solve issue in data resampling and stan mergeing of chains

R/analysis/lmer.run.R

@@ -69,7 +69,7 @@ fun.call.lmer.and.save <- function(formula, df.lmer, path.out,
 run.lmer <- function (model.file, trait,
                       min.obs = 10, sample.size = NA,
                       type.filling, cat.TF, fname = 'data.all.no.log.rds',
-                      var.sample = 'ecocode.id',
+                      var.sample = 'ecocode',
                       select.biome = NA) {
     require(lme4)
     source(model.file, local = TRUE)
@@ -128,18 +128,18 @@ load.data.for.lmer <- function(trait, cat.TF,
                                fname = 'data.all.no.log.rds',
                                base.dir = "output/processed",
                                sample.size. ,
-                               var.sample. = 'ecocode.id',
+                               var.sample. = 'ecocode',
                                select.biome. = NA,
                                sample.vec.TF. = FALSE){
 if (fname == 'data.all.no.log.rds') type <- 'no.log'
 if (fname == 'data.all.log.rds') type <- 'log'
 if (cat.TF) cat <- 'cat'
 if (!cat.TF) cat <- 'no.cat'
-df <- readRDS(file = file.path(base.dir,paste('data', trait, type, cat,'rds',
+df <- readRDS(file = file.path(base.dir,paste('data', trait, type,'rds',
                                               sep = '.')))
-df$species.id[is.na(df$species.id)] <-  'missing.sp'
+df$sp.name[is.na(df$sp.name)] <-  'missing.sp'
 if(!is.na(select.biome.)) {
-df <-  df[df$biomes.id == select.biome., ]
+df <-  df[df$biomes == select.biome., ]
   }
 if(!is.na(sample.size.)){
    if(!sample.vec.TF.){
@@ -151,24 +151,24 @@ if(!is.na(sample.size.)){
      saveRDS(sample.vec,file = file.path(base.dir,paste('sample.vec', trait, type, cat,'rds',
                                  sep = '.')))
      print(paste('sub-sampled by',var.sample.))
-     df <- fun.format.data.for.lmer(data.tree.tot, trait,
+     res <- fun.format.data.for.lmer(df, trait,
                                     type.filling = type.filling,
                                     cat.TF = cat.TF)
    }else{
      sample.vec <- readRDS(file = file.path(base.dir,paste('sample.vec', trait, type, cat,'rds',
                                  sep = '.')))
      df <- df[sample.vec, ]
-     df <- fun.format.data.for.lmer(data.tree.tot, trait,
+     res <- fun.format.data.for.lmer(df, trait,
                                     type.filling = type.filling,
                                     cat.TF = cat.TF)
      print(paste('sub-sampled by',var.sample., 'by loading sample.vec'))
    }    
   }else{
-    df <- fun.format.data.for.lmer(data.tree.tot, trait,
+    res <- fun.format.data.for.lmer(df, trait,
                                    type.filling = type.filling,
                                   cat.TF = cat.TF)
- }     
-return( df)
+ }
+return( res)
 }
 
 load.and.save.data.for.lmer <- function(trait,
@@ -179,8 +179,7 @@ load.and.save.data.for.lmer <- function(trait,
                                         data.table.TF = FALSE){
 data.tree.tot <- readRDS(file.path(base.dir, fname))
 df <- fun.select.data.for.lmer(data.tree.tot, trait,
-                        type.filling = type.filling,
-                        cat.TF = cat.TF)
+                        type.filling = type.filling)
 if (fname == 'data.all.no.log.rds') type <- 'no.log'
 if (fname == 'data.all.log.rds') type <- 'log'
 saveRDS(df,file = file.path(base.dir,paste('data', trait, type, 'rds',

R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.r.biomes.R

@@ -68,15 +68,15 @@ parameters{
     vector[N_biomes] param_sumTnBn_biomes;
     vector[N_biomes] param_sumTnTfBn_abs_biomes;
     vector[N_species] param_logD_species;
-    vector[N_species] intercept_species;
-    vector[N_plot] intercept_plot;
-    vector[N_set] intercept_set;
-    vector[N_tree] intercept_tree;
 }
 transformed parameters {
     # vector for prediction
     vector[N_indiv] theo_g;
-
+    vector[N_species] intercept_species;
+    vector[N_plot] intercept_plot;
+    vector[N_set] intercept_set;
+    vector[N_tree] intercept_tree;
+ 
     for (i in 1:N_indiv){
         theo_g[i] <- intercept + intercept_species[species_id[i]]
           + intercept_tree[tree_id[i]]*tree_01[i]

