Mercurial > repos > ecology > regionalgam_autocor_acf

comparison dennis-gam-initial-functions.R @ 0:b416a363a2d5 draft default tip

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/regionalgam commit ffe42225fff8992501b743ebe2c78e50fddc4a4e
author ecology
date Thu, 20 Jun 2019 04:03:31 -0400
parents
children
comparison
equal deleted inserted replaced
-1:000000000000 0:b416a363a2d5
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6 packageStartupMessage(" The regionalGAM package that is no longer maintained, \n use the new rbms package instead.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, GamFamily = 'nb', MinVisit = 2, MinOccur = 1) {
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
184 flight_pheno <- data.frame()
185
186 your_dataset$DAYNO <- strptime(paste(your_dataset$DAY, your_dataset$MONTH,
187 your_dataset$YEAR, sep = "/"), "%d/%m/%Y")$yday + 1
188 dataset <- your_dataset[, c("SPECIES", "SITE", "YEAR", "MONTH",
189 "DAY", "DAYNO", "COUNT")]
190 sample_year <- unique(dataset$YEAR)
191 sample_year <- sample_year[order(sample_year)]
192
193 if (length(sample_year) >1 ) {
194 for (y in sample_year) {
195 dataset_y <- dataset[dataset$YEAR == y, ]
196
197 # subset sites with enough visit and occurence
198 occ <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) sum(x > 0))
199 vis <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) length(x))
200 dataset_y <- dataset_y[dataset_y$SITE %in% occ$SITE[occ$x >= MinOccur], ]
201 dataset_y <- dataset_y[dataset_y$SITE %in% vis$SITE[vis$x >= MinVisit], ]
202 nsite <- length(unique(dataset_y$SITE))
203 if (nsite < 1) {
204 print(paste("No sites with sufficient visits and occurence, MinOccur:", MinOccur, " MinVisit: ", MinVisit, " for " , dataset$SPECIES[1],"at year", y))
205 next
206 }
207 # Determine missing days and add to dataset
208 sp_data_all <- year_day_func(dataset_y)
209 if (nsite > 200) {
210 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
211 200, replace = F)]), ]
212 sp_data_all <- sp_data_all
213 }
214 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
215 print(paste("Fitting the GAM for",as.character(sp_data_all$SPECIES[1]),"and year",y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time()))
216 if(length(unique(sp_data_all$SITE))>1){
217 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1,
218 data = sp_data_all, family = GamFamily), silent = TRUE)
219 }
220 else {
221 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
222 data = sp_data_all, family = GamFamily), silent = TRUE)
223 }
224 # Give a second try if the GAM does not converge the first time
225 if (class(gam_obj_site)[1] == "try-error") {
226 # Determine missing days and add to dataset
227 sp_data_all <- year_day_func(dataset_y)
228 if (nsite > 200) {
229 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
230 200, replace = F)]), ]
231 }
232 else {
233 sp_data_all <- sp_data_all
234 }
235 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
236 print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time(),"second try"))
237 if(length(unique(sp_data_all$SITE))>1){
238 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1,
239 data = sp_data_all, family = GamFamily), silent = TRUE)
240 }
241 else {
242 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
243 data = sp_data_all, family = GamFamily), silent = TRUE)
244 }
245 if (class(gam_obj_site)[1] == "try-error") {
246 print(paste("Error in fitting the flight period for",sp_data_all$SPECIES[1],"at year", y,"no convergence after two trial"))
247 sp_data_all[, "FITTED"] <- NA
248 sp_data_all[, "COUNT_IMPUTED"] <- NA
249 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
250 sp_data_all[, "NM"] <- NA
251 }
252 else {
253 # Generate a list of values for all days from the additive model and use
254 # these value to fill the missing observations
255 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
256 c("trimDAYNO", "SITE")], type = "response")
257 # force zeros at the beginning end end of the year
258 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
259 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
260 # if infinite number in predict replace with NA.
261 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
262 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
263 sp_data_all[, "FITTED"] <- NA
264 sp_data_all[, "COUNT_IMPUTED"] <- NA
265 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
266 sp_data_all[, "NM"] <- NA
267 }
268 else {
269 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
270 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
271 # Define the flight curve from the fitted values and append them over
272 # years (this is one flight curve per year for all site)
273 site_sums <- aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
274 FUN = sum)
275 # Rename sum column
276 names(site_sums)[names(site_sums) == "x"] <- "SITE_YR_FSUM"
277 # Add data to sp_data data.frame (ensure merge does not sort the data!)
