--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/FunctExeCalcGLMGalaxy.r	Tue Jul 21 06:00:31 2020 -0400
@@ -0,0 +1,277 @@
+#Rscript 
+
+#####################################################################################################################
+#####################################################################################################################
+################################# Compute a Generalized Linear Model from your data #################################
+#####################################################################################################################
+#####################################################################################################################
+
+###################### Packages
+suppressMessages(library(multcomp))
+suppressMessages(library(glmmTMB)) ###Version: 0.2.3
+suppressMessages(library(gap)) 
+
+###################### Load arguments and declaring variables
+
+args = commandArgs(trailingOnly=TRUE)
+#options(encoding = "UTF-8")
+
+if (length(args) < 10) {
+    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) #si pas d'arguments -> affiche erreur et quitte / if no args -> error and exit1
+
+} else {
+    Importdata <- args[1] ###### file name : metrics table
+    ImportUnitobs <- args[2] ###### file name : unitobs informations
+    colmetric <- as.numeric(args[3]) ###### Selected interest metric for GLM
+    listFact <- strsplit(args [4],",")[[1]] ###### Selected response factors for GLM
+    listRand <- strsplit(args [5],",")[[1]] ###### Selected randomized response factors for GLM
+    colFactAna <- args[6] ####### (optional) Selected splitting factors for GLMs
+    Distrib <- args[7] ###### (optional) Selected distribution for GLM 
+    log <- args[8] ###### (Optional) Log on interest metric ?
+    aggreg <- args[9] ###### Aggregation level of the data table
+    source(args[10]) ###### Import functions
+
+}
+#### Data must be a dataframe with at least 3 variables : unitobs representing location and year ("observation.unit"), species code ("species.code") and abundance ("number")
+
+
+#Import des données / Import data 
+obs<- read.table(Importdata,sep="\t",dec=".",header=TRUE,encoding="UTF-8") #
+obs[obs == -999] <- NA 
+metric <- colnames(obs)[colmetric]
+tabUnitobs <- read.table(ImportUnitobs,sep="\t",dec=".",header=TRUE,encoding="UTF-8")
+tabUnitobs[tabUnitobs == -999] <- NA 
+
+if (colFactAna != "None")
+{
+    FactAna <- colnames(tabUnitobs)[as.numeric(colFactAna)]
+    if (class(tabUnitobs[FactAna]) == "numeric" || FactAna == "observation.unit"){stop("Wrong chosen separation factor : Analysis can't be separated by observation unit or numeric factor")}
+}else{
+    FactAna <- colFactAna
+}
+
+
+#factors <- fact.det.f(Obs=obs)
+
+vars_data1<- NULL
+err_msg_data1<-"The input metrics 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- numeric or integer metric\n"
+check_file(obs,err_msg_data1,vars_data1,2)
+
+vars_data2 <- c(listFact,listRand)
+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"
+check_file(tabUnitobs,err_msg_data2,vars_data2[vars_data2 != "None"],2)
+
+####################################################################################################
+########## Computing Generalized Linear Model ## Function : modeleLineaireWP2.unitobs.f ############
+####################################################################################################
+
+modeleLineaireWP2.unitobs.f <- function(metrique, listFact, listRand, FactAna, Distrib, log=FALSE, tabMetrics, tableMetrique, tabUnitobs, unitobs="observation.unit", nbName="number")
+{
+    ## Purpose: Monitoring steps for GLM on unitobs
+    ## ----------------------------------------------------------------------
+    ## Arguments: metrique : selected metric
+    ##            listFact : Factors for GLM
+    ##            listRand : Random factors for GLM
+    ##            factAna : Separation factor for GLMs
+    ##            Distrib : selected distribution for model
+    ##            log : log transformation on data ? boolean
+    ##            tabMetrics : data table metrics
+    ##            tableMetrique : data table's name
+    ##            tabUnitobs : data table unitobs