R/analysis/stan.all.no.log.output.R

@@ -12,53 +12,20 @@ for (trait in traits){
     for(model in c(model.files.stan.Tf.1[3])){
        source(model, local = TRUE)
        model.obj <- load.model()
-## TODO
        path.out <- output.dir(type = 'stan', model = model.obj$name,
                            trait = trait, set = 'all.no.log',
                            type.filling = 'species')
-       paths <- c(files,path.out)
+       
+       paths <- c(paths,path.out)
 
       }
     }
 ## TODO 
-        paste(var.sample,
-              "results.stan", chains.id, "rds", sep = '.')
 
-out <- lapply(files, summarise.lmer.output.all.list)
-names(out) <- lapply(lapply(files,files.details.all),
-                     function(x) paste(as.vector(x[names(x) != 'file']),
-                                       collapse="_"))
-### remove missing
-out <- out[!unlist(lapply(out,FUN=function(x) is.null(x$lmer.summary)))]
-saveRDS(out,file='output/list.lmer.out.all.ecocode.id.no.log.rds')
-
-## LOOP OVER FILES AND NOT SCAN ALL FILES
-traits <- c("SLA", "Wood.density","Max.height")
-type.filling <- 'species'
-files <-  c()
-for (trait in traits){
-    for(model in c(model.files.lmer.Tf.1,
-                   model.files.lmer.Tf.2,
-                   model.files.lmer.Tf.3,
-                   model.files.lmer.Tf.5,
-                   model.files.lmer.Tf.CAT.1,
-                   model.files.lmer.Tf.CAT.2)){
-       source(model, local = TRUE)
-       model.obj <- load.model()
-       pathout <- output.dir('lmer', model.obj$name, trait, 'all.no.log',
-                                type.filling=type.filling)
-
-       files <- c(files,file.path(pathout,"species.id.results.nolog.all.rds"))
-
-      }
-    }
-
-out <- lapply(files, summarise.lmer.output.all.list)
-names(out) <- lapply(lapply(files,files.details.all),
-                     function(x) paste(as.vector(x[names(x) != 'file']),
-                                       collapse="_"))
-### remove missing
-out <- out[!unlist(lapply(out,FUN=function(x) is.null(x$lmer.summary)))]
-saveRDS(out,file='output/list.lmer.out.all.species.id.no.log.rds')
+out <-  lapply(1:1, fun.load.print.conv,
+               paths,
+               models = c(model.files.stan.Tf.1[3]),
+               var.sample = 'ecocode.id',
+               chains.vec = 1:3)
 
 

R/analysis/stan.output-fun.R

@@ -13,6 +13,25 @@ res.merged <- sflist2stanfit(sflist)
 return(res.merged)
 }
 
+
+
+fun.print.conv <- function(res.stan, pars){
+     res <- monitor(extract(res.stan, pars,
+                            permuted = FALSE,
+                            inc_warmup = TRUE))
+     return(res)
+ }
+
+## function to load and print convergence
+    
+fun.load.print.conv <- function(i,paths, models, var.sample, chains.vec){
+  source(models[i], local = TRUE)
+  model.obj <- load.model()
+  res.stan <- fun.merge.chain(paths[i], var.sample, chains.vec)
+  res <- fun.print.conv(res.stan, model.obj$pars)
+  print(res)
+  return(res)
+}
 ## path.out <- 'output/stan/all.no.log/Wood.density/species/LOGLIN.ER.AD.Tf.r.biomes'
 ## res <- fun.merge.chain(path.out, var.sample = 'ecocode.id', chains.vec = 1:3)
 

R/analysis/test.stan.R

 ### test stan
-source('R/analysis/lmer.run.R')
 source('R/analysis/stan.run.R')
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 1000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = TRUE)
+
+system.time(res <- run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 1000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+ 
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 10000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = TRUE)
+
+system.time(res <- run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 10000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 20000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = TRUE)
+
+system.time(res <- run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 20000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+
+
+
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 5000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = TRUE)
+
+system.time(res <- run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 5000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 5000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 2000, warmup = 200, chains = 2, chain_id = '1',
+                                     init.TF = TRUE)
+
+system.time(res <- run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
+                                     trait = 'SLA',type.filling='species', sample.size = 5000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 2000, warmup = 200, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+
 
