Mercurial > repos > ecology > pampa_plotglm
annotate FunctExePlotGLMGalaxy.r @ 2:c448e026f122 draft default tip
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 65ab5b6fe84871db0fe18244d805cea19a44e830"
author | ecology |
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date | Sat, 26 Jun 2021 07:20:47 +0000 |
parents | 3ab852a7ff53 |
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rev | line source |
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3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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1 #Rscript |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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2 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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3 ##################################################################################################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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4 ##################################################################################################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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5 ###################################### Create a plot from your community data ####################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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6 ##################################################################################################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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7 ##################################################################################################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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8 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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9 ###################### Packages |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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10 suppressMessages(library(ggplot2)) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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11 suppressMessages(library(boot)) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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12 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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13 ###################### Load arguments and declaring variables |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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14 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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15 args <- commandArgs(trailingOnly = TRUE) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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16 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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17 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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18 if (length(args) < 2) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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19 stop("At least 3 arguments must be supplied input dataset file with GLM results (.tabular)", call. = FALSE) #if no args -> error and exit1 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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20 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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21 } else { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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22 import_data <- args[1] ###### file name : glm results table |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
ecology
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23 data_tab <- args[2] ###### file name : Metrics table |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
ecology
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24 unitobs_tab <- args[3] ###### file name : Unitobs table |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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25 source(args[4]) ###### Import functions |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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26 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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27 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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28 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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29 #Import data |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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30 glmtable <- read.table(import_data, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8") # |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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31 datatable <- read.table(data_tab, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8") # |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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32 unitobs <- read.table(unitobs_tab, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8") # |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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33 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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34 #Check files |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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35 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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36 vars_data1 <- c("analysis", "Interest.var", "distribution") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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37 err_msg_data1 <- "The input GLM results dataset doesn't have the right format. It needs to have at least the following 3 fields :\n- analysis\n- Interest.var\n- distribution\n" |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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38 check_file(glmtable, err_msg_data1, vars_data1, 4) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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39 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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40 if (length(grep("[0-2][0|9][0-9][0-9].[Estimate|Pvalue]", colnames(glmtable))) == 0) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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41 stop("The input GLM results dataset doesn't have the right format or informations. This tool is to represent temporal trends, if your GLM doesn't take the year variable as a fixed effect this tool is not proper to make any representation of it. It needs to have at least estimates and p-value for every year from your time series GLM as columns with name formated as : yyyy Estimate (example : 2020 Estimate) and yyyy Pvalue (example : 2020 Pvalue).") