I ran the kuenmceval function on my data, it ran smoothly (except for the mutate function error in dplyr, but from what I saw here that should not be a problem).
So, I ran the function and got the results. I explored them a bit and noticed that the model presented in the "best_candidate_models_OR_AICc.csv" is not coincident with the data for that model from the "calibration_results.csv" file.
The "best candidate model" has a deltaAICc value of 0, but on the "calibration_results.csv" file that same model has a deltaAICc value different from 0.
I ran the kuenmceval function on my data, it ran smoothly (except for the mutate function error in dplyr, but from what I saw here that should not be a problem).
So, I ran the function and got the results. I explored them a bit and noticed that the model presented in the "best_candidate_models_OR_AICc.csv" is not coincident with the data for that model from the "calibration_results.csv" file.
The "best candidate model" has a deltaAICc value of 0, but on the "calibration_results.csv" file that same model has a deltaAICc value different from 0.
Which file should I trust?
The code that I used:
occ_joint <- "prover_joint.csv" occ_tra <- "prover_train.csv" M_var_dir <- "M_variables" batch_cal <- "Candidate_models" out_dir <- "Candidate_Models" reg_mult <- c(0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4) f_clas <- "basic" args <- NULL maxent_path <- "C:/Users/biodeserts/Documents/joaolima/kuenm/prover" wait <- FALSE run <- TRUE
kuenm_cal(occ.joint = occ_joint, occ.tra = occ_tra, M.var.dir = M_var_dir, batch = batch_cal, out.dir = out_dir, reg.mult = reg_mult, f.clas = f_clas, args = args, maxent.path = maxent_path, wait = wait, run = run)
occ_test <- "prover_test.csv" out_eval <- "Calibration_results" threshold <- 10 rand_percent <- 50 iterations <- 100 kept <- TRUE selection <- "OR_AICc" paral_proc <- FALSE
cal_eval <- kuenm_ceval(path = out_dir, occ.joint = occ_joint, occ.tra = occ_tra, occ.test = occ_test, batch = batch_cal, out.eval = out_eval, threshold = threshold, rand.percent = rand_percent, iterations = iterations, kept = kept, selection = selection, parallel.proc = paral_proc)