-res <- run.stan(model.file = model.files.stan.Tf.1[4], trait = 'SLA',
-                min.obs = 10, sample.size = 1000,
-                type.filling = 'species', cat.TF = FALSE,
-                fname = 'data.all.no.log.csv')
 saveRDS(res, 'stan.res.rds')
 res <- readRDS('output/stan/all.no.log/SLA/species/LOGLIN.ER.AD.norandom/results.stan.rds')
 res

R/process.data/process-fun.R

@@ -696,48 +696,70 @@ return(data.all)
 
 ## function to reformat data with traits centred and standardized
 
-fun.reformat.traits <- function(data, traits, mean.traits, sd.traits){
-  for (trait in traits){
-   trait.f <- paste(trait, 'focal', sep ='.')
-   data[[trait.f]] <-  (data[[trait.f]]- mean.traits[trait])/sd.traits[trait]
-   trait.CWM <-  paste(trait, 'CWM.fill', sep = '.')
-   data[[trait.CWM]] <-  (data[[trait.CWM]]- mean.traits[trait])/sd.traits[trait]
-   trait.abs <-  paste(trait, 'abs.CWM.fill', sep = '.')
-   data[[trait.abs]] <-  (data[[trait.CWM]])/sd.traits[trait]
-  }
+
+fun.reformat.trait <- function(data,trait){
+##
+  trait.CWM.sd.n <-  paste(trait, 'CWM.fill', sep = '.')
+##
+  trait.CWM.sd.e <- (parse(text =
+                           paste('(',trait,'.CWM.fill',
+                                 ' - mean(', trait,
+                                 '.focal, na.rm =TRUE))/sd(',trait,
+                                 '.focal, na.rm =TRUE)' ,
+                                 sep = '')))
+  data[, c(trait.CWM.sd.n) := eval(trait.CWM.sd.e) ]
+
+  ##
+  trait.abs.CWM.sd.n <-  paste(trait, 'abs.CWM.fill', sep = '.')
+##
+  trait.abs.CWM.sd.e <- (parse(text =
+                           paste('(',trait,'.abs.CWM.fill',
+                                 ')/sd(',trait,
+                                 '.focal, na.rm =TRUE)' ,
+                                 sep = '')))
+  data[, c(trait.abs.CWM.sd.n) := eval(trait.abs.CWM.sd.e) ]
 return(data)
 }
 
-fun.reformat.traits.CAT <- function(data, traits, mean.traits, sd.traits){
-  for (trait in traits){
-    for(cat in c('A_EV', 'A_D', 'C')){
-    trait.CWM <-  paste(trait, 'CWM.fill', cat, sep = '.')
-    data[[trait.CWM]] <-  (data[[trait.CWM]]- mean.traits[trait])/sd.traits[trait]
-    trait.abs <-  paste(trait, 'abs.CWM.fill', cat, sep = '.')
-    data[[trait.abs]] <-  (data[[trait.CWM]])/sd.traits[trait]
-    } 
-  }
+fun.reformat.trait.CAT <- function(data, trait, cat){
+##
+  trait.CWM.sd.n <-  paste(trait, 'CWM.fill', cat, sep = '.')
+##
+  trait.CWM.sd.e <- (parse(text =
+                           paste('(',trait,'.CWM.fill.', cat,
+                                 ' - mean(', trait,
+                                 '.focal, na.rm =TRUE))/sd(',trait,
+                                 '.focal, na.rm =TRUE)' ,
+                                 sep = '')))
+  data[, c(trait.CWM.sd.n) := eval(trait.CWM.sd.e) ]
+
+  ##
+  trait.abs.CWM.sd.n <-  paste(trait, 'abs.CWM.fill', cat, sep = '.')
+##
+  trait.abs.CWM.sd.e <- (parse(text =
+                           paste('(',trait,'.abs.CWM.fill.', cat,
+                                 ')/sd(',trait,
+                                 '.focal, na.rm =TRUE)' ,
+                                 sep = '')))
+  data[, c(trait.abs.CWM.sd.n) := eval(trait.abs.CWM.sd.e) ]
 return(data)
 }
 