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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42 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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43 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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44 if (length(grep("[0-2][0|9][0-9][0-9].IC_[up|inf]", colnames(glmtable))) == 0) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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45 assess_ic <- FALSE |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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46 }else{ |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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47 assess_ic <- TRUE |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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48 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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49 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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50 metric <- as.character(glmtable[1, "Interest.var"]) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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51 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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52 vars_data2 <- c("observation.unit", "location", metric) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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53 err_msg_data2 <- "The input metrics dataset doesn't have the right format. It needs to have at least the following 3 fields :\n- observation.unit\n- location\n- the name of the interest metric\n" |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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54 check_file(datatable, err_msg_data2, vars_data2, 4) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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55 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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56 vars_data3 <- c("observation.unit", "year") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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57 err_msg_data3 <- "The input unitobs dataset doesn't have the right format. It needs to have at least the following 2 fields :\n- observation.unit\n- year\n" |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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58 check_file(unitobs, err_msg_data3, vars_data3, 2) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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59 if (length(grep("[0-2][0|9][0-9][0-9]", unitobs$year)) == 0) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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60 stop("The year column in the input unitobs dataset doesn't have the right format. Years must be fully written as : yyyy (example : 2020).") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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61 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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62 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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63 if (all(is.na(match(datatable[, "observation.unit"], unitobs[, "observation.unit"])))) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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64 stop("Observation units doesn't match in the inputs metrics dataset and unitobs dataset") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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65 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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66 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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67 #################################################################################################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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68 ######################### Creating plot from time series GLM data ## Function : ggplot_glm ######################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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69 #################################################################################################################### |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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70 ggplot_glm <- function(glmtable, datatable, unitobs, metric = metric, sp, description = TRUE, |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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71 trend_on_graph = TRUE, assess_ic = TRUE) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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72 ## Purpose: Creating plot from time series GLM data |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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73 ## ---------------------------------------------------------------------- |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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74 ## Arguments: glmtable : GLM(s) results table |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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75 ## datatable : Metrics table |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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76 ## unitobs : Unitobs table |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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77 ## metric : Interest variable in GLM(s) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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78 ## sp : name of processed GLM |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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79 ## description : Two graphs ? |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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80 ## trend_on_graph : Write global trend of the time series on graph ? |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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81 ## assess_ic : Assess confidence intervals ? |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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82 ## ---------------------------------------------------------------------- |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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83 ## Author: Coline ROYAUX 13 october 2020 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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84 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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85 s_signif <- 0.05 ## threshold when pvalue is considered significant |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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86 distrib <- as.character(glmtable[1, "distribution"]) ## extract GLM distribution |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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87 |
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88 col <- c("observation.unit", "location", metric) ## names of needed columns in metrics table to construct the 2nd panel of the graph |
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89 |
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90 if (colnames(glmtable)[length(glmtable)] == "separation") { ## if GLM is a community analysis |
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91 cut <- as.character(glmtable[1, "separation"]) |
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92 if (cut != "None") { ## if there is plural GLM analysis performed |
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93 if (! is.element(as.character(cut), colnames(unitobs))) { |
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94 stop("The input unitobs dataset doesn't have the right format. If plural GLM analysis have been performed it needs to have the field used as separation factor.") |
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95 } |
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96 datatable <- cbind(datatable[, col], unitobs[match(datatable[, "observation.unit"], unitobs[, "observation.unit"]), c("year", cut)]) ## extracting 'year' and analysis separation factor columns from unitobs table to merge with metrics table /// Matching lines with 'observation.unit' column |
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97 colnames(datatable) <- c(col, "year", cut) |
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98 }else{ |
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99 datatable <- cbind(datatable[, col], unitobs[match(datatable[, "observation.unit"], unitobs[, "observation.unit"]), "year"]) ## extracting 'year' column from unitobs table to merge with metrics table /// Matching lines with 'observation.unit' column |