+    ## ----------------------------------------------------------------------
+    ## Author: Yves Reecht, Date: 18 août 2010, 15:59 modified by Coline ROYAUX 04 june 2020
+
+    tmpData <- tabMetrics
+
+    if (listRand[1] != "None")
+    {
+        if (all(is.element(listFact,listRand)) || listFact[1] == "None")
+        {
+            RespFact <- paste("(1|",paste(listRand,collapse=") + (1|"),")")
+            listF <- NULL
+            listFact <- listRand
+        }else{
+            listF <- listFact[!is.element(listFact,listRand)]
+            RespFact <- paste(paste(listF, collapse=" + ")," + (1|",paste(listRand,collapse=") + (1|"),")")
+            listFact <- c(listF,listRand)
+        }   
+    }else{
+        listF <- listFact
+        RespFact <- paste(listFact, collapse=" + ")
+    }
+
+    ##Creating model's expression :
+
+    #if (log == FALSE) {
+        exprML <- eval(parse(text=paste(metrique, "~", RespFact)))
+    #}else{
+     #   exprML <- eval(parse(text=paste("log(",metrique,")", "~", RespFact)))
+    #}
+
+    ##Creating analysis table :
+
+    listFactTab <- c(listFact, FactAna)
+    listFactTab <- listFactTab[listFactTab != "None"]
+
+    if (all(is.na(match(tmpData[,unitobs],tabUnitobs[,unitobs])))) {stop("Observation units doesn't match in the two input tables")}
+
+    if(! is.element("species.code",colnames(tmpData)))
+    {
+        col <- c(unitobs,metrique)
+        tmpData <- cbind(tmpData[,col], tabUnitobs[match(tmpData[,unitobs],tabUnitobs[,unitobs]),listFactTab])
+        colnames(tmpData) <- c(col,listFactTab)
+
+        for (i in listFactTab) {
+            switch(i,
+                  tmpData[,i] <- as.factor(tmpData[,i]))
+         }
+    }else{
+        stop("Warning : wrong data frame, data frame should be aggregated by observation unit (year and site)")
+    }
+
+    ## Suppress unsed 'levels' :
+    tmpData <- dropLevels.f(tmpData)
+
+    ## Automatic choice of distribution if none is selected by user :
+    if (Distrib == "None") 
+    {
+        switch(class(tmpData[,metrique]),
+              "integer"={loiChoisie <- "poisson"},
+              "numeric"={loiChoisie <- "gaussian"},
+              stop("Selected metric class doesn't fit, you should select an integer or a numeric variable"))
+    }else{
+        loiChoisie <- Distrib
+    }
+
+
+    ## Compute Model(s) :
+
+    if (FactAna != "None" && nlevels(tmpData[,FactAna]) > 1)
+    {
+        Anacut <- levels(tmpData[,FactAna])
+    }else{
+        Anacut <- NULL
+    }
+
+    ##Create results table : 
+    lev <- unlist(lapply(listF,FUN=function(x){levels(tmpData[,x])}))
+
+    if (listRand[1] != "None") ## if random effects
+    {
+        TabSum <- data.frame(analysis=c("global", Anacut),AIC=NA,BIC=NA,logLik=NA, deviance=NA,df.resid=NA)
+        colrand <- unlist(lapply(listRand, 
+                           FUN=function(x){lapply(c("Std.Dev","NbObservation","NbLevels"),
+                                                  FUN=function(y){paste(x,y,collapse = ":")
+                                                                 })
+                                          }))
+        TabSum[,colrand] <- NA
+
+        if (! is.null(lev)) ## if fixed effects + random effects
+        {
+            colcoef <- unlist(lapply(c("(Intercept)",lev),
+                               FUN=function(x){lapply(c("Estimate","Std.Err","Zvalue","Pvalue","signif"),
+                                                      FUN=function(y){paste(x,y,collapse = ":")
+                                                                     })
+                                              }))
+        }else{ ## if no fixed effects
+            colcoef <- NULL
+        }
+
+    }else{ ## if no random effects
+        TabSum <- data.frame(analysis=c("global", Anacut),AIC=NA,Resid.deviance=NA,df.resid=NA,Null.deviance=NA,df.null=NA)
+
+        switch(loiChoisie,
+               "gaussian"={colcoef <- unlist(lapply(c("(Intercept)",lev),
+                                             FUN=function(x){lapply(c("Estimate","Std.Err","Tvalue","Pvalue","signif"),
+                                                                    FUN=function(y){paste(x,y,collapse = ":")
+                                                                                   })
