This function evaluates the Ensemble of Small Models by pooling the different runs of the cross validation as in Collart et al. (2021).
ecospat.ESM.EnsembleEvaluation(ESM.modeling.output,
ESM.EnsembleModeling.output,
metrics = c("SomersD","AUC","MaxTSS","MaxKappa","Boyce"),
EachSmallModels = FALSE)a list containing:
a matrix with the evaluation scores for the ESMs based on the different modelling algorithms and based on the consensus across the modelling algorithms (called here "ensemble")
a matrix of predicted values resulting from the pooling procedure and used to compute the evaluation scores. The column resp is where the species occurs or not
a list containing a matrix of evaluation scores for each bivariate models (generated only if EachSmallModels = T)
a list containing a matrix of of predicted values resulting from the pooling procedure for each bivariate models (generated only if EachSmallModels = T)
a list returned by ecospat.ESM.Modeling
a list returned by ecospat.ESM.EnsembleModeling
a vector of evaluation metrics chosen among "SomersD", "AUC", "MaxTSS", "MaxKappa", "Boyce"
should the individual bivariate models be evaluated by the pooling procedure?
Flavien Collart flavien.collart@unil.ch
with contributions of Olivier Broennimann olivier.broennimann@unil.ch
Because a minimum sample size is needed to evaluate models (see Collart & Guisan, 2023), this function uses the approach from Collart et al.(2021), which consists to pool the suitability values of the hold-out data (evaluation dataset) across replicates. As the same data point (presence or absence or background point) is presumably sampled in several replicates, the suitability values for each data point is consequently averaged across replicates where they were sampled. This procedure generates a series of independent suitability values with a size approximately equal (as some data points may not have been sampled by chance in any of the n replicates) to that of the number of data point.
Collart, F., & Guisan, A. (2023). Small to train, small to test: Dealing with low sample size in model evaluation. Ecological Informatics. 75, 102106. tools:::Rd_expr_doi("10.1016/j.ecoinf.2023.102106")
Collart, F., Hedenas, L., Broennimann, O., Guisan, A. and Vanderpoorten, A. 2021. Intraspecific differentiation: Implications for niche and distribution modelling. Journal of Biogeography. 48, 415-426. tools:::Rd_expr_doi("10.1111/jbi.14009")
ecospat.ESM.EnsembleModeling