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100 colnames(datatable) <- c(col, "year") |
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101 } |
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102 |
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103 }else{ ## GLM is a population analysis |
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104 cut <- "species.code" |
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105 if (! is.element("species.code", colnames(datatable))) { |
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106 stop("The input metrics dataset or the GLM results dataset doesn't have the right format. If the GLM is a population analysis, the field species.code needs to be informed in the metrics dataset. If the GLM is a community analysis the field separation (None if only one analysis and name of the separation factor if several analysis) needs to be informed in the last column of the GLM results dataset.") |
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107 } |
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108 col <- c(col, cut) |
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109 datatable <- cbind(datatable[, col], unitobs[match(datatable[, "observation.unit"], unitobs[, "observation.unit"]), "year"]) ## extracting 'year' column from unitobs table to merge with metrics table /// Matching lines with 'observation.unit' column |
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110 colnames(datatable) <- c(col, "year") |
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111 } |
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112 |
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113 ##vpan vector of names of the two panels in the ggplot |
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114 |
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115 switch(as.character(metric), |
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116 "number" = vpan <- c("Abundance variation", "Raw abundance"), |
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117 "presence_absence" = vpan <- c("Presence-absence variation", "% presence in location"), |
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118 vpan <- c(paste(metric, " variation"), paste("Mean ", metric))) |
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119 |
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120 ##Cut table for 1 analysis |
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121 glmtab <- glmtable[glmtable[, "analysis"] == sp, ] |
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122 |
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123 glmtab <- glmtab[, grep("FALSE", is.na(glmtab[1, ]))] ## Supress columns with NA only |
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124 |
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125 ## specification of temporal variable necessary for the analyses |
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126 an <- as.numeric(unlist(strsplit(gsub("X", "", paste(colnames(glmtab)[grep("[0-2][0|9][0-9][0-9].Estimate", colnames(glmtab))], collapse = " ")), split = ".Estimate"))) ## Extract list of years studied in the GLM |
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127 |
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128 year <- sort(c(min(an) - 1, an)) ## Add the first level, "reference" in the GLM |
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129 nbans <- length(year) |
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130 pasdetemps <- nbans - 1 |
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131 |
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132 ##### Table 1 |
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133 |
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134 coefan <- unlist(lapply(an, FUN = function(x) { |
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135 if (length(glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".Estimate"), colnames(glmtab))]) > 0) { |
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136 glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".Estimate"), colnames(glmtab))] |
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137 }else{ |
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138 NA |
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139 } |
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140 })) ## extract estimates for each years to contruct graph 1 |
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141 |
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142 ic_inf <- unlist(lapply(an, FUN = function(x) { |
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143 if (length(glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".IC_inf"), colnames(glmtab))]) > 0) { |
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144 glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".IC_inf"), colnames(glmtab))] |
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145 }else{ |
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146 NA |
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147 } |
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148 })) |
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149 |
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150 ic_up <- unlist(lapply(an, FUN = function(x) { |
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151 if (length(glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".IC_up"), colnames(glmtab))]) > 0) { |
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152 glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".IC_up"), colnames(glmtab))] |
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153 }else{ |
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154 NA |
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155 } |
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156 })) |
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157 |
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158 switch(distrib, ## Applying the reciprocal of the link function to coefficients and confidence intervals depending on distribution law |
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159 "poisson" = { |
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160 coefyear <- c(1, exp(as.numeric(coefan))) ## link function : log |
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161 if (assess_ic) { |
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162 ic_inf_sim <- c(1, exp(as.numeric(ic_inf))) |
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163 ic_sup_sim <- c(1, exp(as.numeric(ic_up))) |
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164 } else { |
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165 ic_inf_sim <- NA |
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166 ic_sup_sim <- NA |
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167 }}, |
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168 "quasipoisson" = { |
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169 coefyear <- c(1, exp(as.numeric(coefan))) ## link function : log |
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170 if (assess_ic) { |
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171 ic_inf_sim <- c(1, exp(as.numeric(ic_inf))) |
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172 ic_sup_sim <- c(1, exp(as.numeric(ic_up))) |
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173 } else { |
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174 ic_inf_sim <- NA |
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175 ic_sup_sim <- NA |
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176 }}, |
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177 "inverse.gaussian" = { |
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178 coefyear <- c(0, as.numeric(coefan) ^ (- 1 / 2)) ## link function : x^ - 2 |
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179 if (assess_ic) { |
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180 ic_inf_sim <- c(0, as.numeric(ic_inf) ^ (- 1 / 2)) |
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181 ic_sup_sim <- c(0, as.numeric(ic_up) ^ (- 1 / 2)) |
