# HG changeset patch
# User ecology
# Date 1589231896 14400
# Node ID a961be0a964f055643ebd03ec269780b1a6f5f60
# Parent  df3ce23d0d2360df16b866b9a1209a8d19bea055
"planemo upload for repository https://github.com/Alanamosse/Galaxy-E/tree/stoctool/tools/stoc commit d49a4da1ffac1f33a15fdb7ae98827d0034a7879"

diff -r df3ce23d0d23 -r a961be0a964f FunctTrendSTOCGalaxy.r
--- a/FunctTrendSTOCGalaxy.r	Thu Apr 02 03:32:56 2020 -0400
+++ b/FunctTrendSTOCGalaxy.r	Mon May 11 17:18:16 2020 -0400
@@ -62,7 +62,7 @@
     if(length(colNull)>0){
         cat("\n",length(colNull)," Species removed from the analysis, abundance is always 0.\n\n",sep="")  #Espèces enlevées de l'analyse car abondance toujours égale a 0\n\n",sep="")
         #tabNull <- data.frame(Code_espece = colNull, nom_espece = tabsp[colNull,"nom"])
-        #cat("\n\n",sep="")
+        cat("Removed absent species : \n-",paste(colNull,collapse="\n-"),sep="")
         tab <- tab[,c("carre","annee",colConserve)]
     }
 ################################################################################ FIN DE LA PARTIE ISOLABLE
@@ -119,7 +119,7 @@
         cat("\n",length(colSupr)," Rare species removed from the analysis.\n\n",sep="")
         #tabSupr <- subset(tabsp,espece %in% colSupr ,select=c("espece","nom"))
         #tabSupr <- tabSupr[order(tabSupr$espece),]
-        #cat("\n\n",sep="")
+        cat("Removed rare species : \n-",paste(colSupr,collapse="\n-"),sep="")
         
     }
     if(length(colConserve)==0) {
diff -r df3ce23d0d23 -r a961be0a964f mainglm.xml
--- a/mainglm.xml	Thu Apr 02 03:32:56 2020 -0400
+++ b/mainglm.xml	Mon May 11 17:18:16 2020 -0400
@@ -1,4 +1,4 @@
-<tool id="stoceps_glm" name="Estimate temporal population evolution" version="@VERSION@">
+<tool id="stoceps_glm" name="Estimate temporal population variation" version="@VERSION@">
     <description>by species</description>
     <macros>
         <import>stoceps_macros.xml</import>
@@ -94,12 +94,12 @@
     </tests>
     <help><![CDATA[
 =================================================
-STOC Estimate species population evolution
+STOC Estimate species population variation
 =================================================
 
 **What it does**
 
-Compute and plot evolution of species population, using a glm model.
+Compute and plot trends of species population, using a glm model of formula : abundance ~ site + year. Testing the effect of site and year on abundance with quasipoisson distribution.
 
 |
 
@@ -142,44 +142,6 @@
 UnPublished script available at http://www.vigienature.fr/sites/vigienature/files/atoms/files/analysestoceps_0.zip
 the first version written by Romain Lorrilliere
 
-Original script information:
-
-Estimate temporal evolution of population per species - ExeMainGlmGalaxy.r
-This script analyse the temporal evolution of species population and create graphical vizualisation.
-
-Script needs the followings inputs :
- - stoc or community data filtered with at least 4 columns: year, site, species, and abundance with 0. Corresponding to "observed" or predicted 0 abundance. May come from the tools "Preprocess population data for evolution trend analyzes" (ExemakeTableAnalyseGalaxy.r) followed by "Filter species with rare and low abundances" (ExeFilteringRareLowabundSPGalaxy.r).
- - species details file with name and indicator status file with at least 2 columns: the species name or species ID (found in the community data or in stoc data) and his status as indicator species
- - file that stocks functions : "FunctTrendSTOCGalaxy.r"
-
-
-Arguments are :
- - spExclude: list of species (using the the species name or ID) that you want to exclude
- - assessIC : compute and show confidence interval in plots (TRUE / FALSE)
- - analysis custom id
-
-
-How to execute, eg :
- # all files are available in github repo
- #Exec id=mainglm, return IC on plot, no species excluded
- $ Rscript ExeMainGlmGalaxy.r' Datafilteredfortrendanalysis.tabular tabSpecies.csv 'mainglm' '' 'TRUE' FunctTrendSTOCGalaxy.r
-
-
-Outputs are created in an Output repo :
-GLM gives 1 graph per species and 2 tables:
-- nameofspecies_id.png (one plot per species)
-- tendanceGlobalEspece_id.tabular
-- variationsAnnuellesEspece_id.tabular
-
-
-R library needed
-r-lme4  version 1.1.18.1
-r-ggplot2  version 3.0.0
-r-speedglm  version 0.3.2
-r-arm  version 1.10.1
-r-reshape  version 0.8.8
-r-data.table  version 1.12.0
-r-reshape2   version 1.4.3
   ]]></help>
   <expand macro="stoceps_bibref" />
 </tool>
diff -r df3ce23d0d23 -r a961be0a964f stoceps_macros.xml
--- a/stoceps_macros.xml	Thu Apr 02 03:32:56 2020 -0400
+++ b/stoceps_macros.xml	Mon May 11 17:18:16 2020 -0400
@@ -1,5 +1,5 @@
 <macros>
-    <token name="@VERSION@">0.0.1</token>
+    <token name="@VERSION@">0.0.2</token>
     <xml name="reshape_requirements">
         <requirements>
             <requirement type="package" version="0.8.8">r-reshape</requirement>
@@ -53,11 +53,10 @@
         <param name="compute_ic" type="boolean" truevalue="TRUE" falsevalue="FALSE" checked="yes" label="Compute confidence intervals"/>
     </xml>
     <xml name="stoceps_filter_glmmtmb">
-        <filter> settings['advanced'] == 'advanced'</filter>
-        <filter> settings['method'] == 'glmmtmb'</filter>
+        <filter> method['model'] == 'glmmtmb'</filter>
     </xml>
     <xml name="stoceps_filter_gam">
-        <filter> settings['method'] == 'gam'</filter>
+        <filter> method['model'] == 'gam'</filter>
     </xml>
     <xml name="stoceps_bibref">
         <citations>