278 sp_data_all = merge(sp_data_all, site_sums, by <- c("SITE"),
279 all = TRUE, sort = FALSE)
280 # Calculate normalized values
281 sp_data_all[, "NM"] <- sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
282 }
283 }
284 }
285 else {
286 # Generate a list of values for all days from the additive model and use
287 # these value to fill the missing observations
288 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
289 c("trimDAYNO", "SITE")], type = "response")
290 # force zeros at the beginning end end of the year
291 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
292 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
293 # if infinite number in predict replace with NA.
294 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
295 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
296 sp_data_all[, "FITTED"] <- NA
297 sp_data_all[, "COUNT_IMPUTED"] <- NA
298 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
299 sp_data_all[, "NM"] <- NA
300 }
301 else {
302 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
303 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
304 # Define the flight curve from the fitted values and append them over
305 # years (this is one flight curve per year for all site)
306 site_sums = aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
307 FUN = sum)
308 # Rename sum column
309 names(site_sums)[names(site_sums) == "x"] = "SITE_YR_FSUM"
310 # Add data to sp_data data.frame (ensure merge does not sort the data!)
311 sp_data_all = merge(sp_data_all, site_sums, by = c("SITE"), all = TRUE,
312 sort = FALSE)
313 # Calculate normalized values
314 sp_data_all[, "NM"] = sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
315 }
316 }
317 sp_data_filled <- sp_data_all
318 flight_curve <- data.frame(species = sp_data_filled$SPECIES, year = sp_data_filled$YEAR,
319 week = sp_data_filled$WEEK, DAYNO = sp_data_filled$DAYNO, DAYNO_adj = sp_data_filled$trimDAYNO,
320 nm = sp_data_filled$NM)[!duplicated(paste(sp_data_filled$YEAR,
321 sp_data_filled$DAYNO, sep = "_")), ]
322 flight_curve <- flight_curve[order(flight_curve$DAYNO), ]
323 # bind if exist else create
324 if (is.na(flight_curve$nm[1])) next()
325
326 flight_pheno <- rbind(flight_pheno, flight_curve)
327
328 } # end of year loop
329 }
330 else {
331 y <- unique(dataset$YEAR)
332 dataset_y <- dataset[dataset$YEAR == y, ]
333 # subset sites with enough visit and occurence
334 occ <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) sum(x > 0))
335 vis <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) length(x))
336 dataset_y <- dataset_y[dataset_y$SITE %in% occ$SITE[occ$x >= MinOccur], ]
337 dataset_y <- dataset_y[dataset_y$SITE %in% vis$SITE[vis$x >= MinVisit], ]
338 nsite <- length(unique(dataset_y$SITE))
339 if (nsite < 1) {
340 stop(paste("No sites with sufficient visits and occurence, MinOccur:", MinOccur, " MinVisit: ", MinVisit, " for " ,dataset$SPECIES[1],"at year", y))
341 }
342 # Determine missing days and add to dataset
343 sp_data_all <- year_day_func(dataset_y)
344 if (nsite > 200) {
345 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
346 200, replace = F)]), ]
347 }
348 else {
349 sp_data_all <- sp_data_all
350 }
351 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
352 print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,":",Sys.time()))
353 if(length(unique(sp_data_all$SITE))>1){
354 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1,
355 data = sp_data_all, family = GamFamily), silent = TRUE)
356 }
357 else {
358 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
359 data = sp_data_all, family = GamFamily), silent = TRUE)
360 }
361 # Give a second try if the GAM does not converge the first time
362 if (class(gam_obj_site)[1] == "try-error") {
363 # Determine missing days and add to dataset
364 sp_data_all <- year_day_func(dataset_y)
365 if (nsite > 200) {
366 sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite,
367 200, replace = F)]), ]
368 }
369 else {
370 sp_data_all <- sp_data_all
371 }
372 sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1
373 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"))
374 if(length(unique(sp_data_all$SITE))>1){
375 gam_obj_site <- try(mgcv::bam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) - 1,
376 data = sp_data_all, family = GamFamily), silent = TRUE)
377 }
378 else {
379 gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1,
380 data = sp_data_all, family = GamFamily), silent = TRUE)
381 }
382 if (class(gam_obj_site)[1] == "try-error") {
383 print(paste("Error in fitting the flight period for",sp_data_all$SPECIES[1],"at year", y,"no convergence after two trial"))
384 sp_data_all[, "FITTED"] <- NA
385 sp_data_all[, "COUNT_IMPUTED"] <- NA
386 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
387 sp_data_all[, "NM"] <- NA
388 }
389 else {
390 # Generate a list of values for all days from the additive model and use
391 # these value to fill the missing observations
392 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
393 c("trimDAYNO", "SITE")], type = "response")
394 # force zeros at the beginning end end of the year
395 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
396 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
397 # if infinite number in predict replace with NA.
