Mercurial > repos > ecology > regionalgam_ab_index
diff dennis-gam-initial-functions.R @ 0:d0ee713d4758 draft default tip
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/regionalgam commit ffe42225fff8992501b743ebe2c78e50fddc4a4e
| author | ecology |
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| date | Thu, 20 Jun 2019 04:05:06 -0400 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/dennis-gam-initial-functions.R Thu Jun 20 04:05:06 2019 -0400 @@ -0,0 +1,648 @@ +### R-Script Adapted from script provided by the CEH, UK BY: Reto Schmucki [ reto.schmucki@mail.mcgill.ca] +### DATE: 14 July 2014 function to run two stage model in DENNIS et al. 2013 + + +.onAttach <- function(libname, pkgname) { + packageStartupMessage(" The regionalGAM package that is no longer maintained, \n use the new rbms package instead. \n + devtools::install_github(\"RetoSchmucki/rbms\", force=TRUE)") +} + + +#' year_day_func Function +#' This function generate the full sequence of days, months and include the observation to that file. +#' @param sp_data A data.frame with your observation. +#' @keywords year days +#' @export +#' @author Reto Schmucki +#' @examples +#' year_day_func() + + +# FUNCTIONS + +year_day_func = function(sp_data) { + + year <- unique(sp_data$YEAR) + + origin.d <- paste(year, "01-01", sep = "-") + if ((year%%4 == 0) & ((year%%100 != 0) | (year%%400 == 0))) { + nday <- 366 + } else { + nday <- 365 + } + + date.serie <- as.POSIXlt(seq(as.Date(origin.d), length = nday, by = "day"), format = "%Y-%m-%d") + + dayno <- as.numeric(julian(date.serie, origin = as.Date(origin.d)) + 1) + month <- as.numeric(strftime(date.serie, format = "%m")) + week <- as.numeric(strftime(date.serie, format = "%W")) + week_day <- as.numeric(strftime(date.serie, format = "%u")) + day <- as.numeric(strftime(date.serie, format = "%d")) + + site_list <- sp_data[!duplicated(sp_data$SITE), c("SITE")] + + all_day_site <- data.frame(SPECIES = sp_data$SPECIES[1], SITE = rep(site_list, rep(nday, length(site_list))), + YEAR = sp_data$YEAR[1], MONTH = month, WEEK = week, DAY = day, DAY_WEEK = week_day, DAYNO = dayno, + COUNT = NA) + + count_index <- match(paste(sp_data$SITE, sp_data$DAYNO, sep = "_"), paste(all_day_site$SITE, all_day_site$DAYNO, + sep = "_")) + all_day_site$COUNT[count_index] <- sp_data$COUNT + site_count_length <- aggregate(sp_data$COUNT, by = list(sp_data$SITE), function(x) list(1:length(x))) + names(site_count_length$x) <- as.character(site_count_length$Group.1) + site_countno <- utils::stack(site_count_length$x) + all_day_site$COUNTNO <- NA + all_day_site$COUNTNO[count_index] <- site_countno$values # add count number to ease extraction of single count + + # Add zero to close observation season two weeks before and after the first and last + first_obs <- min(all_day_site$DAYNO[!is.na(all_day_site$COUNT)]) + last_obs <- max(all_day_site$DAYNO[!is.na(all_day_site$COUNT)]) + + closing_season <- c((first_obs - 11):(first_obs - 7), (last_obs + 7):(last_obs + 11)) + + # If closing season is before day 1 or day 365, simply set the first and last 5 days to 0 + if (min(closing_season) < 1) + closing_season[1:5] <- c(1:5) + if (max(closing_season) > nday) + closing_season[6:10] <- c((nday - 4):nday) + + all_day_site$COUNT[all_day_site$DAYNO %in% closing_season] <- 0 + all_day_site$ANCHOR <- 0 + all_day_site$ANCHOR[all_day_site$DAYNO %in% closing_season] <- 1 + + all_day_site <- all_day_site[order(all_day_site$SITE, all_day_site$DAYNO), ] + + return(all_day_site) +} + + +#' trap_area Function +#' +#' This function compute the area under the curve using the trapezoid method. +#' @param x A vector or a two columns matrix. +#' @param y A vector, Default is NULL +#' @keywords trapezoid +#' @export +#' @examples +#' trap_area() + + +trap_area = function(x, y = NULL) { + # If y is null and x has multiple columns then set y to x[,2] and x to x[,1] + if (is.null(y)) { + if (length(dim(x)) == 2) { + y = x[, 2] + x = x[, 1] + } else { + stop("ERROR: need to either specifiy both x and y or supply a two column data.frame/matrix to x") + } + } + + # Check x and y are same length + if (length(x) != length(y)) { + stop("ERROR: x and y need to be the same length") + } + + # Need to exclude any pairs that are NA for either x or y + rm_inds = which(is.na(x) | is.na(y)) + if (length(rm_inds) > 0) { + x = x[-rm_inds] + y = y[-rm_inds] + } + + # Determine values of trapezoids under curve Get inds + inds = 1:(length(x) - 1) + # Determine area using trapezoidal rule Area = ( (b1 + b2)/2 ) * h where b1 and b2 are lengths of bases + # (the parallel sides) and h is the height (the perpendicular distance between two bases) + areas = ((y[inds] + y[inds + 1])/2) * diff(x) + + # total area is sum of all trapezoid areas + tot_area = sum(areas) + + # Return total area + return(tot_area) +} + + +#' trap_index Function +#' +#' This function compute the area under the curve (Abundance Index) across species, sites and years +#' @param sp_data A data.frame containing species