-
Notifications
You must be signed in to change notification settings - Fork 0
/
RSCRIPTBATCH_OVERSEE_niche_final_modelle.R
389 lines (327 loc) · 14.8 KB
/
RSCRIPTBATCH_OVERSEE_niche_final_modelle.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
### ==============================================================================================================================
library("raster")
library("rgdal")
library("sp")
library("rJava")
library("stringr")
library("reshape2")
library("tidyverse")
library("biomod2")
### ==============================================================================================================================
### Set the dataset and the background selection strategy
#set <- "v9v3.2"
set <- "v9v3.1"
strat <- "total"
# Or:
#strat <- "total2"
### Set the working directories
if (strat == "total") {
setwd( paste(getwd(),"/","species_data_",set,"/","total_background/", sep = "") )
WD <- getwd()
# Niche modelling dir
setwd(paste(WD,"/","niche.modelling","/", sep = ""))
second.wd <- getwd()
} else if ( strat == "total2" ) {
setwd( paste(getwd(),"/","species_data_",set,"/","total_background_rar/", sep = "") )
WD <- getwd()
setwd(paste(WD,"/","niche.modelling","/", sep = ""))
second.wd <- getwd()
} # eo if else loop
setwd(WD)
### List of SDMs
SDMs <- c('GLM','GAM','RF','ANN')
#sdm <- "MAXENT.Phillips"
### List of eval_runs :
eval_runs <- c("RUN1","RUN2","RUN3","RUN4","RUN5","RUN6","RUN7","RUN8","RUN9","RUN10")
### List of tets species names
#files <- dir()[grep(".txt",dir())]
# Extract species name sfrom file names
#species <- stringr::str_replace_all(string = files, pattern = paste("data_",strat,"_", sep =""), replacement = "")
#species <- stringr::str_replace_all(string = species, pattern = ".txt", replacement = "")
# species
### To identify the species that have not been modelled and those that have been but for which eval scores are missing
#setwd(paste("/net/kryo/work/fabioben/OVERSEE/data/biology/species_data_",set,"/total_background_rar/eval_scores", sep = "") )
#donespp <- dir()
# Remove .Rdata and eval_scores from ch string
#donespp <- str_replace_all(donespp, ".Rdata", "")
#donespp <- str_replace_all(donespp, "eval_scores_", "")
#for(i in 1:length(donespp)) {
#sp <- donespp[i]
#if( length( strsplit(sp, "_", fixed = TRUE)[[1]] ) == 3) {
# Then add brackets around the second piece
#sp <- paste(strsplit(sp, "_", fixed = TRUE)[[1]][1],"_(",
#strsplit(sp, "_", fixed = TRUE)[[1]][2],")_",
#strsplit(sp, "_", fixed = TRUE)[[1]][3], sep = "")
#donespp[i] <- sp
#}
#} # eo for loop
# OK Now remove the 'donespp' from 'species'
#species2 <- species[!(species %in% donespp)]
### Load the env stacks (winter and summer, like January & August) for projection
setwd("/net/kryo/work/fabioben/OVERSEE/data/env_predictors/global_monthly_clims_1d/")
