Mercurial > repos > ecology > pampa_glmsp

annotate FunctExeCalcGLMSpGalaxy.r @ 2:6c14021f678e draft

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"planemo upload for repository https://github.com/ColineRoyaux/PAMPA-Galaxy commit 3df1978827a91be30e815dee2ed83a92862d1b1c"
author ecology
date Sun, 22 Nov 2020 18:40:40 +0000
parents 0778efa9eb2e
children c12897ba5f83
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1 #Rscript
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3 #####################################################################################################################
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4 #####################################################################################################################
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5 ################################# Compute a Generalized Linear Model from your data #################################
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6 #####################################################################################################################
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7 #####################################################################################################################
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9 ###################### Packages
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10 suppressMessages(library(multcomp))
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11 suppressMessages(library(DHARMa))
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12 suppressMessages(library(glmmTMB)) ###Version: 0.2.3
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13 suppressMessages(library(gap))
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16 ###################### Load arguments and declaring variables
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18 args <- commandArgs(trailingOnly = TRUE)
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20 if (length(args) < 10) {
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21 stop("At least 4 arguments must be supplied : \n- two input dataset files (.tabular) : metrics table and unitobs table \n- Interest variable field from metrics table \n- Response variable from unitobs table.", call. = FALSE) # if no args -> error and exit1
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23 } else {
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24 import_data <- args[1] ###### file name : metrics table
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25 import_unitobs <- args[2] ###### file name : unitobs informations
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26 colmetric <- as.numeric(args[3]) ###### Selected interest metric for GLM
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27 list_fact <- strsplit(args [4], ",")[[1]] ###### Selected response factors for GLM
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28 list_rand <- strsplit(args [5], ",")[[1]] ###### Selected randomized response factors for GLM
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29 col_fact_ana <- args[6] ####### (optional) Selected splitting factors for GLMs
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30 distrib <- args[7] ###### (optional) Selected distribution for GLM
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31 glm_out <- args[8] ###### (Optional) GLM object as Rdata output ?
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32 aggreg <- args[9] ###### Aggregation level of the data table
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33 source(args[10]) ###### Import functions
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35 }
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36 #### d_ata must be a dataframe with at least 3 variables : unitobs representing location and year ("observation.unit"), species code ("species.code") and abundance ("number")
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39 #Import des données / Import data
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40 obs <- read.table(import_data, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8") #
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41 obs[obs == -999] <- NA
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42 metric <- colnames(obs)[colmetric]
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43 tab_unitobs <- read.table(import_unitobs, sep = "\t", dec = ".", header = TRUE, encoding = "UTF-8")
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44 tab_unitobs[tab_unitobs == -999] <- NA
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46 vars_data1 <- c("species.code")
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47 err_msg_data1 <- "The input metrics dataset doesn't have the right format. It needs to have at least the following 3 variables :\n- species.code \n- observation.unit (or year and site)\n- numeric or integer metric\n"
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48 check_file(obs, err_msg_data1, vars_data1, 3)
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50 vars_data2 <- c("observation.unit", list_fact, list_rand)
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51 err_msg_data2 <- "The input unitobs dataset doesn't have the right format. It needs to have at least the following 2 variables :\n- observation.unit (or year and site)\n- factors used in GLM (habitat, year and/or site)\n"
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52 check_file(tab_unitobs, err_msg_data2, vars_data2[vars_data2 != "None"], 2)
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55 if (col_fact_ana != "None") {
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56 fact_ana <- col_fact_ana
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57 if (class(obs[fact_ana]) == "numeric" || fact_ana == "observation.unit") {
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58 stop("Wrong chosen separation factor : Analysis can't be separated by observation unit or numeric factor")
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59 }
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60 }else{
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61 fact_ana <- col_fact_ana
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62 }
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64 if (all(c(list_fact, list_rand) == "None")) {
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65 stop("GLM needs to have at least one response variable.")