398 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
399 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
400 sp_data_all[, "FITTED"] <- NA
401 sp_data_all[, "COUNT_IMPUTED"] <- NA
402 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
403 sp_data_all[, "NM"] <- NA
404 }
405 else {
406 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
407 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
408 # Define the flight curve from the fitted values and append them over
409 # years (this is one flight curve per year for all site)
410 site_sums <- aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
411 FUN = sum)
412 # Rename sum column
413 names(site_sums)[names(site_sums) == "x"] <- "SITE_YR_FSUM"
414 # Add data to sp_data data.frame (ensure merge does not sort the data!)
415 sp_data_all = merge(sp_data_all, site_sums, by <- c("SITE"),
416 all = TRUE, sort = FALSE)
417 # Calculate normalized values
418 sp_data_all[, "NM"] <- sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
419 }
420 }
421 }
422 else {
423 # Generate a list of values for all days from the additive model and use
424 # these value to fill the missing observations
425 sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[,
426 c("trimDAYNO", "SITE")], type = "response")
427 # force zeros at the beginning end end of the year
428 sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0
429 sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0
430 # if infinite number in predict replace with NA.
431 if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){
432 print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values"))
433 sp_data_all[, "FITTED"] <- NA
434 sp_data_all[, "COUNT_IMPUTED"] <- NA
435 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA
436 sp_data_all[, "NM"] <- NA
437 }
438 else {
439 sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT
440 sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)]
441 # Define the flight curve from the fitted values and append them over
442 # years (this is one flight curve per year for all site)
443 site_sums = aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE),
444 FUN = sum)
445 # Rename sum column
446 names(site_sums)[names(site_sums) == "x"] = "SITE_YR_FSUM"
447 # Add data to sp_data data.frame (ensure merge does not sort the data!)
448 sp_data_all = merge(sp_data_all, site_sums, by = c("SITE"), all = TRUE,
449 sort = FALSE)
450 # Calculate normalized values
451 sp_data_all[, "NM"] = sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM
452 }
453 }
454 sp_data_filled <- sp_data_all
455 flight_curve <- data.frame(species = sp_data_filled$SPECIES, year = sp_data_filled$YEAR,
456 week = sp_data_filled$WEEK, DAYNO = sp_data_filled$DAYNO, DAYNO_adj = sp_data_filled$trimDAYNO,
457 nm = sp_data_filled$NM)[!duplicated(paste(sp_data_filled$YEAR,
458 sp_data_filled$DAYNO, sep = "_")), ]
459 flight_curve <- flight_curve[order(flight_curve$DAYNO), ]
460
461 flight_pheno <- rbind(flight_pheno, flight_curve)
462
463 }
464 return(flight_pheno)
465 }
466
467
468 #' abundance_index Function
469 #'
470 #' This function compute the Abundance Index across sites and years from your dataset and the regional flight curve
471 #' @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"
472 #' @param flight_pheno A data.frame for the regional flight curve computed with the function flight_curve
473 #' @keywords standardize flight curve
474 #' @export
475 #' @examples
476 #' abundance_index()
477
478 abundance_index <- function(your_dataset,flight_pheno) {
479
480 your_dataset$DAYNO <- strptime(paste(your_dataset$DAY, your_dataset$MONTH,
481 your_dataset$YEAR, sep = "/"), "%d/%m/%Y")$yday + 1
482
483 dataset <- your_dataset[, c("SPECIES", "SITE", "YEAR", "MONTH",
484 "DAY", "DAYNO", "COUNT")]
485
486 sample_year <- unique(dataset$YEAR)
487 sample_year <- sample_year[order(sample_year)]
488
489
490 if (length(sample_year)>1){
491
492 for (y in sample_year) {
493
494 year_pheno <- flight_pheno[flight_pheno$year == y, ]
495
496 dataset_y <- dataset[dataset$YEAR == y, ]
497
498 sp_data_site <- year_day_func(dataset_y)
499 sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1
500
501 sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")],
502 by = c("DAYNO"), all.x = TRUE, sort = FALSE)
503
504 # compute proportion of the flight curve sampled due to missing visits
505 pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK,
506 NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR)
507 pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK,
508 sep = "_")
509 siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week),
510 function(x) sum(!is.na(x)) == 0)
511 pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in%
512 siteweeknocount$Group.1[siteweeknocount$x == TRUE], ]
513 pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE),
514 function(x) 1 - sum(x, na.rm = T))
515 names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED")
516
517 # remove samples outside the monitoring window