jan <- read.table("glob_stack_month_jan_23_10_18.txt", h = T, sep = ";")
feb <- read.table("glob_stack_month_feb_23_10_18.txt", h = T, sep = ";")
mar <- read.table("glob_stack_month_mar_23_10_18.txt", h = T, sep = ";")
apr <- read.table("glob_stack_month_apr_23_10_18.txt", h = T, sep = ";")
may <- read.table("glob_stack_month_may_23_10_18.txt", h = T, sep = ";")
jun <- read.table("glob_stack_month_jun_23_10_18.txt", h = T, sep = ";")
jul <- read.table("glob_stack_month_jul_23_10_18.txt", h = T, sep = ";")
aug <- read.table("glob_stack_month_aug_23_10_18.txt", h = T, sep = ";")
sep <- read.table("glob_stack_month_sep_23_10_18.txt", h = T, sep = ";")
oct <- read.table("glob_stack_month_oct_23_10_18.txt", h = T, sep = ";")
nov <- read.table("glob_stack_month_nov_23_10_18.txt", h = T, sep = ";")
dec <- read.table("glob_stack_month_dec_23_10_18.txt", h = T, sep = ";")
# Vector of variables name
vars <- c("SST","deltaT","SSS","PAR","MLD1","logChl","logSiO2")
### Set modelling options
myBiomodOption <- BIOMOD_ModelingOptions(
GLM = list( type = 'quadratic',
interaction.level = 0,
myFormula = NULL,
test = 'AIC',
family = binomial("logit"),
mustart = 0.5,
control = glm.control(epsilon = 1e-08, maxit = 50, trace = FALSE)),
MAXENT.Phillips = list(path_to_maxent.jar = second.wd,
maximumiterations = 500,
visible = FALSE,
linear = TRUE,
quadratic = TRUE,
product = TRUE,
threshold = FALSE,
hinge = TRUE,
lq2lqptthreshold = 80,
l2lqthreshold = 10,
hingethreshold = 15,
beta_threshold = -1,
beta_categorical = -1,
beta_lqp = -1,
beta_hinge = -1,
defaultprevalence = 0.5),
GAM = list(algo = 'GAM_mgcv',
type = 's_smoother',
k = 5,
interaction.level = 0,
myFormula = NULL,
family = binomial("logit"),
method = 'GCV.Cp',
optimizer = c('outer','newton'),
select = FALSE,
knots = NULL,
paraPen = NULL,
control = list(nthreads = 1, irls.reg = 0, epsilon = 1e-07
, maxit = 200, trace = FALSE, mgcv.tol = 1e-07, mgcv.half = 15
, rank.tol = 1.49011611938477e-08
, nlm = list(ndigit=7, gradtol=1e-06, stepmax=2, steptol=1e-04, iterlim=200, check.analyticals=0)
, optim = list(factr=1e+07)
, newton = list(conv.tol=1e-06, maxNstep=5, maxSstep=2, maxHalf=30, use.svd=0)
, outerPIsteps = 0, idLinksBases = TRUE, scalePenalty = TRUE, keepData = FALSE) ),
RF = list(do.classif = T,
ntree = 5000,
nodesize = 10
)
) # eo modelling options
setwd(WD)
### Define the function you will apply in parallel
# sp <- "Calanus_finmarchicus"
SpeciesNicheModelling <- function(sp = sp) {
# Get the data
setwd(WD)
message(paste("Modelling ",sp, " =========================================================", sep = ""))
data <- read.table(paste("data_",strat,"_",sp,".txt", sep = ""), h = T, sep = ";")
# summary(data[data$obs == 1,])
### Initialisation: data formatting ==================================================================================
myRespName <- str_replace_all(sp, "_", ".")
