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Commit message:
planemo upload for repository https://github.com/galaxyecology/tools-ecology/tree/master/tools/regionalgam commit ffe42225fff8992501b743ebe2c78e50fddc4a4e |
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added:
ab-index.R autocorr-res-acf.R dennis-gam-initial-functions.R flight-curve.R glmmpql.R gls-adjusted.R gls.R plot-trend.R plot-trend.xml regionalgam_macros.xml test-data/gatekeeper_CM_200320042005 test-data/s_data_index.tabular test-data/s_flight_curve_result.tabular test-data/s_glmmpql.tabular test-data/s_gls-adju_rda.rda test-data/s_gls-adju_sum.txt test-data/s_gls_rda.rda test-data/s_gls_sum.txt test-data/s_res.txt test-data/s_trend.tsv |
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| diff -r 000000000000 -r 58ecd15e6836 ab-index.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/ab-index.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,14 @@ +#!/usr/bin/env Rscript + +args = commandArgs(trailingOnly=TRUE) +source(args[1]) + + +tryCatch({input = read.table(args[2], header=TRUE,sep=" ")},finally={input = read.table(args[2], header=TRUE,sep=",")}) +pheno = read.table(args[3], header=TRUE,sep=" ") + +if("TREND" %in% colnames(input)){ + input <- input[input$TREND==1,c("SPECIES","SITE","YEAR","MONTH","DAY","COUNT")] +} +data.index <- abundance_index(input, pheno) +write.table(data.index, file="data.index", row.names=FALSE, sep=" ") |
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| diff -r 000000000000 -r 58ecd15e6836 autocorr-res-acf.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/autocorr-res-acf.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,10 @@ +#!/usr/bin/env Rscript +args = commandArgs(trailingOnly=TRUE) +load(args[1]) + +png('output-acf.png',width = 480, height = 480, units = "px") +graph<-acf(residuals(mod,type="normalized"),plot=FALSE) +plot(graph, main="Acf residuals") +invisible(dev.off()) +sink("output-acf.txt") +print(graph) |
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| diff -r 000000000000 -r 58ecd15e6836 dennis-gam-initial-functions.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/dennis-gam-initial-functions.R Thu Jun 20 04:04:43 2019 -0400 |
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| b'@@ -0,0 +1,648 @@\n+### R-Script Adapted from script provided by the CEH, UK BY: Reto Schmucki [ reto.schmucki@mail.mcgill.ca]\n+### DATE: 14 July 2014 function to run two stage model in DENNIS et al. 2013\n+\n+\n+.onAttach <- function(libname, pkgname) {\n+ packageStartupMessage(" The regionalGAM package that is no longer maintained, \\n use the new rbms package instead. \\n\n+ devtools::install_github(\\"RetoSchmucki/rbms\\", force=TRUE)")\n+}\n+\n+\n+#\' year_day_func Function\n+#\' This function generate the full sequence of days, months and include the observation to that file.\n+#\' @param sp_data A data.frame with your observation.\n+#\' @keywords year days\n+#\' @export\n+#\' @author Reto Schmucki\n+#\' @examples\n+#\' year_day_func()\n+\n+\n+# FUNCTIONS\n+\n+year_day_func = function(sp_data) {\n+\n+ year <- unique(sp_data$YEAR)\n+\n+ origin.d <- paste(year, "01-01", sep = "-")\n+ if ((year%%4 == 0) & ((year%%100 != 0) | (year%%400 == 0))) {\n+ nday <- 366\n+ } else {\n+ nday <- 365\n+ }\n+\n+ date.serie <- as.POSIXlt(seq(as.Date(origin.d), length = nday, by = "day"), format = "%Y-%m-%d")\n+\n+ dayno <- as.numeric(julian(date.serie, origin = as.Date(origin.d)) + 1)\n+ month <- as.numeric(strftime(date.serie, format = "%m"))\n+ week <- as.numeric(strftime(date.serie, format = "%W"))\n+ week_day <- as.numeric(strftime(date.serie, format = "%u"))\n+ day <- as.numeric(strftime(date.serie, format = "%d"))\n+\n+ site_list <- sp_data[!duplicated(sp_data$SITE), c("SITE")]\n+\n+ all_day_site <- data.frame(SPECIES = sp_data$SPECIES[1], SITE = rep(site_list, rep(nday, length(site_list))),\n+ YEAR = sp_data$YEAR[1], MONTH = month, WEEK = week, DAY = day, DAY_WEEK = week_day, DAYNO = dayno,\n+ COUNT = NA)\n+\n+ count_index <- match(paste(sp_data$SITE, sp_data$DAYNO, sep = "_"), paste(all_day_site$SITE, all_day_site$DAYNO,\n+ sep = "_"))\n+ all_day_site$COUNT[count_index] <- sp_data$COUNT\n+ site_count_length <- aggregate(sp_data$COUNT, by = list(sp_data$SITE), function(x) list(1:length(x)))\n+ names(site_count_length$x) <- as.character(site_count_length$Group.1)\n+ site_countno <- utils::stack(site_count_length$x)\n+ all_day_site$COUNTNO <- NA\n+ all_day_site$COUNTNO[count_index] <- site_countno$values # add count number to ease extraction of single count\n+\n+ # Add zero to close observation season two weeks before and after the first and last\n+ first_obs <- min(all_day_site$DAYNO[!is.na(all_day_site$COUNT)])\n+ last_obs <- max(all_day_site$DAYNO[!is.na(all_day_site$COUNT)])\n+\n+ closing_season <- c((first_obs - 11):(first_obs - 7), (last_obs + 7):(last_obs + 11))\n+\n+ # If closing season is before day 1 or day 365, simply set the first and last 5 days to 0\n+ if (min(closing_season) < 1)\n+ closing_season[1:5] <- c(1:5)\n+ if (max(closing_season) > nday)\n+ closing_season[6:10] <- c((nday - 4):nday)\n+\n+ all_day_site$COUNT[all_day_site$DAYNO %in% closing_season] <- 0\n+ all_day_site$ANCHOR <- 0\n+ all_day_site$ANCHOR[all_day_site$DAYNO %in% closing_season] <- 1\n+\n+ all_day_site <- all_day_site[order(all_day_site$SITE, all_day_site$DAYNO), ]\n+\n+ return(all_day_site)\n+}\n+\n+\n+#\' trap_area Function\n+#\'\n+#\' This function compute the area under the curve using the trapezoid method.\n+#\' @param x A vector or a two columns matrix.\n+#\' @param y A vector, Default is NULL\n+#\' @keywords trapezoid\n+#\' @export\n+#\' @examples\n+#\' trap_area()\n+\n+\n+trap_area = function(x, y = NULL) {\n+ # If y is null and x has multiple columns then set y to x[,2] and x to x[,1]\n+ if (is.null(y)) {\n+ if (length(dim(x)) == 2) {\n+ y = x[, 2]\n+ x = x[, 1]\n+ } else {\n+ stop("ERROR: need to either specifiy both x and y or supply a two column data.frame/matrix to x")\n+ }\n+ }\n+\n+ # Check x and y are same length\n+ if (length(x) != length(y)) {\n+ stop("ERROR: x and y need to be the same length")\n+ }\n+\n+ # Need to exclude any'..b'u_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled")\n+\n+ cumu_index <- cumu_index[order(cumu_index$SITE), ]\n+\n+ # bind if exist else create\n+ if ("cumullated_indices" %in% ls()) {\n+ cumullated_indices <- rbind(cumullated_indices, cumu_index)\n+ } else {\n+ cumullated_indices <- cumu_index\n+ }\n+\n+} # end of year loop\n+\n+} else {\n+\n+ y <- unique(dataset$YEAR)\n+ year_pheno <- flight_pheno[flight_pheno$year == y, ]\n+\n+ dataset_y <- dataset[dataset$YEAR == y, ]\n+\n+ sp_data_site <- year_day_func(dataset_y)\n+ sp_data_site$trimDAYNO <- sp_data_site$DAYNO - min(sp_data_site$DAYNO) + 1\n+\n+ sp_data_site <- merge(sp_data_site, year_pheno[, c("DAYNO", "nm")],\n+ by = c("DAYNO"), all.x = TRUE, sort = FALSE)\n+\n+ # compute proportion of the flight curve sampled due to missing visits\n+ pro_missing_count <- data.frame(SITE = sp_data_site$SITE, WEEK = sp_data_site$WEEK,\n+ NM = sp_data_site$nm, COUNT = sp_data_site$COUNT, ANCHOR = sp_data_site$ANCHOR)\n+ pro_missing_count$site_week <- paste(pro_missing_count$SITE, pro_missing_count$WEEK,\n+ sep = "_")\n+ siteweeknocount <- aggregate(pro_missing_count$COUNT, by = list(pro_missing_count$site_week),\n+ function(x) sum(!is.na(x)) == 0)\n+ pro_missing_count <- pro_missing_count[pro_missing_count$site_week %in%\n+ siteweeknocount$Group.1[siteweeknocount$x == TRUE], ]\n+ pro_count_agg <- aggregate(pro_missing_count$NM, by = list(pro_missing_count$SITE),\n+ function(x) 1 - sum(x, na.rm = T))\n+ names(pro_count_agg) <- c("SITE", "PROP_PHENO_SAMPLED")\n+\n+ # remove samples outside the monitoring window\n+ sp_data_site$COUNT[sp_data_site$nm==0] <- NA\n+\n+ # Compute the regional GAM index\n+ if(length(unique(sp_data_site$SITE))>1){\n+ glm_obj_site <- glm(COUNT ~ factor(SITE) + offset(log(nm)) - 1, data = sp_data_site,\n+ family = quasipoisson(link = "log"), control = list(maxit = 100))\n+ } else {\n+ glm_obj_site <- glm(COUNT ~ offset(log(nm)) - 1, data = sp_data_site,\n+ family = quasipoisson(link = "log"), control = list(maxit = 100))\n+ }\n+\n+ sp_data_site[, "FITTED"] <- predict.glm(glm_obj_site, newdata = sp_data_site,\n+ type = "response")\n+ sp_data_site[, "COUNT_IMPUTED"] <- sp_data_site$COUNT\n+ sp_data_site[is.na(sp_data_site$COUNT), "COUNT_IMPUTED"] <- sp_data_site$FITTED[is.na(sp_data_site$COUNT)]\n+\n+ # add fitted value for missing mid-week data\n+ sp_data_site <- sp_data_site[!paste(sp_data_site$DAY_WEEK, sp_data_site$COUNT) %in%\n+ c("1 NA", "2 NA", "3 NA", "5 NA", "6 NA", "7 NA"), ]\n+\n+ # remove all added mid-week values for weeks with real count\n+ # (observation)\n+ sp_data_site$site_week <- paste(sp_data_site$SITE, sp_data_site$WEEK,\n+ sep = "_")\n+ siteweekcount <- aggregate(sp_data_site$COUNT, by = list(sp_data_site$site_week),\n+ function(x) sum(!is.na(x)) > 0)\n+ sp_data_site <- sp_data_site[!