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182 } else { |
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183 ic_inf_sim <- NA |
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184 ic_sup_sim <- NA |
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185 }}, |
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186 "binomial" = { |
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187 coefyear <- c(1, boot::inv.logit(as.numeric(coefan))) ## link function : logit |
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188 if (assess_ic) { |
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189 ic_inf_sim <- c(1, boot::inv.logit(as.numeric(ic_inf))) |
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190 ic_sup_sim <- c(1, boot::inv.logit(as.numeric(ic_up))) |
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191 } else { |
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192 ic_inf_sim <- NA |
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193 ic_sup_sim <- NA |
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194 }}, |
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195 "quasibinomial" = { |
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196 coefyear <- c(1, boot::inv.logit(as.numeric(coefan))) ## link function : logit |
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197 if (assess_ic) { |
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198 ic_inf_sim <- c(1, boot::inv.logit(as.numeric(ic_inf))) |
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199 ic_sup_sim <- c(1, boot::inv.logit(as.numeric(ic_up))) |
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200 } else { |
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201 ic_inf_sim <- NA |
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202 ic_sup_sim <- NA |
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203 }}, |
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204 "Gamma" = { |
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205 coefyear <- c(1, as.numeric(coefan) ^ (- 1)) ## link function : -x^ - 1 |
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206 if (assess_ic) { |
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207 ic_inf_sim <- c(1, as.numeric(ic_inf) ^ (- 1)) |
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208 ic_sup_sim <- c(1, as.numeric(ic_up) ^ (- 1)) |
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209 } else { |
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210 ic_inf_sim <- NA |
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211 ic_sup_sim <- NA |
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212 }} |
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213 , { |
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214 coefyear <- c(0, as.numeric(coefan)) |
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215 if (assess_ic) { |
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216 ic_inf_sim <- c(0, as.numeric(ic_inf)) |
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217 ic_sup_sim <- c(0, as.numeric(ic_up)) |
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218 } else { |
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219 ic_inf_sim <- NA |
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220 ic_sup_sim <- NA |
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221 }}) |
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222 |
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223 pval <- c(1, unlist(lapply(an, FUN = function(x) { |
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224 if (length(glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".Pvalue"), colnames(glmtab))]) > 0) { |
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225 glmtab[glmtab[, 1] == sp, grep(paste0("X", x, ".Pvalue"), colnames(glmtab))] |
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226 }else{ |
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227 NA |
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228 } |
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229 }))) ## extract p value for each year |
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230 |
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231 |
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232 tab1 <- data.frame(year, val = coefyear, ## table for the graphical output 1 |
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233 ll = unlist(ic_inf_sim), ul = unlist(ic_sup_sim), |
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234 catPoint = ifelse(pval < s_signif, "significatif", NA), pval, |
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235 courbe = vpan[1], |
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236 panel = vpan[1]) |
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237 ## cleaning of wrong or biaised measures of the confidence interval |
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238 if (assess_ic) { |
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239 tab1$ul <- ifelse(tab1$ul == Inf, NA, tab1$ul) |
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240 tab1$ul <- ifelse(tab1$ul > 1.000000e+20, NA, tab1$ul) |
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241 tab1$val <- ifelse(tab1$val > 1.000000e+20, 1.000000e+20, tab1$val) |
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242 } |
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243 |
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244 coefancontinu <- as.numeric(as.character(glmtab[glmtab[, 1] == sp, grep("year.Estimate", colnames(glmtab))])) ## tendency of population evolution on the studied period = regression coefficient of the variable year as a continuous variable in the GLM |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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245 switch(distrib, ## Applying the reciprocal of the link function to coefficients and confidence intervals depending on distribution law |
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246 "poisson" = { |
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247 trend <- round(exp(as.numeric(coefancontinu)), 3) ## link function : log |
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248 pourcentage <- round((exp(as.numeric(coefancontinu) * as.numeric(pasdetemps)) - 1) * 100, 2) |
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249 }, |
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250 "quasipoisson" = { |
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251 trend <- round(exp(as.numeric(coefancontinu)), 3) ## link function : log |
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252 pourcentage <- round((exp(as.numeric(coefancontinu) * as.numeric(pasdetemps)) - 1) * 100, 2) |
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253 }, |
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254 "inverse.gaussian" = { |
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255 trend <- round(as.numeric(coefancontinu) ^ (- 1 / 2), 3) ## link function : x^ - 2 |
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256 pourcentage <- round((((as.numeric(coefancontinu) * as.numeric(pasdetemps)) ^ (- 1 / 2)) - 1) * 100, 2) |
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257 }, |
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258 "binomial" = { |
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259 trend <- round(boot::inv.logit(as.numeric(coefancontinu)), 3) ## link function : logit |
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260 pourcentage <- round((boot::inv.logit(as.numeric(coefancontinu) * as.numeric(pasdetemps)) - 1) * 100, 2) |
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261 }, |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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262 "quasibinomial" = { |
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263 trend <- round(boot::inv.logit(as.numeric(coefancontinu)), 3) ## link function : logit |
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264 pourcentage <- round((boot::inv.logit(as.numeric(coefancontinu) * as.numeric(pasdetemps)) - 1) * 100, 2) |