-
-fun.standardized.traits <-  function(data, 
+fun.standardized.traits <-  function(data,
                                      traits = c('SLA', 'Leaf.N',
                                           'Wood.density', 'Max.height',
-                                          'Seed.mass')){
-  ## Compute Std deviation of each traits
-  mean.traits <- unlist(lapply(paste(traits, 'focal', sep = '.'),
-                        function(var, data) mean(data[[var]], na.rm = TRUE),
-                        data))
-  sd.traits <- unlist(lapply(paste(traits, 'focal', sep = '.'),
-                        function(var, data) sd(data[[var]], na.rm = TRUE),
-                        data))
-  names(mean.traits) <-  names(sd.traits) <-  traits
-  ## Reformat to account for traits std
-  data <- fun.reformat.traits(data, traits, mean.traits, sd.traits)
-  data <- fun.reformat.traits.CAT(data, traits, mean.traits, sd.traits)
+                                          'Seed.mass'),
+                                     cat.vec = c('A_EV', 'A_D', 'C')){
+
+for(trait in traits){
+   data <- fun.reformat.trait(data, trait)
+   for (cat in cat.vec){
+   data <- fun.reformat.trait.CAT(data, trait, cat)
+     }
+   print(data)
+   }
 return(data)
-}    
+}
 
 
 
@@ -762,8 +784,8 @@ fun.reform.data.and.remove.outlier <- function(data.all,
     data.all[ , cluster := paste(ecocode, cluster)]
     data.all[ , tree.id := paste(ecocode, tree.id)]
     data.all[ , obs.id := paste(ecocode, obs.id)]
-    data.all <-  as.data.frame(data.all)
     if(std.traits.TF) fun.standardized.traits(data.all)
+    data.all <-  as.data.frame(data.all)
     return(data.all)
 }
 

docs/paper/Makefile

+TARGETS = $(subst md,pdf,$(shell ls *.md))
+
+all: outline.pdf
+
+outline.pdf: outline.md include.tex 
+	pandoc $< --csl=journal-of-applied-ecology.csl --filter pandoc-citeproc --bibliography=refs.bib --template=include.tex --variable mainfont="Times New Roman" --variable sansfont=Arial --variable fontsize=12pt --latex-engine=xelatex -o $@
+
+
+clean:
+	rm -f *.pdf
+# 