(is.na(sp_data_site$COUNT) & (sp_data_site$site_week %in%\n+ siteweekcount$Group.1[siteweekcount$x == TRUE])), names(sp_data_site) !=\n+ "site_week"]\n+\n+ # Compute the regional gam index\n+ print(paste("Compute index for",sp_data_site$SPECIES[1],"at year", y,"for",length(unique(sp_data_site$SITE)),"sites:",Sys.time()))\n+ regional_gam_index <- trap_index(sp_data_site, data_col = "COUNT_IMPUTED",\n+ time_col = "DAYNO", by_col = c("SPECIES", "SITE", "YEAR"))\n+\n+ cumu_index <- merge(regional_gam_index, pro_count_agg, by = c("SITE"),\n+ all.x = TRUE, sort = FALSE)\n+ names(cumu_index) <- c("SITE", "SPECIES", "YEAR", "regional_gam", "prop_pheno_sampled")\n+\n+ cumu_index <- cumu_index[order(cumu_index$SITE), ]\n+\n+ # bind if exist else create\n+ if ("cumullated_indices" %in% ls()) {\n+ cumullated_indices <- rbind(cumullated_indices, cumu_index)\n+ } else {\n+ cumullated_indices <- cumu_index\n+ }\n+\n+}\n+\n+return(cumullated_indices)\n+\n+}\n' |
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| diff -r 000000000000 -r 58ecd15e6836 flight-curve.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/flight-curve.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,8 @@ +#!/usr/bin/env Rscript +args = commandArgs(trailingOnly=TRUE) +source(args[1]) #TODO replace by library(regionalGAM) if available as official package from bioconda +tryCatch({input = read.table(args[2], header=TRUE,sep=" ")},finally={input = read.table(args[2], header=TRUE,sep=",")}) +dataset1 <- input[,c("SPECIES", "SITE", "YEAR", "MONTH", "DAY", "COUNT")] +pheno <- flight_curve(dataset1, MinVisit = args[3], MinOccur = args[4]) + +write.table(pheno, file="pheno", row.names=FALSE, sep=" ") |
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| diff -r 000000000000 -r 58ecd15e6836 glmmpql.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/glmmpql.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,16 @@ +#!/usr/bin/env Rscript +library(nlme) +library(MASS) + +args = commandArgs(trailingOnly=TRUE) +input = read.table(args[1], header=TRUE,sep=" ") +glmm.mod_fullyear <- glmmPQL(regional_gam~ as.factor(YEAR)-1,data=input,family=quasipoisson,random=~1|SITE, correlation = corAR1(form = ~ YEAR | SITE),verbose = FALSE) + +col.index <- as.numeric(glmm.mod_fullyear$coefficients$fixed) +year <- unique(input$YEAR) + +write.table(col.index, file="output-glmmpql", row.names=FALSE, sep=" ") + +png('output-plot.png') +plot(year,col.index,type='o', xlab="year",ylab="collated index") +invisible(dev.off()) |
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| diff -r 000000000000 -r 58ecd15e6836 gls-adjusted.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/gls-adjusted.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,16 @@ +#!/usr/bin/env Rscript +library(nlme) +library(MASS) + +args = commandArgs(trailingOnly=TRUE) +input1 = read.table(args[1], header=TRUE) #input1=col.index =glmmpql-output +input2 = read.table(args[2], header=TRUE,sep=" ") #input2=data.index =abundance_index-output + +input1<-as.matrix(input1) + +year <- unique(input2$YEAR) +mod <- gls(input1~year, correlation = corARMA(p=2)) +summary<-summary(mod) + +save(mod, file = "mod_adjusted.rda") +capture.output(summary, file="mod_adjusted-summary.txt") |
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| diff -r 000000000000 -r 58ecd15e6836 gls.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/gls.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,16 @@ +#!/usr/bin/env Rscript +library(nlme) +library(MASS) + +args = commandArgs(trailingOnly=TRUE) +input1 = read.table(args[1], header=TRUE) #input1=col.index =glmmpql-output +input2 = read.table(args[2], header=TRUE,sep=" ") #input2=data.index =abundance_index-output + +input1<-as.matrix(input1) + +year <- unique(input2$YEAR) +mod <- gls(input1~year) +summary<-summary(mod) + +save(mod, file = "mod.rda") +capture.output(summary, file="mod-summary.txt") |
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| diff -r 000000000000 -r 58ecd15e6836 plot-trend.R --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plot-trend.R Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,23 @@ +#!/usr/bin/env Rscript +library(nlme) +library(MASS) + +args = commandArgs(trailingOnly=TRUE) +input = read.table(args[1], header=TRUE,sep=" ") #input=data.index =ab_index-output +load(args[2]) + +glmm.mod_fullyear <- glmmPQL(regional_gam~ as.factor(YEAR)-1,data=input,family=quasipoisson,random=~1|SITE, correlation = corAR1(form = ~ YEAR | SITE),verbose = FALSE) + +col.index <- as.numeric(glmm.mod_fullyear$coefficients$fixed) +year <- unique(input$YEAR) + +png('output-plot-trend.png') +plot(year,col.index, type='o',xlab="year",ylab="collated index") +abline(mod,lty=2,col="red") +invisible(dev.off()) + +d<-data.frame(year,col.index) +colnames(d)<-c("year","collated index") +write.table(d,"s_trend.tsv",sep=" ",row.names=FALSE) + +cat("Show trend line on abundance plot") |