myRespName <- gsub("\\(|\\)", "", myRespName)
# the presence/absences data for our species
myResp <- as.numeric(data$obs)
# the XY coordinates of species data
myRespXY <- data[,c("x","y")]
# Environmental variables
colnames(data)[c(20)] <- c("deltaT")
myExpl <- data[,vars]
# weights vector
weights <- na.omit(data[,c(vars,"weights")])[,"weights"]
# formatage des données
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName
)
### Modelling
setwd(second.wd)
myBiomodModelOut <- BIOMOD_Modeling(myBiomodData,
models = SDMs,
models.options = myBiomodOption,
NbRunEval = length(eval_runs),
DataSplit = 80,
Yweights = weights,
VarImport = 0,
models.eval.meth = c('ROC','TSS','KAPPA'),
SaveObj = FALSE,
do.full.models = FALSE
) # eo modelling
### Save evaluation scores ==================================================================================
scores <- data.frame(get_evaluations(myBiomodModelOut))
# Use lapply to summarize all scores (10 scores per 6 SDMs) AND PROBABILITY THRESH
scores.ls <- lapply(SDMs, function(sdm) {
# Retrieve all scores for the sdm 's'
tss <- scores["TSS",grep(paste("Testing.data.",sdm, sep = ""), colnames(scores))]
tss_cutoffs <- scores["TSS",grep(paste("Cutoff.",sdm, sep = ""), colnames(scores))]
colnames(tss) <- paste(sdm, eval_runs, sep = "_")
colnames(tss) <- paste(sdm, eval_runs, sep = "_")
tss <- t(tss)
tss_cutoffs <- t(tss_cutoffs)
colnames(tss_cutoffs) <- "Cutoff_TSS"
# Same with AUC
auc <- scores["ROC",grep(paste("Testing.data.",sdm, sep = ""), colnames(scores))]
auc_cutoffs <- scores["ROC",grep(paste("Cutoff.",sdm, sep = ""), colnames(scores))]
colnames(auc) <- paste(sdm, eval_runs, sep = "_")
colnames(auc_cutoffs) <- paste(sdm, eval_runs, sep = "_")
auc <- t(auc)
auc_cutoffs <- t(auc_cutoffs)
colnames(auc_cutoffs) <- "Cutoff_AUC"
# Same with Cohen's KAPPA
kappa <- scores["KAPPA",grep(paste("Testing.data.",sdm, sep = ""), colnames(scores))]
kappa_cutoffs <- scores["KAPPA",grep(paste("Cutoff.",sdm, sep = ""), colnames(scores))]
colnames(kappa) <- paste(sdm, eval_runs, sep = "_")
colnames(kappa_cutoffs) <- paste(sdm, eval_runs, sep = "_")
kappa <- t(kappa)
kappa_cutoffs <- t(kappa_cutoffs)
colnames(kappa_cutoffs) <- "Cutoff_Kappa"
return(cbind(tss, tss_cutoffs, auc, auc_cutoffs, kappa, kappa_cutoffs))
}
) # eo lapply
scores.tbl <- data.frame(do.call(rbind, scores.ls))
# scores.tbl
rm(scores, scores.ls)
### Make projections (they will be printed in the species folder) ============================================
message(paste("Projecting ",sp, " =========================================================", sep = ""))
### Project niches in jan conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = jan[,vars],
proj.name = paste("projection",sp,"jan", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in feb conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = feb[,vars],
proj.name = paste("projection",sp,"feb", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in mar conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = mar[,vars],
proj.name = paste("projection",sp,"mar", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in apr conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = apr[,vars],
proj.name = paste("projection",sp,"apr", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in may conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = may[,vars],
proj.name = paste("projection",sp,"may", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in jun conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = jun[,vars],
proj.name = paste("projection",sp,"jun", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in jul conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = jul[,vars],
proj.name = paste("projection",sp,"jul", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in aug conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = aug[,vars],
proj.name = paste("projection",sp,"aug", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in sep conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = sep[,vars],
proj.name = paste("projection",sp,"sep", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in oct conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = oct[,vars],
proj.name = paste("projection",sp,"oct", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in nov conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = nov[,vars],
proj.name = paste("projection",sp,"nov", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Project niches in dec conditions
myBiomodProj <- BIOMOD_Projection(
modeling.output = myBiomodModelOut,
new.env = dec[,vars],
proj.name = paste("projection",sp,"dec", sep = "_"),
selected.models = 'all',
binary.meth = 'TSS',
compress = 'xz',
clamping.mask = FALSE
) # eo projection
### Save evaluation scores ===================================================================================
setwd(paste(WD,"/","eval_scores","/", sep = ""))
save(scores.tbl, file = paste("eval_scores_",sp,".Rdata", sep = ""))
setwd(WD)
} # eo NicheModelling
library("parallel")
mclapply(X = sp, SpeciesNicheModelling, mc.cores = 25)
### ==============================================================================================================================