docs/paper/include.tex

+% THIS FILE TAKEN FROM PANDOC DEMOS PAGE:http://johnmacfarlane.net/pandoc/demos.html,http://johnmacfarlane.net/pandoc/demo/mytemplate.tex
+
+\documentclass[$if(fontsize)$$fontsize$,$endif$$if(lang)$$lang$,$endif$, a4paper]{$documentclass$}
+
+\RequirePackage[hmargin=2cm,vmargin=2cm]{geometry}
+
+\usepackage[T1]{fontenc}
+\usepackage{lmodern}
+\usepackage{amssymb,amsmath}
+\usepackage{ifxetex,ifluatex}
+\usepackage{fixltx2e} % provides \textsubscript
+% use microtype if available
+\IfFileExists{microtype.sty}{\usepackage{microtype}}{}
+% use upquote if available, for straight quotes in verbatim environments
+\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
+\ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex
+  \usepackage[utf8]{inputenc}
+
+$if(euro)$
+  \usepackage{eurosym}
+$endif$
+\else % if luatex or xelatex
+  \usepackage{fontspec}
+  \ifxetex
+    \usepackage{xltxtra,xunicode}
+  \fi
+  \defaultfontfeatures{Mapping=tex-text,Scale=MatchLowercase}
+  \newcommand{\euro}{€}
+$if(mainfont)$
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+$endif$
+$if(sansfont)$
+    \setsansfont{$sansfont$}
+$endif$
+$if(monofont)$
+    \setmonofont{$monofont$}
+$endif$
+$if(mathfont)$
+    \setmathfont{$mathfont$}
+$endif$
+\fi
+
+% Fancy HEADER
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\pagenumbering{arabic}
+\lhead{\itshape{\nouppercase{\leftmark}}}
+\chead{}
+\rhead{\thepage}
+%\lfoot{v $version$}
+\cfoot{}
+%\rfoot{\thepage}
+
+$if(geometry)$
+\usepackage[$for(geometry)$$geometry$$sep$,$endfor$]{geometry}
+$endif$
+$if(natbib)$
+\usepackage{natbib}
+\bibliographystyle{plainnat}
+$endif$
+$if(biblatex)$
+\usepackage{biblatex}
+$if(biblio-files)$
+\bibliography{$biblio-files$}
+$endif$
+$endif$
+$if(listings)$
+\usepackage{listings}
+$endif$
+$if(lhs)$
+\lstnewenvironment{code}{\lstset{language=Haskell,basicstyle=\small\ttfamily}}{}
+$endif$
+$if(highlighting-macros)$
+$highlighting-macros$
+$endif$
+$if(verbatim-in-note)$
+\usepackage{fancyvrb}
+$endif$
+$if(tables)$
+\usepackage{longtable}
+$endif$
+$if(graphics)$
+\usepackage{graphicx}
+% We will generate all images so they have a width \maxwidth. This means
+% that they will get their normal width if they fit onto the page, but
+% are scaled down if they would overflow the margins.
+\makeatletter
+\def\maxwidth{\ifdim\Gin@nat@width>\linewidth\linewidth
+\else\Gin@nat@width\fi}
+\makeatother
+\let\Oldincludegraphics\includegraphics
+\renewcommand{\includegraphics}[1]{\Oldincludegraphics[width=\maxwidth]{#1}}
+$endif$
+\ifxetex
+  \usepackage[setpagesize=false, % page size defined by xetex
+              unicode=false, % unicode breaks when used with xetex
+              xetex]{hyperref}
+\else
+  \usepackage[unicode=true]{hyperref}
+\fi
+\hypersetup{breaklinks=true,
+            bookmarks=true,
+            pdfauthor={$author-meta$},
+            pdftitle={$title-meta$},
+            colorlinks=true,
+            urlcolor=$if(urlcolor)$$urlcolor$$else$blue$endif$,
+            linkcolor=$if(linkcolor)$$linkcolor$$else$magenta$endif$,
+            pdfborder={0 0 0}}
+$if(links-as-notes)$
+% Make links footnotes instead of hotlinks:
+\renewcommand{\href}[2]{#2\footnote{\url{#1}}}
+$endif$
+$if(strikeout)$
+\usepackage[normalem]{ulem}
+% avoid problems with \sout in headers with hyperref:
+\pdfstringdefDisableCommands{\renewcommand{\sout}{}}
+$endif$
+\setlength{\parindent}{0pt}
+\setlength{\parskip}{6pt plus 2pt minus 1pt}
+\setlength{\emergencystretch}{3em}  % prevent overfull lines
+$if(numbersections)$
+$else$
+\setcounter{secnumdepth}{0}
+$endif$
+$if(verbatim-in-note)$
+\VerbatimFootnotes % allows verbatim text in footnotes
+$endif$
+$if(lang)$
+\ifxetex
+  \usepackage{polyglossia}
+  \setmainlanguage{$mainlang$}
+\else
+  \usepackage[$lang$]{babel}
+\fi
+$endif$
+$for(header-includes)$
+$header-includes$
+$endfor$
+
+$if(title)$
+\title{$title$}
+$endif$
+\author{$for(author)$$author$$sep$ \and $endfor$}
+\date{$date$}
+
+\begin{document}
+$if(title)$
+\maketitle
+$endif$
+
+$for(include-before)$
+$include-before$
+
+$endfor$
+$if(toc)$
+{
+\hypersetup{linkcolor=black}
+\setcounter{tocdepth}{$toc-depth$}
+\tableofcontents
+}
+$endif$
+$body$
+
+$if(natbib)$
+$if(biblio-files)$
+$if(biblio-title)$
+$if(book-class)$
+\renewcommand\bibname{$biblio-title$}
+$else$
+\renewcommand\refname{$biblio-title$}
+$endif$
+$endif$
+\bibliography{$biblio-files$}
+
+$endif$
+$endif$
+$if(biblatex)$
+\printbibliography$if(biblio-title)$[title=$biblio-title$]$endif$
+
+$endif$
+$for(include-after)$
+$include-after$
+
+$endfor$
+\end{document}

docs/paper/journal-of-applied-ecology.csl

+<?xml version="1.0" encoding="utf-8"?>
+<style xmlns="http://purl.org/net/xbiblio/csl" class="in-text" version="1.0" demote-non-dropping-particle="sort-only" default-locale="en-GB">
+  <info>
+    <title>Journal of Applied Ecology</title>
+    <id>http://www.zotero.org/styles/journal-of-applied-ecology</id>
+    <link href="http://www.zotero.org/styles/journal-of-applied-ecology" rel="self"/>
+    <link href="http://www.journalofappliedecology.org/view/0/authorGuideline.html" rel="documentation"/>
+    <author>
+      <name>Johan Asplund</name>
+      <email>asplundj@gmail.com</email>
+    </author>
+    <contributor>
+      <name>Sebastian Karcher</name>
+    </contributor>
+    <category citation-format="author-date"/>
+    <category field="biology"/>
+    <issn>0021-8901</issn>
+    <eissn>1365-2664</eissn>
+    <updated>2013-09-08T00:04:27+00:00</updated>
+    <rights license="http://creativecommons.org/licenses/by-sa/3.0/">This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License</rights>
+  </info>