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| diff -r 000000000000 -r 58ecd15e6836 plot-trend.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plot-trend.xml Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,54 @@ +<tool id="regionalgam_plot_trend" name="Plot abundance" version="@VERSION@"> + <description>with trend line</description> + <macros> + <import>regionalgam_macros.xml</import> + </macros> + <expand macro="rg_nlme_mass_requirements"/> + <command detect_errors="exit_code"><![CDATA[ + Rscript '$__tool_directory__/plot-trend.R' + '$ab_indices' '$gls_model' + '$output' + '$output_tsv' + ]]> + </command> + <inputs> + <expand macro="rg_ab_indices"/> + <expand macro="rg_gls_model"/> + </inputs> + <outputs> + <data format="png" name="output" from_work_dir="output-plot-trend.png" /> + <data format="tabular" name="output_tsv" from_work_dir="s_trend.tsv" /> + </outputs> + <tests> + <test> + <param name="ab_indices" value="s_data_index.tabular"/> + <param name="gls_model" value="s_gls-adju_rda.rda"/> + <output name="output_tsv" ftype="tabular"> + <assert_contents> + <has_line_matching expression="2003	5.5699.*"/> + <has_line_matching expression="2004	5.6221.*"/> + <has_line_matching expression="2005	5.6915.*"/> + <has_line line=""year"	"collated index""/> + <has_n_columns n="2"/> + </assert_contents> + </output> + </test> + </tests> + <help><![CDATA[ +========================== +Plot abundance trend +========================== + +This tool is an implementation of the plot_trend function from RegionalGAM package: https://github.com/RetoSchmucki/regionalGAM/ + +This function plot abundance and add a trend line, it is the year effect estimated in the model. + +| + +**Outputs** + +Trend estimation plotted in an image png file, and raw values in a tabular file. + + ]]></help> + <expand macro="rg_citation"/> +</tool> |
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| diff -r 000000000000 -r 58ecd15e6836 regionalgam_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/regionalgam_macros.xml Thu Jun 20 04:04:43 2019 -0400 |
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| @@ -0,0 +1,31 @@ +<macros> + <token name="@VERSION@">1.5</token> + <xml name="rg_r_requirements"> + <requirements> + <requirement type="package" version="3.5.1">r-base</requirement> + </requirements> + </xml> + <xml name="rg_nlme_mass_requirements"> + <requirements> + <requirement type="package" version="3.1_137">r-nlme</requirement> + <requirement type="package" version="7.3_50">r-mass</requirement> + </requirements> + </xml> + <xml name="rg_count_file"> + <param format="tabular,csv" name="count_file" type="data" label="Count file" help="The file must contain the SPECIES, SITE, YEAR, MONTH, DAY and COUNT columns. Csv or tabular file."/> + </xml> + <xml name="rg_ab_indices"> + <param format="tabular" name="ab_indices" type="data" label="Computed abundance indices" help="Tabular file generated by the Abundance index tool."/> + </xml> + <xml name="rg_collated_index"> + <param name="collated_index" type="data" format="tabular" label="Collated index" help="File generated by the glmmpql/Expected temporal trend tool."/> + </xml> + <xml name="rg_gls_model"> + <param format="rdata" name="gls_model" type="data" label="Linear model" help="Linear model saved in a Rdata file."/> + </xml> + <xml name="rg_citation"> + <citations> + <citation type="doi">10.1111/1365-2664.12561</citation> + </citations> + </xml> +</macros> |
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| diff -r 000000000000 -r 58ecd15e6836 test-data/gatekeeper_CM_200320042005 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/gatekeeper_CM_200320042005 Thu Jun 20 04:04:43 2019 -0400 |
