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Evaluate cross-validated Hmsc Model

Source: R/Mod_CV_Eval.R
Mod_CV_Eval.Rd

This function evaluates Hmsc models trained via cross-validation using. It merges posterior chains, makes predictions, and computes 4 evaluation metrics: AUC, RMSE, Tjur's R2, and Boyce Index.

Usage

Mod_CV_Eval(Path_CV = NULL, predictEtaMean = TRUE, NCores = 8)

Arguments

Path_CV

Character. The directory path where cross-validation models and outputs are stored.

predictEtaMean

boolean flag indicating whether to use the estimated mean values of posterior predictive distribution for random effects corresponding for the new units. See Hmsc:::predict.Hmsc for more details.

NCores

Integer specifying the number of parallel cores for parallelization. Default: 8 cores.

Author

Ahmed El-Gabbas