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66 }
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68 if (list_fact[1] == "None" || all(is.element(list_fact, list_rand))) {
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69 stop("GLM can't have only random effects.")
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70 }
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72 ####################################################################################################
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73 ########## Computing Generalized Linear Model ## Function : glm_species ############
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74 ####################################################################################################
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75
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76 glm_species <- function(metrique, list_fact, list_rand, fact_ana, distrib, tab_metrics, tab_metrique, tab_unitobs, unitobs = "observation.unit", nb_name = "number") {
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77 ## Purpose: Monitoring steps for GLM on species data
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78 ## ----------------------------------------------------------------------
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79 ## Arguments: metrique : selected metric
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80 ## list_fact : Factors for GLM
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81 ## list_rand : Random factors for GLM
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82 ## fact_ana : Separation factor for GLMs
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83 ## distrib : selected distribution for model
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84 ## tab_metrics : data table metrics
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85 ## tab_metrique : data table's name
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86 ## tab_unitobs : data table unitobs
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87 ## ----------------------------------------------------------------------
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88 ## Author: Yves Reecht, Date: 18 août 2010, 15:59 modified by Coline ROYAUX 04 june 2020
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89
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90 tmpd_ata <- tab_metrics
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91
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92 out_fact <- .GlobalEnv$organise_fact(list_rand = list_rand, list_fact = list_fact)
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93 resp_fact <- out_fact[[1]]
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94 list_f <- out_fact[[2]]
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95 list_fact <- out_fact[[3]]
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96
0
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97 ##Creating model's expression :
2
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98 expr_lm <- eval(parse(text = paste(metrique, "~", resp_fact)))
0
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99
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100 ##Creating analysis table :
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101 list_fact_tab <- c(list_fact, fact_ana)
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102 list_fact_tab <- list_fact_tab[list_fact_tab != "None"]
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103
2
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104 if (all(is.na(match(tmpd_ata[, unitobs], tab_unitobs[, unitobs])))) {
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105 stop("Observation units doesn't match in the two input tables")
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106 }
0
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107
2
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108 if (is.element("species.code", colnames(tmpd_ata))) {
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109 col <- c(unitobs, metrique, fact_ana)
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110 tmpd_ata <- cbind(tmpd_ata[, col], tab_unitobs[match(tmpd_ata[, unitobs], tab_unitobs[, unitobs]), list_fact])
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111 colnames(tmpd_ata) <- c(col, list_fact)
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112
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113 for (i in list_fact_tab) {
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114 tmpd_ata[, i] <- as.factor(tmpd_ata[, i])
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115 }
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116 }else{
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117 stop("Warning : wrong data frame, data frame should be aggregated by observation unit (year and site) and species")
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118 }
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119
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120 ## Suppression des 'levels' non utilisés :
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121 tmpd_ata <- .GlobalEnv$drop_levels_f(tmpd_ata)
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122
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123 ## Automatic choice of distribution if none is selected by user :
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124
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125 chose_distrib <- .GlobalEnv$distrib_choice(distrib = distrib, metrique = metrique, data = tmpd_ata)
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126
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127 ##Create results table :
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128 lev <- unlist(lapply(list_f, FUN = function(x) {
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129 levels(tmpd_ata[, x])
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130 }))
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131 row <- levels(tmpd_ata[, fact_ana])
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132
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133 if (is.element("year", list_f) && ! is.element("year", list_rand)) {
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134 tab_sum <- .GlobalEnv$create_res_table(list_rand = list_rand, list_fact = list_fact, row = row, lev = unlist(c("year", lev)), distrib = chose_distrib)
0
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135 }else{
2
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136 tab_sum <- .GlobalEnv$create_res_table(list_rand = list_rand, list_fact = list_fact, row = row, lev = lev, distrib = chose_distrib)
0