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| b'@@ -0,0 +1,29947 @@\n+"SPECIES","SITE","YEAR","MONTH","DAY","COUNT","TREND"\n+"Pyronia tithonus","ESBMS.12",2003,4,4,0,0\n+"Pyronia tithonus","ESBMS.12",2003,4,7,0,0\n+"Pyronia tithonus","ESBMS.12",2003,4,13,0,0\n+"Pyronia tithonus","ESBMS.12",2003,4,23,0,0\n+"Pyronia tithonus","ESBMS.12",2003,5,2,0,0\n+"Pyronia tithonus","ESBMS.12",2003,5,10,0,0\n+"Pyronia tithonus","ESBMS.12",2003,5,23,0,0\n+"Pyronia tithonus","ESBMS.12",2003,5,29,0,0\n+"Pyronia tithonus","ESBMS.12",2003,6,4,0,0\n+"Pyronia tithonus","ESBMS.12",2003,6,10,0,0\n+"Pyronia tithonus","ESBMS.12",2003,6,19,0,0\n+"Pyronia tithonus","ESBMS.12",2003,6,21,0,0\n+"Pyronia tithonus","ESBMS.12",2003,7,2,9,0\n+"Pyronia tithonus","ESBMS.12",2003,7,6,18,0\n+"Pyronia tithonus","ESBMS.12",2003,7,16,38,0\n+"Pyronia tithonus","ESBMS.12",2003,7,23,39,0\n+"Pyronia tithonus","ESBMS.12",2003,7,27,51,0\n+"Pyronia tithonus","ESBMS.12",2003,8,4,42,0\n+"Pyronia tithonus","ESBMS.12",2003,8,18,11,0\n+"Pyronia tithonus","ESBMS.12",2003,8,25,5,0\n+"Pyronia tithonus","ESBMS.12",2003,9,10,0,0\n+"Pyronia tithonus","ESBMS.12",2003,9,16,0,0\n+"Pyronia tithonus","ESBMS.12",2003,9,26,0,0\n+"Pyronia tithonus","ESBMS.28",2003,4,4,0,0\n+"Pyronia tithonus","ESBMS.28",2003,4,6,0,0\n+"Pyronia tithonus","ESBMS.28",2003,4,13,0,0\n+"Pyronia tithonus","ESBMS.28",2003,4,25,0,0\n+"Pyronia tithonus","ESBMS.28",2003,4,27,0,0\n+"Pyronia tithonus","ESBMS.28",2003,5,3,0,0\n+"Pyronia tithonus","ESBMS.28",2003,5,23,0,0\n+"Pyronia tithonus","ESBMS.28",2003,5,29,0,0\n+"Pyronia tithonus","ESBMS.28",2003,6,4,0,0\n+"Pyronia tithonus","ESBMS.28",2003,6,12,0,0\n+"Pyronia tithonus","ESBMS.28",2003,6,14,0,0\n+"Pyronia tithonus","ESBMS.28",2003,6,21,0,0\n+"Pyronia tithonus","ESBMS.28",2003,7,11,23,0\n+"Pyronia tithonus","ESBMS.28",2003,7,18,166,0\n+"Pyronia tithonus","ESBMS.28",2003,7,27,736,0\n+"Pyronia tithonus","ESBMS.28",2003,8,2,658,0\n+"Pyronia tithonus","ESBMS.28",2003,8,9,406,0\n+"Pyronia tithonus","ESBMS.28",2003,8,20,94,0\n+"Pyronia tithonus","ESBMS.28",2003,8,27,23,0\n+"Pyronia tithonus","ESBMS.28",2003,9,10,3,0\n+"Pyronia tithonus","ESBMS.28",2003,9,19,0,0\n+"Pyronia tithonus","ESBMS.28",2003,9,26,0,0\n+"Pyronia tithonus","ESBMS.55",2003,4,4,0,0\n+"Pyronia tithonus","ESBMS.55",2003,4,10,0,0\n+"Pyronia tithonus","ESBMS.55",2003,4,23,0,0\n+"Pyronia tithonus","ESBMS.55",2003,5,2,0,0\n+"Pyronia tithonus","ESBMS.55",2003,5,9,0,0\n+"Pyronia tithonus","ESBMS.55",2003,5,14,0,0\n+"Pyronia tithonus","ESBMS.55",2003,5,23,0,0\n+"Pyronia tithonus","ESBMS.55",2003,5,30,0,0\n+"Pyronia tithonus","ESBMS.55",2003,6,4,0,0\n+"Pyronia tithonus","ESBMS.55",2003,6,9,0,0\n+"Pyronia tithonus","ESBMS.55",2003,6,19,0,0\n+"Pyronia tithonus","ESBMS.55",2003,6,22,0,0\n+"Pyronia tithonus","ESBMS.55",2003,7,1,0,0\n+"Pyronia tithonus","ESBMS.55",2003,7,7,0,0\n+"Pyronia tithonus","ESBMS.55",2003,7,16,0,0\n+"Pyronia tithonus","ESBMS.55",2003,7,19,1,0\n+"Pyronia tithonus","ESBMS.55",2003,8,1,4,0\n+"Pyronia tithonus","ESBMS.55",2003,8,7,6,0\n+"Pyronia tithonus","ESBMS.55",2003,8,12,10,0\n+"Pyronia tithonus","ESBMS.55",2003,8,18,9,0\n+"Pyronia tithonus","ESBMS.55",2003,8,26,3,0\n+"Pyronia tithonus","ESBMS.55",2003,9,5,0,0\n+"Pyronia tithonus","ESBMS.55",2003,9,10,0,0\n+"Pyronia tithonus","ESBMS.55",2003,9,17,0,0\n+"Pyronia tithonus","ESBMS.55",2003,9,25,1,0\n+"Pyronia tithonus","ESBMS.72",2003,4,6,0,0\n+"Pyronia tithonus","ESBMS.72",2003,4,13,0,0\n+"Pyronia tithonus","ESBMS.72",2003,4,19,0,0\n+"Pyronia tithonus","ESBMS.72",2003,4,27,0,0\n+"Pyronia tithonus","ESBMS.72",2003,5,3,0,0\n+"Pyronia tithonus","ESBMS.72",2003,5,15,0,0\n+"Pyronia tithonus","ESBMS.72",2003,5,18,0,0\n+"Pyronia tithonus","ESBMS.72",2003,5,28,0,0\n+"Pyronia tithonus","ESBMS.72",2003,6,1,0,0\n+"Pyronia tithonus","ESBMS.72",2003,6,11,0,0\n+"Pyronia tithonus","ESBMS.72",2003,6,16,0,0\n+"Pyronia tithonus","ESBMS.72",2003,6,23,0,0\n+"Pyronia tithonus","ESBMS.72",2003,7,1,0,0\n+"Pyronia tithonus","ESBMS.72",2003,7,7,4,0\n+"Pyronia tithonus","ESBMS.72",2003,7,13,11,0\n+"Pyronia tithonus","ESBMS.72",2003,7,20,45,0\n+"Pyronia tithonus","ESBMS.72",2003,7,30,40,0\n+"Pyronia tithon'..b'5,8,21,6,0\n+"Pyronia tithonus","UKBMS.2403",2005,8,28,0,0\n+"Pyronia tithonus","UKBMS.2403",2005,9,5,0,0\n+"Pyronia tithonus","UKBMS.2403",2005,9,13,0,0\n+"Pyronia tithonus","UKBMS.2403",2005,9,17,0,0\n+"Pyronia tithonus","UKBMS.2403",2005,9,24,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,4,11,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,4,26,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,5,5,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,5,15,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,5,23,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,5,29,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,6,7,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,6,18,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,6,27,8,0\n+"Pyronia