('SR.deviation','community.AUC','probabilistic.Sorensen','Max.Sorensen')
myCCV.Eval.prob <- ecospat.CCV.communityEvaluation.prob(
ccv.modeling.data = myCCV.Models,
community.metrics = metrics)



[Package ecospat version 3.4 Index]