# ecospat.CCV.communityEvaluation.prob

##### Evaluates community predictions directly on the probabilities (i.e., threshold independent)

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

a matrix with `TRUE/FALSE`

for each model run (`TRUE`

=Calibration point, `FALSE`

=Evaluation point)

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)

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 any of the other community evaluation metrics (`'probabilistic.Sorensen', 'probabilistic.Jaccard', 'probabilistic.Simpson'`

) is selected the returned tables will have the follwing colums:

`METRIC.mean`

= The average Sorensen/Jaccard/Simpson based on a number of random draws of the probabilities.`METRIC.sd`

= The standard deviation of Sorensen/Jaccard/Simpson based on a number of random draws of the probabilities.`METRIC.CI`

= The 95% confidence intervall of the average Sorensen/Jaccard/Simpson based on the standard deviation and number of draws. Should normally be <=`se.th`

.`nb.it`

= number of draws used to estimate all the parameters. The draws stop as soon as the desired precission (`se.th`

) is reached or the limit of allowed iterations (default=10'000).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. .....

##### 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)
# }
```

*Documentation reproduced from package ecospat, version 3.0, License: GPL*