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comparison dennis_gam_initial_functions.R @ 0:5b126f770671 draft

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1 ### R-Script Adapted from script provided by the CEH, UK BY: Reto Schmucki [ reto.schmucki@mail.mcgill.ca]
2 ### DATE: 14 July 2014 function to run two stage model in DENNIS et al. 2013
3
4
5 .onAttach <- function(libname, pkgname) {
6 packageStartupMessage(" The regionalGAM package that is no longer maintained, \n use the new rbms package instead. \n
7 devtools::install_github(\"RetoSchmucki/rbms\", force=TRUE)")
8 }
9
10
11 #' year_day_func Function
12 #' This function generate the full sequence of days, months and include the observation to that file.
13 #' @param sp_data A data.frame with your observation.
14 #' @keywords year days
15 #' @export
16 #' @author Reto Schmucki
17 #' @examples
18 #' year_day_func()
19
20
21 # FUNCTIONS
22
23 year_day_func = function(sp_data) {
24
25 year <- unique(sp_data$YEAR)
26
27 origin.d <- paste(year, "01-01", sep = "-")
28 if ((year%%4 == 0) & ((year%%100 != 0) | (year%%400 == 0))) {
29 nday <- 366
30 } else {
31 nday <- 365
32 }
33
34 date.serie <- as.POSIXlt(seq(as.Date(origin.d), length = nday, by = "day"), format = "%Y-%m-%d")
35
36 dayno <- as.numeric(julian(date.serie, origin = as.Date(origin.d)) + 1)
37 month <- as.numeric(strftime(date.serie, format = "%m"))
38 week <- as.numeric(strftime(date.serie, format = "%W"))
39 week_day <- as.numeric(strftime(date.serie, format = "%u"))
40 day <- as.numeric(strftime(date.serie, format = "%d"))
41
42 site_list <- sp_data[!duplicated(sp_data$SITE), c("SITE")]
43
44 all_day_site <- data.frame(SPECIES = sp_data$SPECIES[1], SITE = rep(site_list, rep(nday, length(site_list))),
45 YEAR = sp_data$YEAR[1], MONTH = month, WEEK = week, DAY = day, DAY_WEEK = week_day, DAYNO = dayno,
46 COUNT = NA)
47
48 count_index <- match(paste(sp_data$SITE, sp_data$DAYNO, sep = "_"), paste(all_day_site$SITE, all_day_site$DAYNO,
49 sep = "_"))
50 all_day_site$COUNT[count_index] <- sp_data$COUNT
51 site_count_length <- aggregate(sp_data$COUNT, by = list(sp_data$SITE), function(x) list(1:length(x)))
52 names(site_count_length$x) <- as.character(site_count_length$Group.1)
53 site_countno <- utils::stack(site_count_length$x)
54 all_day_site$COUNTNO <- NA
55 all_day_site$COUNTNO[count_index] <- site_countno$values # add count number to ease extraction of single count
56
57 # Add zero to close observation season two weeks before and after the first and last
58 first_obs <- min(all_day_site$DAYNO[!is.na(all_day_site$COUNT)])
59 last_obs <- max(all_day_site$DAYNO[!is.na(all_day_site$COUNT)])
60
61 closing_season <- c((first_obs - 11):(first_obs - 7), (last_obs + 7):(last_obs + 11))
62
63 # If closing season is before day 1 or day 365, simply set the first and last 5 days to 0
64 if (min(closing_season) < 1)
65 closing_season[1:5] <- c(1:5)
66 if (max(closing_season) > nday)
67 closing_season[6:10] <- c((nday - 4):nday)
68
69 all_day_site$COUNT[all_day_site$DAYNO %in% closing_season] <- 0
70 all_day_site$ANCHOR <- 0
71 all_day_site$ANCHOR[all_day_site$DAYNO %in% closing_season] <- 1
72
73 all_day_site <- all_day_site[order(all_day_site$SITE, all_day_site$DAYNO), ]
74
75 return(all_day_site)
76 }
77
78
79 #' trap_area Function
80 #'
81 #' This function compute the area under the curve using the trapezoid method.
