Choosing best model with ENMeval
Francisco Rodriguez-Sanchez
2017-11-15
library(Carex.bipolar)
library(readr)
library(dplyr)
library(ENMeval)
library(rSDM)Load data
locs <- as.data.frame(read_csv(file.path(root, "data/locs_30m.csv")))
bioclim <- as.data.frame(read_csv(file.path(root, "data/bioclim_pres_30m.csv")))Select only occurrences of this species (defined in makefile):
species <- "canescens"
if (species != "allspp") locs <- locs[locs$species == species, ]Load present bioclim rasters
bioclim.pres <- read_presclim()
ENMeval
modeval <- ENMevaluate(occ = locs[, c("longitude", "latitude")],
env = bioclim.pres,
RMvalues = c(0.5, 1, 1.5, 2),
fc = c("LQ", "LQH"),
method = "randomkfold", kfolds = 10,
clamp = TRUE,
rasterPreds = TRUE,
parallel = TRUE, numCores = 4)
results <- modeval@results
kable(results)| settings | features | rm | full.AUC | Mean.AUC | Var.AUC | Mean.AUC.DIFF | Var.AUC.DIFF | Mean.OR10 | Var.OR10 | Mean.ORmin | Var.ORmin | AICc | delta.AICc | w.AIC | nparam |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LQ_0.5 | LQ | 0.5 | 0.8651 | 0.8614940 | 0.0013909 | 0.0070191 | 0.0009293 | 0.0340793 | 0.0005329 | 0.0015873 | 2.52e-05 | 10648.86 | 122.66055 | 0 | 22 |
| LQH_0.5 | LQH | 0.5 | 0.8989 | 0.8850875 | 0.0007034 | 0.0150685 | 0.0007477 | 0.0487055 | 0.0000533 | 0.0015873 | 2.52e-05 | 10669.64 | 143.43334 | 0 | 161 |
| LQ_1 | LQ | 1.0 | 0.8651 | 0.8614940 | 0.0013909 | 0.0070191 | 0.0009293 | 0.0340793 | 0.0005329 | 0.0015873 | 2.52e-05 | 10648.86 | 122.66055 | 0 | 22 |
| LQH_1 | LQH | 1.0 | 0.8946 | 0.8832822 | 0.0007418 | 0.0125761 | 0.0005571 | 0.0470926 | 0.0001365 | 0.0031746 | 4.48e-05 | 10579.88 | 53.67347 | 0 | 119 |
| LQ_1.5 | LQ | 1.5 | 0.8651 | 0.8614940 | 0.0013909 | 0.0070191 | 0.0009293 | 0.0340793 | 0.0005329 | 0.0015873 | 2.52e-05 | 10648.86 | 122.66055 | 0 | 22 |
| LQH_1.5 | LQH | 1.5 | 0.8900 | 0.8812912 | 0.0009052 | 0.0108581 | 0.0005306 | 0.0404075 | 0.0001197 | 0.0031746 | 4.48e-05 | 10581.93 | 55.72539 | 0 | 106 |
| LQ_2 | LQ | 2.0 | 0.8651 | 0.8614940 | 0.0013909 | 0.0070191 | 0.0009293 | 0.0340793 | 0.0005329 | 0.0015873 | 2.52e-05 | 10648.86 | 122.66055 | 0 | 22 |
| LQH_2 | LQH | 2.0 | 0.8878 | 0.8805924 | 0.0008768 | 0.0093201 | 0.0004986 | 0.0404075 | 0.0001197 | 0.0015873 | 2.52e-05 | 10526.20 | 0.00000 | 1 | 76 |
eval.plot(results)
maps <- modeval@predictions
plot(maps)
plot(maps[[which(results$delta.AICc == 0)]], main = "Models with lowest AICc")
for (i in which(results$delta.AICc == 0)) {
response(modeval@models[[i]])
}
save(modeval, file = paste0(root, "/analyses/output/fullspp_ENMeval/", species, "_modeval.rda"))rm(list = ls())devtools::session_info()## - Session info ----------------------------------------------------------
## setting value
## version R version 3.4.2 (2017-09-28)
## os Windows 7 x64 SP 1
## system i386, mingw32
## ui RTerm
## language (EN)
## collate English_United Kingdom.1252
## tz Europe/Paris
## date 2017-11-13
##
## - Packages --------------------------------------------------------------
## package * version date
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## ENMeval * 0.2.2 2017-01-10
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## Rcpp 0.12.13 2017-09-28
## readr * 1.1.1 2017-05-16
## reshape2 1.4.2 2016-10-22
## rgdal 1.2-15 2017-10-30
## RgoogleMaps 1.4.1 2016-09-18
## rJava * 0.9-9 2017-10-12
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## udunits2 0.13 2016-11-17
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## withr 2.1.0 2017-11-01
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## zoo 1.8-0 2017-04-12
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