count data generated from the year_day_func() +#' @param y A vector, Default is NULL +#' @keywords Abundance index +#' @export +#' @examples +#' trap_index() + + + +trap_index = function(sp_data, data_col = "IMP", time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR")) { + + # Build output data.frame + out_obj = unique(sp_data[, by_col]) + # Set row.names to be equal to collapsing of output rows (will be unique, you need them to make uploading + # values back to data.frame will be easier) + row.names(out_obj) = apply(out_obj, 1, paste, collapse = "_") + + # Using this row.names from out_obj above as index in by function to loop through values all unique combs + # of by_cols and fit trap_area to data + ind_dat = by(sp_data[, c(time_col, data_col)], apply(sp_data[, by_col], 1, paste, collapse = "_"), trap_area) + + # Add this data to output object + out_obj[names(ind_dat), "SINDEX"] = round(ind_dat/7, 1) + + # Set row.names to defaults + row.names(out_obj) = NULL + + # Return output object + return(out_obj) +} + + +#' flight_curve Function +#' This function compute the flight curve across and years +#' @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" +#' @keywords standardize flight curve +#' @export +#' @examples +#' flight_curve() + + +flight_curve <- function(your_dataset, GamFamily = 'nb', MinVisit = 2, MinOccur = 1) { + + if("mgcv" %in% installed.packages() == "FALSE") { + print("mgcv package is not installed.") + x <- readline("Do you want to install it? Y/N") + if (x == 'Y') { + install.packages("mgcv") + } + if (x == 'N') { + stop("flight curve can not be computed without the mgcv package, sorry") + } + } + + flight_pheno <- data.frame() + + your_dataset$DAYNO <- strptime(paste(your_dataset$DAY, your_dataset$MONTH, + your_dataset$YEAR, sep = "/"), "%d/%m/%Y")$yday + 1 + dataset <- your_dataset[, c("SPECIES", "SITE", "YEAR", "MONTH", + "DAY", "DAYNO", "COUNT")] + sample_year <- unique(dataset$YEAR) + sample_year <- sample_year[order(sample_year)] + + if (length(sample_year) >1 ) { + for (y in sample_year) { + dataset_y <- dataset[dataset$YEAR == y, ] + + # subset sites with enough visit and occurence + occ <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) sum(x > 0)) + vis <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) length(x)) + dataset_y <- dataset_y[dataset_y$SITE %in% occ$SITE[occ$x >= MinOccur], ] + dataset_y <- dataset_y[dataset_y$SITE %in% vis$SITE[vis$x >= MinVisit], ] + nsite <- length(unique(dataset_y$SITE)) + if (nsite < 1) { + print(paste("No sites with sufficient visits and occurence, MinOccur:", MinOccur, " MinVisit: ", MinVisit, " for " , dataset$SPECIES[1],"at year", y)) + next + } + # Determine missing days and add to dataset + sp_data_all <- year_day_func(dataset_y) + if (nsite > 200) { + sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite, + 200, replace = F)]), ] + sp_data_all <- sp_data_all + } + sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1 + print(paste("Fitting the GAM for",as.character(sp_data_all$SPECIES[1]),"and year",y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time())) + if(length(unique(sp_data_all$SITE))>1){ + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + else { + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + # Give a second try if the GAM does not converge the first time + if (class(gam_obj_site)[1] == "try-error") { + # Determine missing days and add to dataset + sp_data_all <- year_day_func(dataset_y) + if (nsite > 200) { + sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite, + 200, replace = F)]), ] + } + else { + sp_data_all <- sp_data_all + } + sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1 + print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,"with",length(unique(sp_data_all$SITE)),"sites :",Sys.time(),"second try")) + if(length(unique(sp_data_all$SITE))>1){ + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + else { + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + if (class(gam_obj_site)[1] == "try-error") { + print(paste("Error in fitting the flight period for",sp_data_all$SPECIES[1],"at year", y,"no convergence after two trial")) + sp_data_all[, "FITTED"] <- NA + sp_data_all[, "COUNT_IMPUTED"] <- NA + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA + sp_data_all[, "NM"] <- NA + } + else { + # Generate a list of values for all days from the additive model and use + # these value to fill the missing observations + sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[, + c("trimDAYNO", "SITE")], type = "response") + # force zeros at the beginning end end of the year + sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0 + sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0 + # if infinite number in predict replace with NA. + if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){ + print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values")) + sp_data_all[, "FITTED"] <- NA + sp_data_all[, "COUNT_IMPUTED"] <- NA + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA + sp_data_all[, "NM"] <- NA + } + else { + sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)] + # Define the flight curve from the fitted values and append them over + # years (this is one flight curve per year for all site) + site_sums <- aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE), + FUN = sum) + # Rename sum column + names(site_sums)[names(site_sums) == "x"] <- "SITE_YR_FSUM" + # Add data to sp_data data.frame (ensure merge does not sort the data!) + sp_data_all = merge(sp_data_all, site_sums, by <- c("SITE"), + all = TRUE, sort = FALSE) + # Calculate normalized values + sp_data_all[, "NM"] <- sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM + } + } + } + else { + # Generate a list of values for all days from the additive model and use + # these value to fill the missing observations + sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[, + c("trimDAYNO", "SITE")], type = "response") + # force zeros at the beginning end end of the year + sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0 + sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0 + # if infinite number in predict replace with NA. + if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){ + print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values")) + sp_data_all[, "FITTED"] <- NA + sp_data_all[, "COUNT_IMPUTED"] <- NA + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA + sp_data_all[, "NM"] <- NA + } + else { + sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)] + # Define the flight curve from the fitted values and append them over + # years (this is one flight curve per year for all site) + site_sums = aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE), + FUN = sum) + # Rename sum column + names(site_sums)[names(site_sums) == "x"] = "SITE_YR_FSUM" + # Add data to sp_data data.frame (ensure merge does not sort the data!) + sp_data_all = merge(sp_data_all, site_sums, by = c("SITE"), all = TRUE, + sort = FALSE) + # Calculate normalized values + sp_data_all[, "NM"] = sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM + } + } + sp_data_filled <- sp_data_all + flight_curve <- data.frame(species = sp_data_filled$SPECIES, year = sp_data_filled$YEAR, + week = sp_data_filled$WEEK, DAYNO = sp_data_filled$DAYNO, DAYNO_adj = sp_data_filled$trimDAYNO, + nm = sp_data_filled$NM)[!duplicated(paste(sp_data_filled$YEAR, + sp_data_filled$DAYNO, sep = "_")), ] + flight_curve <- flight_curve[order(flight_curve$DAYNO), ] + # bind if exist else create + if (is.na(flight_curve$nm[1])) next() + + flight_pheno <- rbind(flight_pheno, flight_curve) + + } # end of year loop + } + else { + y <- unique(dataset$YEAR) + dataset_y <- dataset[dataset$YEAR == y, ] + # subset sites with enough visit and occurence + occ <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) sum(x > 0)) + vis <- aggregate(dataset_y$COUNT, by = list(SITE = dataset_y$SITE), function(x) length(x)) + dataset_y <- dataset_y[dataset_y$SITE %in% occ$SITE[occ$x >= MinOccur], ] + dataset_y <- dataset_y[dataset_y$SITE %in% vis$SITE[vis$x >= MinVisit], ] + nsite <- length(unique(dataset_y$SITE)) + if (nsite < 1) { + stop(paste("No sites with sufficient visits and occurence, MinOccur:", MinOccur, " MinVisit: ", MinVisit, " for " ,dataset$SPECIES[1],"at year", y)) + } + # Determine missing days and add to dataset + sp_data_all <- year_day_func(dataset_y) + if (nsite > 200) { + sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite, + 200, replace = F)]), ] + } + else { + sp_data_all <- sp_data_all + } + sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1 + print(paste("Fitting the GAM for",sp_data_all$SPECIES[1],"at year", y,":",Sys.time())) + if(length(unique(sp_data_all$SITE))>1){ + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + else { + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + # Give a second try if the GAM does not converge the first time + if (class(gam_obj_site)[1] == "try-error") { + # Determine missing days and add to dataset + sp_data_all <- year_day_func(dataset_y) + if (nsite > 200) { + sp_data_all <- sp_data_all[as.character(sp_data_all$SITE) %in% as.character(unique(dataset_y$SITE)[sample(1:nsite, + 200, replace = F)]), ] + } + else { + sp_data_all <- sp_data_all + } + sp_data_all$trimDAYNO <- sp_data_all$DAYNO - min(sp_data_all$DAYNO) + 1 + 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")) + if(length(unique(sp_data_all$SITE))>1){ + gam_obj_site <- try(mgcv::bam(COUNT ~ s(trimDAYNO, bs = "cr") + as.factor(SITE) - 1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + else { + gam_obj_site <- try(mgcv::gam(COUNT ~ s(trimDAYNO, bs = "cr") -1, + data = sp_data_all, family = GamFamily), silent = TRUE) + } + if (class(gam_obj_site)[1] == "try-error") { + print(paste("Error in fitting the flight period for",sp_data_all$SPECIES[1],"at year", y,"no convergence after two trial")) + sp_data_all[, "FITTED"] <- NA + sp_data_all[, "COUNT_IMPUTED"] <- NA + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA + sp_data_all[, "NM"] <- NA + } + else { + # Generate a list of values for all days from the additive model and use + # these value to fill the missing observations + sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[, + c("trimDAYNO", "SITE")], type = "response") + # force zeros at the beginning end end of the year + sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0 + sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0 + # if infinite number in predict replace with NA. + if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){ + print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values")) + sp_data_all[, "FITTED"] <- NA + sp_data_all[, "COUNT_IMPUTED"] <- NA + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA + sp_data_all[, "NM"] <- NA + } + else { + sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)] + # Define the flight curve from the fitted values and append them over + # years (this is one flight curve per year for all site) + site_sums <- aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE), + FUN = sum) + # Rename sum column + names(site_sums)[names(site_sums) == "x"] <- "SITE_YR_FSUM" + # Add data to sp_data data.frame (ensure merge does not sort the data!) + sp_data_all = merge(sp_data_all, site_sums, by <- c("SITE"), + all = TRUE, sort = FALSE) + # Calculate normalized values + sp_data_all[, "NM"] <- sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM + } + } + } + else { + # Generate a list of values for all days from the additive model and use + # these value to fill the missing observations + sp_data_all[, "FITTED"] <- mgcv::predict.gam(gam_obj_site, newdata = sp_data_all[, + c("trimDAYNO", "SITE")], type = "response") + # force zeros at the beginning end end of the year + sp_data_all[sp_data_all$trimDAYNO < 60, "FITTED"] <- 0 + sp_data_all[sp_data_all$trimDAYNO > 305, "FITTED"] <- 0 + # if infinite number in predict replace with NA. + if(sum(is.infinite(sp_data_all[, "FITTED"]))>0){ + print(paste("Error in the flight period for",sp_data_all$SPECIES[1],"at year", y,"weird predicted values")) + sp_data_all[, "FITTED"] <- NA + sp_data_all[, "COUNT_IMPUTED"] <- NA + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- NA + sp_data_all[, "NM"] <- NA + } + else { + sp_data_all[, "COUNT_IMPUTED"] <- sp_data_all$COUNT + sp_data_all[is.na(sp_data_all$COUNT), "COUNT_IMPUTED"] <- sp_data_all$FITTED[is.na(sp_data_all$COUNT)] + # Define the flight curve from the fitted values and append them over + # years (this is one flight curve per year for all site) + site_sums = aggregate(sp_data_all$FITTED, by = list(SITE = sp_data_all$SITE), + FUN = sum) + # Rename sum column + names(site_sums)[names(site_sums) == "x"] = "SITE_YR_FSUM" + # Add data to sp_data data.frame (ensure merge does not sort the data!) + sp_data_all = merge(sp_data_all, site_sums, by = c("SITE"), all = TRUE, + sort = FALSE) + # Calculate normalized values + sp_data_all[, "NM"] = sp_data_all$FITTED/sp_data_all$SITE_YR_FSUM + } + } + sp_data_filled <- sp_data_all + flight_curve <- data.frame(species = sp_data_filled$SPECIES, year = sp_data_filled$YEAR, + week = sp_data_filled$WEEK, DAYNO = sp_data_filled$DAYNO, DAYNO_adj = sp_data_filled$trimDAYNO, + nm = sp_data_filled$NM)[!duplicated(paste(sp_data_filled$YEAR, + sp_data_filled$DAYNO, sep = "_")), ] + flight_curve <- flight_curve[order(flight_curve$DAYNO), ] + + flight_pheno <- rbind(flight_pheno, flight_curve) + + } + return(flight_pheno) +} + + +#' abundance_index Function +#' +#' This function compute the Abundance Index across sites and years from your dataset and the regional flight curve +#' @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" +#' @param flight_pheno A data.frame for the regional flight curve computed with the function flight_curve +#' @keywords standardize flight curve +#' @export +#' @examples +#' abundance_index() + +abundance_index <- function(your_dataset,flight_pheno) { + +your_dataset$DAYNO <- strptime(paste(your_dataset$DAY, your_dataset$MONTH, + your_dataset$YEAR, sep = "/"), "%d/%m/%Y")$yday + 1 + +dataset <- your_dataset[, c("SPECIES", "SITE", "YEAR", "MONTH", + "DAY", "DAYNO", "COUNT")] + +sample_year <- unique(dataset$YEAR) +sample_year <- sample_year[order(sample_year)] + + +if (length(sample_year)>1){ + +for (y in sample_year) { + + year_pheno <- flight_pheno[flight_pheno$year == y, ] + + dataset_y <- dataset[dataset$YEAR == y, ] + + sp_data_site <- year_day_func(dataset_y) + sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1 + + sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")], + by = c("DAYNO"), all.x = TRUE, sort = FALSE) + + # compute proportion of the flight curve sampled due to missing visits + pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK, + NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR) + pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK, + sep = "_") + siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week), + function(x) sum(!is.na(x)) == 0) + pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in% + siteweeknocount$Group.1[siteweeknocount$x == TRUE], ] + pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE), + function(x) 1 - sum(x, na.rm = T)) + names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED") + + # remove samples outside the monitoring window + sp_data_site$COUNT[sp_data_site$nm==0] <- NA + + # Compute the regional GAM index + + if(length(unique(sp_data_site$SITE))>1){ + glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site, + family = quasipoisson(link = "log"), control = list(maxit = 100)) + } else { + glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site, + family = quasipoisson(link = "log"), control = list(maxit = 100)) + } + + sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site, + type = "response") + sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT + sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)] + + ## add fitted value for missing mid-week data + sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in% + c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ] + + ## remove all added mid-week values for weeks with real count + ## (observation) + sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK, + sep = "_") + siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week), + function(x) sum(!is.na(x)) > 0) + sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in% + siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) != + "site_week"] + + ## Compute the regional GAM index + print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time())) + regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED", + time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR")) + + cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"), + all.x = TRUE, sort = FALSE) + names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled") + + cumu_index <- cumu_index[order(cumu_index$SITE), ] + + # bind if exist else create + if ("cumullated_indices" %in% ls()) { + cumullated_indices <- rbind(cumullated_indices, cumu_index) + } else { + cumullated_indices <- cumu_index + } + +} # end of year loop + +} else { + + y <- unique(dataset$YEAR) + year_pheno <- flight_pheno[flight_pheno$year == y, ] + + dataset_y <- dataset[dataset$YEAR == y, ] + + sp_data_site <- year_day_func(dataset_y) + sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1 + + sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")], + by = c("DAYNO"), all.x = TRUE, sort = FALSE) + + # compute proportion of the flight curve sampled due to missing visits + pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK, + NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR) + pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK, + sep = "_") + siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week), + function(x) sum(!is.na(x)) == 0) + pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in% + siteweeknocount$Group.1[siteweeknocount$x == TRUE], ] + pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE), + function(x) 1 - sum(x, na.rm = T)) + names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED") + + # remove samples outside the monitoring window + sp_data_site$COUNT[sp_data_site$nm==0] <- NA + + # Compute the regional GAM index + if(length(unique(sp_data_site$SITE))>1){ + glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site, + family = quasipoisson(link = "log"), control = list(maxit = 100)) + } else { + glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site, + family = quasipoisson(link = "log"), control = list(maxit = 100)) + } + + sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site, + type = "response") + sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT + sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)] + + # add fitted value for missing mid-week data + sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in% + c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ] + + # remove all added mid-week values for weeks with real count + # (observation) + sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK, + sep = "_") + siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week), + function(x) sum(!is.na(x)) > 0) + sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in% + siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) != + "site_week"] + + # Compute the regional gam index + print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time())) + regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED", + time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR")) + + cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"), + all.x = TRUE, sort = FALSE) + names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled") + + cumu_index <- cumu_index[order(cumu_index$SITE), ] + + # bind if exist else create + if ("cumullated_indices" %in% ls()) { + cumullated_indices <- rbind(cumullated_indices, cumu_index) + } else { + cumullated_indices <- cumu_index + } + +} + +return(cumullated_indices) + +}