82 #' @param x A vector or a two columns matrix.
83 #' @param y A vector, Default is NULL
84 #' @keywords trapezoid
85 #' @export
86 #' @examples
87 #' trap_area()
88
89
90 trap_area = function(x, y = NULL) {
91 # If y is null and x has multiple columns then set y to x[,2] and x to x[,1]
92 if (is.null(y)) {
93 if (length(dim(x)) == 2) {
94 y = x[, 2]
95 x = x[, 1]
96 } else {
97 stop("ERROR: need to either specifiy both x and y or supply a two column data.frame/matrix to x")
98 }
99 }
100
101 # Check x and y are same length
102 if (length(x) != length(y)) {
103 stop("ERROR: x and y need to be the same length")
104 }
105
106 # Need to exclude any pairs that are NA for either x or y
107 rm_inds = which(is.na(x) | is.na(y))
108 if (length(rm_inds) > 0) {
109 x = x[-rm_inds]
110 y = y[-rm_inds]
111 }
112
113 # Determine values of trapezoids under curve Get inds
114 inds = 1:(length(x) - 1)
115 # Determine area using trapezoidal rule Area = ( (b1 + b2)/2 ) * h where b1 and b2 are lengths of bases
116 # (the parallel sides) and h is the height (the perpendicular distance between two bases)
117 areas = ((y[inds] + y[inds + 1])/2) * diff(x)
118
119 # total area is sum of all trapezoid areas
120 tot_area = sum(areas)
121
122 # Return total area
123 return(tot_area)
124 }
125
126
127 #' trap_index Function
128 #'
129 #' This function compute the area under the curve (Abundance Index) across species, sites and years
130 #' @param sp_data A data.frame containing species count data generated from the year_day_func()
131 #' @param y A vector, Default is NULL
132 #' @keywords Abundance index
133 #' @export
134 #' @examples
135 #' trap_index()
136
137
138
139 trap_index = function(sp_data, data_col = "IMP", time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR")) {
140
141 # Build output data.frame
142 out_obj = unique(sp_data[, by_col])
143 # Set row.names to be equal to collapsing of output rows (will be unique, you need them to make uploading
144 # values back to data.frame will be easier)
145 row.names(out_obj) = apply(out_obj, 1, paste, collapse = "_")
146
147 # Using this row.names from out_obj above as index in by function to loop through values all unique combs
148 # of by_cols and fit trap_area to data
149 ind_dat = by(sp_data[, c(time_col, data_col)], apply(sp_data[, by_col], 1, paste, collapse = "_"), trap_area)
150
151 # Add this data to output object
152 out_obj[names(ind_dat), "SINDEX"] = round(ind_dat/7, 1)
153
154 # Set row.names to defaults
155 row.names(out_obj) = NULL
156
157 # Return output object
158 return(out_obj)
159 }
160
161
162 #' flight_curve Function
163 #' This function compute the flight curve across and years
164 #' @param your_dataset A data.frame containing original species count data. The data format is a csv (comma "," separated) with 6 columns with headers, namely "species","transect_id","visit_year","visit_month","visit_day","sp_count"
165 #' @keywords standardize flight curve
166 #' @export
167 #' @examples
168 #' flight_curve()
169
170
171 flight_curve <- function(your_dataset) {
172
173 if("mgcv" %in% installed.packages() == "FALSE") {
174 print("mgcv package is not installed.")