tithonus","UKBMS.2404",2005,7,4,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,7,13,64,0\n+"Pyronia tithonus","UKBMS.2404",2005,7,21,115,0\n+"Pyronia tithonus","UKBMS.2404",2005,8,4,80,0\n+"Pyronia tithonus","UKBMS.2404",2005,8,12,43,0\n+"Pyronia tithonus","UKBMS.2404",2005,8,23,3,0\n+"Pyronia tithonus","UKBMS.2404",2005,8,30,1,0\n+"Pyronia tithonus","UKBMS.2404",2005,9,7,0,0\n+"Pyronia tithonus","UKBMS.2404",2005,9,9,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,4,1,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,4,8,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,4,15,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,4,22,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,4,29,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,5,6,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,5,13,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,5,20,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,5,27,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,6,3,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,6,10,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,6,17,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,6,24,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,7,1,1,0\n+"Pyronia tithonus","UKBMS.2405",2005,7,8,8,0\n+"Pyronia tithonus","UKBMS.2405",2005,7,15,44,0\n+"Pyronia tithonus","UKBMS.2405",2005,7,22,103,0\n+"Pyronia tithonus","UKBMS.2405",2005,7,29,146,0\n+"Pyronia tithonus","UKBMS.2405",2005,8,5,98,0\n+"Pyronia tithonus","UKBMS.2405",2005,8,12,34,0\n+"Pyronia tithonus","UKBMS.2405",2005,8,19,8,0\n+"Pyronia tithonus","UKBMS.2405",2005,8,26,2,0\n+"Pyronia tithonus","UKBMS.2405",2005,9,2,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,9,9,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,9,16,0,0\n+"Pyronia tithonus","UKBMS.2405",2005,9,23,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,4,1,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,4,11,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,4,18,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,4,22,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,4,29,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,5,11,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,5,17,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,5,26,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,5,27,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,6,7,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,6,14,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,6,21,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,6,27,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,7,4,13,0\n+"Pyronia tithonus","UKBMS.2407",2005,7,12,17,0\n+"Pyronia tithonus","UKBMS.2407",2005,7,18,15,0\n+"Pyronia tithonus","UKBMS.2407",2005,7,22,22,0\n+"Pyronia tithonus","UKBMS.2407",2005,8,2,16,0\n+"Pyronia tithonus","UKBMS.2407",2005,8,10,2,0\n+"Pyronia tithonus","UKBMS.2407",2005,8,16,2,0\n+"Pyronia tithonus","UKBMS.2407",2005,8,24,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,8,31,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,9,7,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,9,14,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,9,20,0,0\n+"Pyronia tithonus","UKBMS.2407",2005,9,28,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,5,3,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,5,6,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,5,24,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,6,8,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,6,10,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,6,19,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,7,10,19,0\n+"Pyronia tithonus","UKBMS.2408",2005,8,9,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,8,18,0,0\n+"Pyronia tithonus","UKBMS.2408",2005,9,20,0,0\n' |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_data_index.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_data_index.tabular Thu Jun 20 04:04:43 2019 -0400 |
| b |