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265 }, |
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266 "Gamma" = { |
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267 trend <- round(as.numeric(coefancontinu) ^ (- 1), 3) ## link function : -x^ - 1 |
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268 pourcentage <- round((((as.numeric(coefancontinu) * as.numeric(pasdetemps)) ^ (- 1)) - 1) * 100, 2) |
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269 } |
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270 , { |
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271 trend <- round(as.numeric(coefancontinu), 3) |
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272 pourcentage <- round((((as.numeric(coefancontinu) * as.numeric(pasdetemps))) - 1) * 100, 2) |
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273 }) |
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274 |
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275 pval <- as.numeric(as.character(glmtab[glmtab[, 1] == sp, grep("year.Pvalue", colnames(glmtab))])) ## Extract p value |
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276 |
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277 ## table for global trend on the whole time series |
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278 tab1t <- NULL |
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279 if (length(pval) > 0) { |
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280 tab1t <- data.frame(Est = trend, pourcent = pourcentage, signif = pval < s_signif, pval) |
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281 } |
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282 |
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283 ##### Table 2 |
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284 |
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285 if (sp == "global") { ## prepare metrics table to extract variables used in graph 2 |
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286 datatablecut <- datatable[grep("FALSE", is.na(datatable[, as.character(metric)])), ] |
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287 |
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288 }else{ |
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289 |
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290 datatablecut <- datatable[datatable[, as.character(cut)] == sp, ] |
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291 datatablecut <- datatablecut[grep("FALSE", is.na(datatablecut[, as.character(metric)])), ] |
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292 } |
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293 |
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294 switch(as.character(metric), ## Different value represented graph 2 depending on the nature of the metric |
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295 "number" = { |
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296 valplot <- lapply(sort(year), FUN = function(x) { |
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297 sum(na.omit(as.numeric(subset(datatablecut, year == x)[, as.character(metric)]))) |
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298 }) |
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299 }, ## sum if abundance |
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300 "presence_absence" = { |
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301 nb_loc <- lapply(sort(year), FUN = function(x) { |
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302 length(unique(subset(datatablecut, year == x)[, "location"])) |
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303 }) ## number of plots per year |
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304 nb_loc_presence <- lapply(sort(year), FUN = function(x) { |
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305 length(unique(subset(datatablecut[datatablecut[, metric] > 0, ], year == x)[, "location"])) |
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306 }) ## number of plots where the species were observed |
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307 valplot <- (na.omit(as.numeric(nb_loc_presence)) / na.omit(as.numeric(nb_loc))) * 100 |
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308 } ## % of presence in observered plots if presence / absence |
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309 , { |
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310 valplot <- lapply(sort(year), FUN = function(x) { |
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311 mean(na.omit(as.numeric(subset(datatablecut, year == x)[, as.character(metric)]))) |
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312 }) |
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313 } ## mean if any other metric |
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314 ) |
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315 |
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316 tab2 <- data.frame(year, val = round(as.numeric(valplot), 2), ll = NA, ul = NA, catPoint = NA, pval = NA, |
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317 courbe = vpan[2], panel = vpan[2]) |
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318 |
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319 ## Creating ggplots |
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320 |
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321 dgg <- tab1 |
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322 |
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323 figname <- paste(sp, ".png", sep = "") |
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324 |
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325 ## coord for horizontal lines in graphs |
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326 hline_data1 <- data.frame(z = tab1$val[1], panel = c(vpan[1]), couleur = "var estimates", type = "var estimates") |
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327 hline_data3 <- data.frame(z = 0, panel = vpan[2], couleur = "seuil", type = "seuil") |
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328 hline_data <- rbind(hline_data1, hline_data3) |
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329 titre <- paste(sp) |
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330 |
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331 ## text for the population evolution trend |
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332 |
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333 pasdetemps <- max(dgg$year) - min(dgg$year) + 1 |
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334 if (! is.null(tab1t)) { |
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335 if (assess_ic) { |
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336 txt_pente1 <- paste("Global trend : ", tab1t$Est, |
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337 ifelse(tab1t$signif, " *", ""), |
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338 ifelse(tab1t$signif, paste("\n", ifelse(tab1t$pourcent > 0, "+ ", "- "), |
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339 abs(tab1t$pourcent), " % in ", pasdetemps, " years", sep = ""), ""), sep = "") |
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340 }else{ |
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341 txt_pente1 <- ifelse(tab1t$signif, paste("\n", ifelse(tab1t$pourcent > 0, "+ ", "- "), |
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342 abs(tab1t$pourcent), " % in ", pasdetemps, " years", sep = ""), "") |
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343 } |
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344 }else{ |
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345 trend_on_graph <- FALSE |
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346 } |
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347 |
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348 ## table of the text for the population evolution trend |
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349 tab_text_pent <- data.frame(y = c(max(c(dgg$val, dgg$ul), na.rm = TRUE) * .9), |