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137 }
2
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138 ### creating rate table
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139 tab_rate <- data.frame(species = row, complete_plan = NA, balanced_plan = NA, NA_proportion_OK = NA, no_residual_dispersion = NA, uniform_residuals = NA, outliers_proportion_OK = NA, no_zero_inflation = NA, observation_factor_ratio_OK = NA, enough_levels_random_effect = NA, rate = NA)
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140
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141 ## Compute Model(s) :
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142
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143 for (sp in levels(tmpd_ata[, fact_ana])) {
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144 cutd_ata <- tmpd_ata[grep(sp, tmpd_ata[, fact_ana]), ]
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145 cutd_ata <- .GlobalEnv$drop_levels_f(cutd_ata)
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146
2
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147 res <- ""
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148 resy <- ""
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149
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150 if (list_rand[1] != "None") {
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151 res <- tryCatch(glmmTMB(expr_lm, family = chose_distrib, data = cutd_ata), error = function(e) {
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152 })
0
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153
2
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154 if (is.element("year", list_f) && ! is.element("year", list_rand)) { #Model with year as continuous
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155 cutd_ata$year <- as.numeric(cutd_ata$year)
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156 resy <- tryCatch(glmmTMB(expr_lm, family = chose_distrib, data = cutd_ata), error = function(e) {
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157 })
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158 cutd_ata$year <- as.factor(cutd_ata$year)
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159 }else{
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160 resy <- ""
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161 }
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162 }else{
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163 res <- tryCatch(glm(expr_lm, data = cutd_ata, family = chose_distrib), error = function(e) {
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164 })
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165 if (is.element("year", list_f)) { #Model with year as continuous
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166 cutd_ata$year <- as.numeric(cutd_ata$year)
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167 resy <- tryCatch(glm(expr_lm, family = chose_distrib, data = cutd_ata), error = function(e) {
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168 })
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169 cutd_ata$year <- as.factor(cutd_ata$year)
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170 }else{
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171 resy <- ""
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172 }
0
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173 }
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174
2
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175 ## Write results :
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176 if (! is.null(res)) {
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177 file_save_glm_sp <- paste("GLM_", sp, ".Rdata", sep = "")
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178 save(res, file = file_save_glm_sp)
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179
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180 tab_sum <- .GlobalEnv$sorties_lm_f(obj_lm = res, obj_lmy = resy, tab_sum = tab_sum, fact_ana = fact_ana, cut = sp, col_ana = "analysis", lev = lev, d_ata = cutd_ata, metrique = metrique, list_fact = list_fact, list_rand = list_rand)
0
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181
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182 tab_rate[tab_rate[, "species"] == sp, c(2:11)] <- .GlobalEnv$note_glm_f(data = cutd_ata, obj_lm = res, metric = metrique, list_fact = list_fact, details = TRUE)
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183
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184 }else{
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185 cat("\nCannot compute GLM for species", sp, "Check if one or more factor(s) have only one level, or try with another distribution for the model in advanced settings \n\n")
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186 }
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187
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188 }
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189 .GlobalEnv$note_glms_f(tab_rate = tab_rate, expr_lm = expr_lm, obj_lm = res, file_out = TRUE)
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190
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191 ## simple statistics and infos :
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192 filename <- "GLMSummaryFull.txt"
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193
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194 ## Save data on model :
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195
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196 info_stats_f(filename = filename, d_ata = tmpd_ata, agreg_level = aggreg, type = "stat",
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197 metrique = metrique, fact_graph = fact_ana, #fact_graph_sel = modSel,
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198 list_fact = list_fact)#, list_fact_sel = list_fact_sel)
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199
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200 return(tab_sum)
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201 }
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202
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203 ################# Analysis
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204
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205 tab <- glm_species(metrique = metric, list_fact = list_fact, list_rand = list_rand, fact_ana = fact_ana, distrib = distrib, tab_metrics = obs, tab_metrique = aggreg, tab_unitobs = tab_unitobs, nb_name = "number")
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206
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207 write.table(tab, "GLMSummary.tabular", row.names = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8")