518 sp_data_site$COUNT[sp_data_site$nm==0] <- NA
519
520 # Compute the regional GAM index
521
522 if(length(unique(sp_data_site$SITE))>1){
523 glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site,
524 family = quasipoisson(link = "log"), control = list(maxit = 100))
525 } else {
526 glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site,
527 family = quasipoisson(link = "log"), control = list(maxit = 100))
528 }
529
530 sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site,
531 type = "response")
532 sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT
533 sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)]
534
535 ## add fitted value for missing mid-week data
536 sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in%
537 c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ]
538
539 ## remove all added mid-week values for weeks with real count
540 ## (observation)
541 sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK,
542 sep = "_")
543 siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week),
544 function(x) sum(!is.na(x)) > 0)
545 sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in%
546 siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) !=
547 "site_week"]
548
549 ## Compute the regional GAM index
550 print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time()))
551 regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED",
552 time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR"))
553
554 cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"),
555 all.x = TRUE, sort = FALSE)
556 names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled")
557
558 cumu_index <- cumu_index[order(cumu_index$SITE), ]
559
560 # bind if exist else create
561 if ("cumullated_indices" %in% ls()) {
562 cumullated_indices <- rbind(cumullated_indices, cumu_index)
563 } else {
564 cumullated_indices <- cumu_index
565 }
566
567 } # end of year loop
568
569 } else {
570
571 y <- unique(dataset$YEAR)
572 year_pheno <- flight_pheno[flight_pheno$year == y, ]
573
574 dataset_y <- dataset[dataset$YEAR == y, ]
575
576 sp_data_site <- year_day_func(dataset_y)
577 sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1
578
579 sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")],
580 by = c("DAYNO"), all.x = TRUE, sort = FALSE)
581
582 # compute proportion of the flight curve sampled due to missing visits
583 pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK,
584 NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR)
585 pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK,
586 sep = "_")
587 siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week),
588 function(x) sum(!is.na(x)) == 0)
589 pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in%
590 siteweeknocount$Group.1[siteweeknocount$x == TRUE], ]
591 pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE),
592 function(x) 1 - sum(x, na.rm = T))
593 names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED")
594
595 # remove samples outside the monitoring window
596 sp_data_site$COUNT[sp_data_site$nm==0] <- NA
597
598 # Compute the regional GAM index
599 if(length(unique(sp_data_site$SITE))>1){
600 glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site,
601 family = quasipoisson(link = "log"), control = list(maxit = 100))
602 } else {
603 glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site,
604 family = quasipoisson(link = "log"), control = list(maxit = 100))
605 }
606
607 sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site,
608 type = "response")
609 sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT
610 sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)]
611
612 # add fitted value for missing mid-week data
613 sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in%
614 c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ]
615
616 # remove all added mid-week values for weeks with real count
617 # (observation)
618 sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK,
619 sep = "_")
620 siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week),
621 function(x) sum(!is.na(x)) > 0)
622 sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in%
623 siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) !=
624 "site_week"]
625
626 # Compute the regional gam index
627 print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time()))
628 regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED",
629 time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR"))
630
631 cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"),
632 all.x = TRUE, sort = FALSE)
633 names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled")
634
635 cumu_index <- cumu_index[order(cumu_index$SITE), ]
636
637 # bind if exist else create
638 if ("cumullated_indices" %in% ls()) {
639 cumullated_indices <- rbind(cumullated_indices, cumu_index)
640 } else {
641 cumullated_indices <- cumu_index
642 }
643
644 }
645
646 return(cumullated_indices)
647
648 }