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350 x = median(dgg$year), |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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351 txt = ifelse(trend_on_graph, c(txt_pente1), ""), |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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352 courbe = c(vpan[1]), panel = c(vpan[1])) |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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353 |
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354 dgg <- rbind(tab1, tab2) |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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355 |
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356 ## colors for plots |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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357 vec_col_point <- c("#ffffff", "#eeb40f", "#ee0f59") |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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358 names(vec_col_point) <- c("significatif", "infSeuil", "0") |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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359 vec_col_courbe <- c("#3c47e0", "#5b754d", "#55bb1d", "#973ce0") |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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360 names(vec_col_courbe) <- c(vpan[1], "loc", "presence", vpan[2]) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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361 vec_col_hline <- c("#ffffff", "#e76060") |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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362 names(vec_col_hline) <- c("var estimates", "seuil") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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363 |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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364 col <- c(vec_col_point, vec_col_courbe, vec_col_hline) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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365 names(col) <- c(names(vec_col_point), names(vec_col_courbe), names(vec_col_hline)) |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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366 |
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367 p <- ggplot2::ggplot(data = dgg, mapping = ggplot2::aes_string(x = "year", y = "val")) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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368 ## Titles and scales |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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369 p <- p + facet_grid(panel ~ ., scale = "free") + |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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370 theme(legend.position = "none", |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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371 panel.grid.minor = element_blank(), |
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372 panel.grid.major.y = element_blank(), |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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373 axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) + |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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374 ylab("") + xlab("year") + ggtitle(titre) + |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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375 scale_colour_manual(values = col, name = "", |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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376 breaks = names(col)) + |
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377 scale_x_continuous(breaks = min(dgg$year):max(dgg$year)) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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378 p <- p + ggplot2::geom_hline(data = hline_data, mapping = ggplot2::aes_string(yintercept = "z", colour = "couleur", linetype = "type"), |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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379 alpha = 1, size = 1.2) |
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380 if (assess_ic) { ############# ONLY FOR THE CONFIDENCE INTERVAL |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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381 p <- p + ggplot2::geom_ribbon(mapping = ggplot2::aes_string(ymin = "ll", ymax = "ul"), fill = col[vpan[1]], alpha = .2) |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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382 p <- p + ggplot2::geom_pointrange(mapping = ggplot2::aes_string(y = "val", ymin = "ll", ymax = "ul"), fill = col[vpan[1]], alpha = .2) |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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383 } |
3ab852a7ff53
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384 |
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385 p <- p + ggplot2::geom_line(mapping = ggplot2::aes_string(colour = "courbe"), size = 1.5) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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386 p <- p + ggplot2::geom_point(mapping = ggplot2::aes_string(colour = "courbe"), size = 3) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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387 alph <- ifelse(!is.na(dgg$catPoint), 1, 0) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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388 p <- p + ggplot2::geom_point(mapping = ggplot2::aes_string(colour = "catPoint", alpha = alph), size = 2) |
3ab852a7ff53
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389 p <- p + ggplot2::geom_text(data = tab_text_pent, mapping = ggplot2::aes_string("x", "y", label = "txt"), parse = FALSE, color = col[vpan[1]], fontface = 2, size = 4) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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390 ggplot2::ggsave(figname, p, width = 16, height = 15, units = "cm") |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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391 } |
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392 ############################################################################################################ fin fonction graphique / end of function for graphical output |
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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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393 |
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394 ################# Analysis |
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395 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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396 for (sp in glmtable[, 1]) { |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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397 |
3ab852a7ff53
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398 if (!all(is.na(glmtable[glmtable[, 1] == sp, 4:(length(glmtable) - 1)]))) { ##ignore lines with only NA |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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399 ggplot_glm(glmtable = glmtable, datatable = datatable, unitobs = unitobs, metric = metric, sp = sp, description = TRUE, trend_on_graph = TRUE, assess_ic = assess_ic) |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
ecology
parents:
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400 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
ecology
parents:
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401 } |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
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402 |
3ab852a7ff53
"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 04381ca7162ec3ec68419e308194b91d11cacb04"
ecology
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403 sink("stdout.txt", split = TRUE) |