| @@ -0,0 +1,97 @@ +"SITE" "SPECIES" "YEAR" "regional_gam" "prop_pheno_sampled" +"NLBMS.62" "Pyronia tithonus" 2003 1041.6 0.999996693504386 +"UKBMS.1016" "Pyronia tithonus" 2003 499 0.999977681782156 +"UKBMS.1017" "Pyronia tithonus" 2003 241.3 0.736095299768367 +"UKBMS.106" "Pyronia tithonus" 2003 1434.1 0.971211148999253 +"UKBMS.117" "Pyronia tithonus" 2003 108.4 0.948901308422968 +"UKBMS.1309" "Pyronia tithonus" 2003 133.4 0.779391691116407 +"UKBMS.133" "Pyronia tithonus" 2003 346.7 0.999981244602577 +"UKBMS.1404" "Pyronia tithonus" 2003 76.1 0.876998793245793 +"UKBMS.1407" "Pyronia tithonus" 2003 487.3 0.967385690145934 +"UKBMS.1411" "Pyronia tithonus" 2003 164 0.999981245217145 +"UKBMS.1412" "Pyronia tithonus" 2003 125.6 0.999981245217145 +"UKBMS.1427" "Pyronia tithonus" 2003 438.7 0.999982286309376 +"UKBMS.16" "Pyronia tithonus" 2003 234.4 0.999982285694808 +"UKBMS.1614" "Pyronia tithonus" 2003 27.1 0.999982286309376 +"UKBMS.1717" "Pyronia tithonus" 2003 407.9 0.999977734484143 +"UKBMS.1720" "Pyronia tithonus" 2003 184.6 0.774898141980137 +"UKBMS.176" "Pyronia tithonus" 2003 2051.5 0.971577195955518 +"UKBMS.2010" "Pyronia tithonus" 2003 136 0.999977734959092 +"UKBMS.2015" "Pyronia tithonus" 2003 103 0.507450558655163 +"UKBMS.2024" "Pyronia tithonus" 2003 431.6 0.971577195815899 +"UKBMS.2027" "Pyronia tithonus" 2003 566.5 0.998900223688378 +"UKBMS.2045" "Pyronia tithonus" 2003 74.4 0.999896655054516 +"UKBMS.2055" "Pyronia tithonus" 2003 509 0.998885816968317 +"UKBMS.2212" "Pyronia tithonus" 2003 974.6 0.779307336663016 +"UKBMS.2234" "Pyronia tithonus" 2003 101.6 0.999982285694808 +"UKBMS.2238" "Pyronia tithonus" 2003 53.6 0.984688802985178 +"UKBMS.2254" "Pyronia tithonus" 2003 69.6 0.985086486666898 +"UKBMS.2258" "Pyronia tithonus" 2003 307.1 0.999982285555189 +"UKBMS.2266" "Pyronia tithonus" 2003 268.9 0.999977734484143 +"UKBMS.2328" "Pyronia tithonus" 2003 123.9 0.999977735098711 +"NLBMS.62" "Pyronia tithonus" 2004 521.4 0.999971669491225 +"UKBMS.1016" "Pyronia tithonus" 2004 323.6 0.825617206195019 +"UKBMS.1017" "Pyronia tithonus" 2004 310.1 0.81576888539393 +"UKBMS.1040" "Pyronia tithonus" 2004 315.4 0.672491753541291 +"UKBMS.106" "Pyronia tithonus" 2004 1599.9 0.998668830862834 +"UKBMS.117" "Pyronia tithonus" 2004 116.2 0.999302016822444 +"UKBMS.1309" "Pyronia tithonus" 2004 135.9 0.81569135548207 +"UKBMS.133" "Pyronia tithonus" 2004 198.1 0.998542487916362 +"UKBMS.1404" "Pyronia tithonus" 2004 360.4 0.852207021142741 +"UKBMS.1407" "Pyronia tithonus" 2004 247 0.634158394415404 +"UKBMS.1411" "Pyronia tithonus" 2004 169.2 0.986851973794985 +"UKBMS.1412" "Pyronia tithonus" 2004 123.7 0.986800724344064 +"UKBMS.1427" "Pyronia tithonus" 2004 818.1 0.999994600355568 +"UKBMS.16" "Pyronia tithonus" 2004 269.5 0.998696961525244 +"UKBMS.1614" "Pyronia tithonus" 2004 61.4 0.850282194685412 +"UKBMS.1717" "Pyronia tithonus" 2004 644.7 0.999722729847095 +"UKBMS.1720" "Pyronia tithonus" 2004 228.7 0.986156648038205 +"UKBMS.176" "Pyronia tithonus" 2004 2752.1 0.99862271577287 +"UKBMS.2010" "Pyronia tithonus" 2004 114.3 0.987196805717397 +"UKBMS.2015" "Pyronia tithonus" 2004 114.9 0.999992748276108 +"UKBMS.2024" "Pyronia tithonus" 2004 307.6 0.999732522153148 +"UKBMS.2027" "Pyronia tithonus" 2004 635.1 0.969104151858962 +"UKBMS.2045" "Pyronia tithonus" 2004 116.1 0.926635738812239 +"UKBMS.2052" "Pyronia tithonus" 2004 135.7 0.83761870748898 +"UKBMS.2055" "Pyronia tithonus" 2004 173.8 0.95641733695997 +"UKBMS.2212" "Pyronia tithonus" 2004 911.8 0.856245926478372 +"UKBMS.2234" "Pyronia tithonus" 2004 64.5 0.854468034950933 +"UKBMS.2238" "Pyronia tithonus" 2004 76.8 0.998550430379774 +"UKBMS.2254" "Pyronia tithonus" 2004 139.7 0.843291130058462 +"UKBMS.2258" "Pyronia tithonus" 2004 362.1 0.998622781213415 +"UKBMS.2266" "Pyronia tithonus" 2004 251.7 0.877020347115025 +"UKBMS.2310" "Pyronia tithonus" 2004 271.7 0.981882284570638 +"UKBMS.2325" "Pyronia tithonus" 2004 253.6 0.993753793846067 +"UKBMS.2328" "Pyronia tithonus" 2004 143.7 0.986827183102062 +"NLBMS.62" "Pyronia tithonus" 2005 765.7 0.999999993055589 +"UKBMS.1016" "Pyronia tithonus" 2005 344.5 0.667090943735622 +"UKBMS.1017" "Pyronia tithonus" 2005 236.9 0.752776928789364 +"UKBMS.1040" "Pyronia tithonus" 2005 306.5 0.792952096805565 +"UKBMS.106" "Pyronia tithonus" 2005 2462.4 0.775573411762395 +"UKBMS.117" "Pyronia tithonus" 2005 165.8 0.724477514496017 +"UKBMS.1309" "Pyronia tithonus" 2005 83.2 0.945399265672354 +"UKBMS.133" "Pyronia tithonus" 2005 446.6 0.764264935309839 +"UKBMS.1404" "Pyronia tithonus" 2005 302.6 0.999996278634474 +"UKBMS.1407" "Pyronia tithonus" 2005 315.8 0.670162984378064 +"UKBMS.1411" "Pyronia tithonus" 2005 274.5 0.669390989287328 +"UKBMS.1412" "Pyronia tithonus" 2005 240.7 0.755718466847496 +"UKBMS.1427" "Pyronia tithonus" 2005 1130.2 0.999996278634474 +"UKBMS.16" "Pyronia tithonus" 2005 420.3 0.986970508915803 +"UKBMS.1614" "Pyronia tithonus" 2005 26.9 0.589430498820143 +"UKBMS.1717" "Pyronia tithonus" 2005 602.4 0.681113438385617 +"UKBMS.1720" "Pyronia tithonus" 2005 205.7 0.769607730246065 +"UKBMS.176" "Pyronia tithonus" 2005 1589.5 0.564092310036324 +"UKBMS.2010" "Pyronia tithonus" 2005 179.1 0.775549875051183 +"UKBMS.2015" "Pyronia tithonus" 2005 161.8 0.775551920647616 +"UKBMS.2027" "Pyronia tithonus" 2005 609.1 0.948678549946944 +"UKBMS.2045" "Pyronia tithonus" 2005 257.3 0.787958799875157 +"UKBMS.2052" "Pyronia tithonus" 2005 101.9 0.794475020313406 +"UKBMS.2055" "Pyronia tithonus" 2005 369.5 0.948484348317652 +"UKBMS.2212" "Pyronia tithonus" 2005 730.7 0.653520661000762 +"UKBMS.2234" "Pyronia tithonus" 2005 45.2 0.857359266293646 +"UKBMS.2238" "Pyronia tithonus" 2005 118.8 0.7640469757367 +"UKBMS.2254" "Pyronia tithonus" 2005 107.7 0.775985716635106 +"UKBMS.2258" "Pyronia tithonus" 2005 380.5 0.886660787898406 +"UKBMS.2310" "Pyronia tithonus" 2005 201.1 0.591169635610145 +"UKBMS.2325" "Pyronia tithonus" 2005 219 0.866034266422452 +"UKBMS.2328" "Pyronia tithonus" 2005 154.4 0.629823626313576 |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_flight_curve_result.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_flight_curve_result.tabular Thu Jun 20 04:04:43 2019 -0400 |
| b |
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tithonus"\t2005\t51\t357\t357\t0\n+"Pyronia tithonus"\t2005\t51\t358\t358\t0\n+"Pyronia tithonus"\t2005\t51\t359\t359\t0\n+"Pyronia tithonus"\t2005\t52\t360\t360\t0\n+"Pyronia tithonus"\t2005\t52\t361\t361\t0\n+"Pyronia tithonus"\t2005\t52\t362\t362\t0\n+"Pyronia tithonus"\t2005\t52\t363\t363\t0\n+"Pyronia tithonus"\t2005\t52\t364\t364\t0\n+"Pyronia tithonus"\t2005\t52\t365\t365\t0\n' |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_glmmpql.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_glmmpql.tabular Thu Jun 20 04:04:43 2019 -0400 |
| b |
| @@ -0,0 +1,4 @@ +"x" +5.56997612228159 +5.62214955758101 +5.69157371643271 |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_gls-adju_rda.rda |
| b |
| Binary file test-data/s_gls-adju_rda.rda has changed |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_gls-adju_sum.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_gls-adju_sum.txt Thu Jun 20 04:04:43 2019 -0400 |
| [ |
| @@ -0,0 +1,33 @@ +Generalized least squares fit by REML + Model: input1 ~ year + Data: NULL + AIC BIC logLik + 4.718075 -5.281925 2.640963 + +Correlation Structure: ARMA(2,0) + Formula: ~1 + Parameter estimate(s): + Phi1 Phi2 +-1.026385e-06 1.342554e-06 + +Coefficients: + Value Std.Error t-value p-value +(Intercept) -116.2129 9.979614 -11.64503 0.0545 +year 0.0608 0.004980 12.20897 0.0520 + + Correlation: + (Intr) +year -1 + +Standardized residuals: + x +[1,] 0.4082493 +[2,] -0.8164967 +[3,] 0.4082493 +attr(,"std") +[1] 0.007042572 0.007042572 0.007042572 +attr(,"label") +[1] "Standardized residuals" + +Residual standard error: 0.007042572 +Degrees of freedom: 3 total; 1 residual |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_gls_rda.rda |
| b |
| Binary file test-data/s_gls_rda.rda has changed |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_gls_sum.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_gls_sum.txt Thu Jun 20 04:04:43 2019 -0400 |
| [ |
| @@ -0,0 +1,27 @@ +Generalized least squares fit by REML + Model: input1 ~ year + Data: NULL + AIC BIC logLik + 0.7180747 -5.281925 2.640963 + +Coefficients: + Value Std.Error t-value p-value +(Intercept) -116.2129 9.97963 -11.64501 0.0545 +year 0.0608 0.00498 12.20895 0.0520 + + Correlation: + (Intr) +year -1 + +Standardized residuals: + x +[1,] 0.4082483 +[2,] -0.8164966 +[3,] 0.4082483 +attr(,"std") +[1] 0.007042578 0.007042578 0.007042578 +attr(,"label") +[1] "Standardized residuals" + +Residual standard error: 0.007042578 +Degrees of freedom: 3 total; 1 residual |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_res.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_res.txt Thu Jun 20 04:04:43 2019 -0400 |
| b |
| @@ -0,0 +1,5 @@ + +Autocorrelations of series ‘residuals(mod, type = "normalized")’, by lag + + 0 1 2 + 1.000 -0.667 0.167 |
| b |
| diff -r 000000000000 -r 58ecd15e6836 test-data/s_trend.tsv --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/s_trend.tsv Thu Jun 20 04:04:43 2019 -0400 |
| b |
| @@ -0,0 +1,4 @@ +"year" "collated index" +2003 5.56997612228159 +2004 5.62214955758101 +2005 5.69157371643271 |