175 x <- readline("Do you want to install it? Y/N")
176 if (x == 'Y') {
177 install.packages("mgcv")
178 }
179 if (x == 'N') {
180 stop("flight curve can not be computed without the mgcv package, sorry")
181 }
182 }
183 your_dataset$DAYNO <- strptime(paste(your_dataset$DAY, your_dataset$MONTH,
184 your_dataset$YEAR, sep = "/"), "%d/%m/%Y")$yday + 1
185 dataset <- your_dataset[, c("SPECIES", "SITE", "YEAR", "MONTH",
186 "DAY", "DAYNO", "COUNT")]
187 sample_year <- unique(dataset$YEAR)
188 sample_year <- sample_year[order(sample_year)]
189 if (length(sample_year) >1 ) {
190 for (y in sample_year) {
191 dataset_y <- dataset[dataset$YEAR == y, ]
192 nsite <- length(unique(dataset_y$SITE))
193 # Determine missing days and add to dataset
194 sp_data_all <- year_day_func(dataset_y)
195 if (nsite > 200) {
196 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
197 200, replace = F)]), ]
198 sp_data_all <- sp_data_all
199 }
200 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
201 print(paste("Fitting the GAM for",as.character(sp_data_all$SPECIES[1]),"and year",y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time()))
202 if(length(unique(sp_data_all$SITE))>1){
203 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1,
204 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
205 }
206 else {
207 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
208 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
209 }
210 # Give a second try if the GAM does not converge the first time
211 if (class(gam_obj_site)[1] == "try-error") {
212 # Determine missing days and add to dataset
213 sp_data_all <- year_day_func(dataset_y)
214 if (nsite > 200) {
215 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
216 200, replace = F)]), ]
217 }
218 else {
219 sp_data_all <- sp_data_all
220 }
221 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
222 print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time(),"second try"))
223 if(length(unique(sp_data_all$SITE))>1){
224 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1,
225 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
226 }
227 else {
228 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
229 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
230 }
231 if (class(gam_obj_site)[1] == "try-error") {
232 print(paste("Error in fitting the flight period for",sp_data_all$SPECIES[1],"at year", y,"no convergence after two trial"))
233 sp_data_all[, "FITTED"] <- NA
234 sp_data_all[, "COUNT_IMPUTED"] <- NA
235 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
236 sp_data_all[, "NM"] <- NA
237 }
238 else {
239 # Generate a list of values for all days from the additive model and use
240 # these value to fill the missing observations
241 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
242 c("trimDAYNO", "SITE")], type = "response")
243 # force zeros at the beginning end end of the year
244 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
245 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
246 # if infinite number in predict replace with NA.
247 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
248 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
249 sp_data_all[, "FITTED"] <- NA
250 sp_data_all[, "COUNT_IMPUTED"] <- NA
251 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
252 sp_data_all[, "NM"] <- NA
253 }
254 else {
255 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
256 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
257 # Define the flight curve from the fitted values and append them over
258 # years (this is one flight curve per year for all site)
259 site_sums <- aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
260 FUN = sum)
261 # Rename sum column
262 names(site_sums)[names(site_sums) == "x"] <- "SITE_YR_FSUM"
263 # Add data to sp_data data.frame (ensure merge does not sort the data!)
264 sp_data_all = merge(sp_data_all, site_sums, by <- c("SITE"),
265 all = TRUE, sort = FALSE)
266 # Calculate normalized values
267 sp_data_all[, "NM"] <- sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
268 }
269 }
270 }
271 else {
272 # Generate a list of values for all days from the additive model and use
273 # these value to fill the missing observations
274 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
275 c("trimDAYNO", "SITE")], type = "response")
276 # force zeros at the beginning end end of the year
277 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
278 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
279 # if infinite number in predict replace with NA.
280 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
281 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
282 sp_data_all[, "FITTED"] <- NA
283 sp_data_all[, "COUNT_IMPUTED"] <- NA
284 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
285 sp_data_all[, "NM"] <- NA
286 }
287 else {
288 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
289 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
290 # Define the flight curve from the fitted values and append them over
291 # years (this is one flight curve per year for all site)
292 site_sums = aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
293 FUN = sum)
294 # Rename sum column
295 names(site_sums)[names(site_sums) == "x"] = "SITE_YR_FSUM"
296 # Add data to sp_data data.frame (ensure merge does not sort the data!)