+                                                            }))},
+               "quasipoisson"={colcoef <- unlist(lapply(c("(Intercept)",lev),
+                                             FUN=function(x){lapply(c("Estimate","Std.Err","Tvalue","Pvalue","signif"),
+                                                                    FUN=function(y){paste(x,y,collapse = ":")
+                                                                                   })
+                                                            }))},
+               colcoef <- unlist(lapply(c("(Intercept)",lev),
+                                        FUN=function(x){lapply(c("Estimate","Std.Err","Zvalue","Pvalue","signif"),
+                                                               FUN=function(y){paste(x,y,collapse = ":")
+                                                                              })
+                                                       })))
+
+    }  
+  
+    TabSum[,colcoef] <- NA
+
+    ### creating rate table 
+    TabRate <- data.frame(analysis=c("global", Anacut), 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)
+
+    for (cut in Anacut) 
+    {
+        cutData <- tmpData[grep(cut,tmpData[,FactAna]),]
+        cutData <- dropLevels.f(cutData)
+
+        res <-""
+
+        if (listRand[1] != "None")
+        {
+            res <- tryCatch(glmmTMB(exprML,family=loiChoisie, data=cutData), error=function(e){})
+        }else{
+            res <- tryCatch(glm(exprML,data=cutData,family=loiChoisie), error=function(e){})
+        }
+
+          ## Write results :
+         if (! is.null(res))
+         {
+            TabSum <- sortiesLM.f(objLM=res, TabSum=TabSum, metrique=metrique,
+                                  factAna=factAna, cut=cut, colAna="analysis", lev=lev, #modSel=iFactGraphSel, listFactSel=listFactSel,
+                                  listFact=listFact,
+                                  Data=cutData, #Log=Log,
+                                  type=ifelse(tableMetrique == "unitSpSz" && factAna != "size.class",
+                                              "CL_unitobs",
+                                              "unitobs"))
+
+            TabRate[TabRate[,"analysis"]==cut,c(2:11)] <- noteGLM.f(data=cutData, objLM=res, metric=metrique, listFact=listFact, details=TRUE)
+
+        }else{
+            cat("\nCannot compute GLM for level",cut,"Check if one or more factor(s) have only one level, or try with another distribution for the model in advanced settings \n\n")
+        }
+
+    }
+
+    ## Global analysis : 
+
+    if (listRand[1] != "None")
+    {
+        resG <- glmmTMB(exprML,family=loiChoisie, data=tmpData)
+    }else{
+        resG <- glm(exprML,data=tmpData,family=loiChoisie)
+    }
+
+    ## write results :
+    TabSum <- sortiesLM.f(objLM=resG, TabSum=TabSum, metrique=metrique,
+                          factAna=factAna, cut="global", colAna="analysis", lev=lev, #modSel=iFactGraphSel, listFactSel=listFactSel,
+                          listFact=listFact,
+                          Data=tmpData, #Log=Log,
+                          type=ifelse(tableMetrique == "unitSpSz" && factAna != "size.class",
+                                      "CL_unitobs",
+                                      "unitobs"))
+
+    TabRate[TabRate[,"analysis"]=="global",c(2:11)] <- noteGLM.f(data=tmpData, objLM=resG, metric=metrique, listFact=listFact, details=TRUE)
+    noteGLMs.f(tabRate=TabRate,exprML=exprML,objLM=resG, file_out=TRUE)
+    ## simple statistics and infos :
+    filename <- "GLMSummaryFull.txt"
+
+    ## Save data on model :
+        
+    infoStats.f(filename=filename, Data=tmpData, agregLevel=aggreg, type="stat",
+                metrique=metrique, factGraph=factAna, #factGraphSel=modSel,
+                listFact=listFact)#, listFactSel=listFactSel)
+
+    return(TabSum)
+
+}
+
+################# Analysis
+
+Tab <- modeleLineaireWP2.unitobs.f(metrique=metric, listFact=listFact, listRand=listRand, FactAna=FactAna, Distrib=Distrib, log=log, tabMetrics=obs, tableMetrique=aggreg, tabUnitobs=tabUnitobs, nbName="number")
+
+write.table(Tab,"GLMSummary.tabular", row.names=FALSE, sep="\t", dec=".",fileEncoding="UTF-8")