Check cutoff BOYCE

check cutoff for BOYCE for bm_OptimStat
+ Add temp_workdir as argument for bm_VariablesImportance

R/bm_FindOptimStat.R

@@ -255,8 +255,7 @@ bm_FindOptimStat <- function(metric.eval = 'TSS',
     } else if (metric.eval == "MPA") {
       cutoff <- ecospat.mpa(fit[which(obs == 1)], perc = mpa.perc)
     }
-
-    if (!is.na(cutoff / 1000)) {
+    if (!is.na(cutoff / 1000) & cutoff <= 1000) {
       EVAL <- presence.absence.accuracy(DATA, threshold = cutoff / 1000)
       sensitivity <- EVAL$sensitivity * 100
       specificity <- EVAL$specificity * 100

R/bm_RunModelsLoop.R

@@ -516,6 +516,7 @@ bm_RunModel <- function(model, run.name, dir.name = '.'
                          obs = data_sp[which(eval.lines.vec == FALSE)],
                          fit = g.pred[which(eval.lines.vec == FALSE)])
       }
+      if (max(cross.validation$cutoff,na.rm = T) > 1000) {cat("\n*** Wrong values predicted, please be careful with the results fo this model")}
       colnames(cross.validation)[which(colnames(cross.validation) == "best.stat")] <- "calibration"
 
       stat.validation <- foreach(xx = metric.eval, .combine = "rbind") %do% {

R/bm_VariablesImportance.R

@@ -97,9 +97,10 @@ bm_VariablesImportance <- function(bm.model,
                                    nb.rep = 1,
                                    # nb.cpu = 1,
                                    seed.val = NULL,
-                                   do.progress = TRUE)
+                                   do.progress = TRUE,
+                                   temp_workdir = NULL)
 {
-  args <- .bm_VariablesImportance.check.args(bm.model, expl.var, variables, method, nb.rep, seed.val, do.progress)
+  args <- .bm_VariablesImportance.check.args(bm.model, expl.var, variables, method, nb.rep, seed.val, do.progress,temp_workdir)
   for (argi in names(args)) { assign(x = argi, value = args[[argi]]) }
   rm(args)
   ## Test if prediction is computable