297 sp_data_all = merge(sp_data_all, site_sums, by = c("SITE"), all = TRUE,
298 sort = FALSE)
299 # Calculate normalized values
300 sp_data_all[, "NM"] = sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
301 }
302 }
303 sp_data_filled <- sp_data_all
304 flight_curve <- data.frame(species = sp_data_filled$SPECIES, year = sp_data_filled$YEAR,
305 week = sp_data_filled$WEEK, DAYNO = sp_data_filled$DAYNO, DAYNO_adj = sp_data_filled$trimDAYNO,
306 nm = sp_data_filled$NM)[!duplicated(paste(sp_data_filled$YEAR,
307 sp_data_filled$DAYNO, sep = "_")), ]
308 flight_curve <- flight_curve[order(flight_curve$DAYNO), ]
309 # bind if exist else create
310 if (is.na(flight_curve$nm[1])) next()
311 if ("flight_pheno" %in% ls()) {
312 flight_pheno <- rbind(flight_pheno, flight_curve)
313 }
314 else {
315 flight_pheno <- flight_curve
316 }
317 } # end of year loop
318 }
319 else {
320 y <- unique(dataset$YEAR)
321 dataset_y <- dataset[dataset$YEAR == y, ]
322 nsite <- length(unique(dataset_y$SITE))
323 # Determine missing days and add to dataset
324 sp_data_all <- year_day_func(dataset_y)
325 if (nsite > 200) {
326 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
327 200, replace = F)]), ]
328 }
329 else {
330 sp_data_all <- sp_data_all
331 }
332 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
333 print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,":",Sys.time()))
334 if(length(unique(sp_data_all$SITE))>1){
335 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1,
336 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
337 }
338 else {
339 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
340 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
341 }
342 # Give a second try if the GAM does not converge the first time
343 if (class(gam_obj_site)[1] == "try-error") {
344 # Determine missing days and add to dataset
345 sp_data_all <- year_day_func(dataset_y)
346 if (nsite > 200) {
347 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
348 200, replace = F)]), ]
349 }
350 else {
351 sp_data_all <- sp_data_all
352 }
353 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
354 print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time(),"second try"))
355 if(length(unique(sp_data_all$SITE))>1){
356 gam_obj_site <- try(mgcv::bam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) - 1,
357 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
358 }
359 else {
360 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
361 data = sp_data_all, family = poisson(link = "log")), silent = TRUE)
362 }
363 if (class(gam_obj_site)[1] == "try-error") {
364 print(paste("Error in fitting the flight period for",sp_data_all$SPECIES[1],"at year", y,"no convergence after two trial"))
365 sp_data_all[, "FITTED"] <- NA
366 sp_data_all[, "COUNT_IMPUTED"] <- NA
367 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
368 sp_data_all[, "NM"] <- NA
369 }
370 else {
371 # Generate a list of values for all days from the additive model and use
372 # these value to fill the missing observations
373 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
374 c("trimDAYNO", "SITE")], type = "response")
375 # force zeros at the beginning end end of the year
376 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
377 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
378 # if infinite number in predict replace with NA.
379 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
380 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
381 sp_data_all[, "FITTED"] <- NA
382 sp_data_all[, "COUNT_IMPUTED"] <- NA
383 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
384 sp_data_all[, "NM"] <- NA
385 }
386 else {
387 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
388 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
389 # Define the flight curve from the fitted values and append them over
390 # years (this is one flight curve per year for all site)
391 site_sums <- aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
392 FUN = sum)
393 # Rename sum column
394 names(site_sums)[names(site_sums) == "x"] <- "SITE_YR_FSUM"
395 # Add data to sp_data data.frame (ensure merge does not sort the data!)
396 sp_data_all = merge(sp_data_all, site_sums, by <- c("SITE"),
397 all = TRUE, sort = FALSE)
398 # Calculate normalized values
399 sp_data_all[, "NM"] <- sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
400 }
401 }
402 }
403 else {
404 # Generate a list of values for all days from the additive model and use
405 # these value to fill the missing observations
406 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
407 c("trimDAYNO", "SITE")], type = "response")
408 # force zeros at the beginning end end of the year
409 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
410 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
411 # if infinite number in predict replace with NA.
412 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
413 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
414 sp_data_all[, "FITTED"] <- NA
415 sp_data_all[, "COUNT_IMPUTED"] <- NA
416 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
417 sp_data_all[, "NM"] <- NA
418 }
419 else {
420 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
421 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
422 # Define the flight curve from the fitted values and append them over
423 # years (this is one flight curve per year for all site)
424 site_sums = aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
425 FUN = sum)
426 # Rename sum column
427 names(site_sums)[names(site_sums) == "x"] = "SITE_YR_FSUM"
428 # Add data to sp_data data.frame (ensure merge does not sort the data!)
429 sp_data_all = merge(sp_data_all, site_sums, by = c("SITE"), all = TRUE,
430 sort = FALSE)
431 # Calculate normalized values
432 sp_data_all[, "NM"] = sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
433 }
434 }
435 sp_data_filled <- sp_data_all
436 flight_curve <- data.frame(species = sp_data_filled$SPECIES, year = sp_data_filled$YEAR,
437 week = sp_data_filled$WEEK, DAYNO = sp_data_filled$DAYNO, DAYNO_adj = sp_data_filled$trimDAYNO,
438 nm = sp_data_filled$NM)[!duplicated(paste(sp_data_filled$YEAR,
439 sp_data_filled$DAYNO, sep = "_")), ]
440 flight_curve <- flight_curve[order(flight_curve$DAYNO), ]
441 if (is.na(flight_curve$nm[1])){
442 flight_pheno <- data.frame()
443 }
444 else {
445 # bind if exist else create
446 if ("flight_pheno" %in% ls()) {
447 flight_pheno <- rbind(flight_pheno, flight_curve)
448 }
449 else {
450 flight_pheno <- flight_curve
451 }
452 }
453 }
454 return(flight_pheno)
455 }
456
457
458 #' abundance_index Function
459 #'
460 #' This function compute the Abundance Index across sites and years from your dataset and the regional flight curve
461 #' @param your_dataset A data.frame containing original species count data. The data format is a csv (comma "," separated) with 6 columns with headers, namely "species","transect_id","visit_year","visit_month","visit_day","sp_count"
462 #' @param flight_pheno A data.frame for the regional flight curve computed with the function flight_curve
463 #' @keywords standardize flight curve
464 #' @export
465 #' @examples
466 #' abundance_index()
467
468 abundance_index <- function(your_dataset,flight_pheno) {
469
470 your_dataset$DAYNO <- strptime(paste(your_dataset$DAY, your_dataset$MONTH,
471 your_dataset$YEAR, sep = "/"), "%d/%m/%Y")$yday + 1
472
473 dataset <- your_dataset[, c("SPECIES", "SITE", "YEAR", "MONTH",
474 "DAY", "DAYNO", "COUNT")]
475
476 sample_year <- unique(dataset$YEAR)
477 sample_year <- sample_year[order(sample_year)]
478
479
480 if (length(sample_year)>1){
481
482 for (y in sample_year) {
483
484 year_pheno <- flight_pheno[flight_pheno$year == y, ]
485
486 dataset_y <- dataset[dataset$YEAR == y, ]
487
488 sp_data_site <- year_day_func(dataset_y)
489 sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1
490
491 sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")],
492 by = c("DAYNO"), all.x = TRUE, sort = FALSE)
493
494 # compute proportion of the flight curve sampled due to missing visits
495 pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK,
496 NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR)
497 pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK,
498 sep = "_")
499 siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week),
500 function(x) sum(!is.na(x)) == 0)
501 pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in%
502 siteweeknocount$Group.1[siteweeknocount$x == TRUE], ]
503 pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE),
504 function(x) 1 - sum(x, na.rm = T))
505 names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED")
506
507 # remove samples outside the monitoring window
508 sp_data_site$COUNT[sp_data_site$nm==0] <- NA
509
510 # Compute the regional GAM index
511
512 if(length(unique(sp_data_site$SITE))>1){
513 glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site,
514 family = quasipoisson(link = "log"), control = list(maxit = 100))
515 } else {
516 glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site,
517 family = quasipoisson(link = "log"), control = list(maxit = 100))
518 }
519
520 sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site,
521 type = "response")
522 sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT
523 sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)]
524
525 ## add fitted value for missing mid-week data
526 sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in%
527 c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ]
528
529 ## remove all added mid-week values for weeks with real count
530 ## (observation)
531 sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK,
532 sep = "_")
533 siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week),
534 function(x) sum(!is.na(x)) > 0)
535 sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in%
536 siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) !=
537 "site_week"]
538
539 ## Compute the regional GAM index
540 print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time()))
541 regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED",
542 time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR"))
543
544 cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"),
545 all.x = TRUE, sort = FALSE)
546 names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled")
547
548 cumu_index <- cumu_index[order(cumu_index$SITE), ]
549
550 # bind if exist else create
551 if ("cumullated_indices" %in% ls()) {
552 cumullated_indices <- rbind(cumullated_indices, cumu_index)
553 } else {
554 cumullated_indices <- cumu_index
555 }
556
557 } # end of year loop
558
559 } else {
560
561 y <- unique(dataset$YEAR)
562 year_pheno <- flight_pheno[flight_pheno$year == y, ]
563
564 dataset_y <- dataset[dataset$YEAR == y, ]
565
566 sp_data_site <- year_day_func(dataset_y)
567 sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1
568
569 sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")],
570 by = c("DAYNO"), all.x = TRUE, sort = FALSE)
571
572 # compute proportion of the flight curve sampled due to missing visits
573 pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK,
574 NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR)
575 pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK,
576 sep = "_")
577 siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week),
578 function(x) sum(!is.na(x)) == 0)
579 pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in%
580 siteweeknocount$Group.1[siteweeknocount$x == TRUE], ]
581 pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE),
582 function(x) 1 - sum(x, na.rm = T))
583 names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED")
584
585 # remove samples outside the monitoring window
586 sp_data_site$COUNT[sp_data_site$nm==0] <- NA
587
588 # Compute the regional GAM index
589 if(length(unique(sp_data_site$SITE))>1){
590 glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site,
591 family = quasipoisson(link = "log"), control = list(maxit = 100))
592 } else {
593 glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site,
594 family = quasipoisson(link = "log"), control = list(maxit = 100))
595 }
596
597 sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site,
598 type = "response")
599 sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT
600 sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)]
601
602 # add fitted value for missing mid-week data
603 sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in%
604 c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ]
605
606 # remove all added mid-week values for weeks with real count
607 # (observation)
608 sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK,
609 sep = "_")
610 siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week),
611 function(x) sum(!is.na(x)) > 0)
612 sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in%
613 siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) !=
614 "site_week"]
615
616 # Compute the regional gam index
617 print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time()))
618 regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED",
619 time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR"))
620
621 cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"),
622 all.x = TRUE, sort = FALSE)
623 names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled")
624
625 cumu_index <- cumu_index[order(cumu_index$SITE), ]
626
627 # bind if exist else create
628 if ("cumullated_indices" %in% ls()) {
629 cumullated_indices <- rbind(cumullated_indices, cumu_index)
630 } else {
631 cumullated_indices <- cumu_index
632 }
633
634 }
635
636 return(cumullated_indices)
637
638 }