fixed problem in initial value

Makefile

@@ -43,12 +43,12 @@ $(D2)/TRY/data.TRY.std.rds:
 GLOBAL: $(D3)/Done.txt
 
 $(D3)/Done.txt: scripts/process.data/remove.out.R scripts/process.data/summarise.data.R $(D3)/Done.t.txt
-	Rscript $<
-	Rscript scripts/process.data/summarise.data.R
-	Rscript scripts/process.data/plot.data.all.R 
+	R CMD BATCH $<
+	R CMD BATCH scripts/process.data/summarise.data.R
+	R CMD BATCH scripts/process.data/plot.data.all.R 
 
 $(D3)/Done.t.txt: scripts/process.data/merge.all.processed.data.R 
-	Rscript $< 
+	R CMD BATCH $< 
 
 #-------------------------------------------------------
 TEST.TREE: $(D4)/Done.txt tree.all.sites

R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.fixed.R

@@ -12,38 +12,38 @@ load.model <- function(){
              init.fun = function(chain_id= 1, list){
                step <- (chain_id-1)/10
                init <-
-                 list(intercept = 0+step,
-                      intercept_species = rnorm(list$N_species, 0, 0.5+step),
-                      param_sumBn_species = rnorm(list$N_species, 0, 0.5+step),
-                      param_logD_species = rnorm(list$N_species, 0, 0.5+step),
-                      intercept_plot = rnorm(list$N_plot, 0, 0.5 +step),
-                      intercept_set = rnorm(list$N_set, 0, 0.5 +step),
-                      mean_logD = 2/3+step,
-                      param_Tf = 2/3+step,
-                      param_sumBn = 2/3+step,
-                      param_sumTfBn = 2/3+step,
-                      param_sumTnBn = 2/3+step,
-                      param_sumTnTfBn_abs= 2/3+step,
-                      sigma_inter_species = 0.5 +step,
-                      sigma_inter_plot = 0.5 + +step,
-                      sigma_inter_set = 0.5 + +step,
+                 list(intercept = 0 + step,
+                      intercept_species = rnorm(list$N_species, 0, 0.3 + step),
+                      param_sumBn_species = rnorm(list$N_species, 0, 0.1 + step),
+                      param_logD_species = rnorm(list$N_species, 0, 0.3 + step),
+                      intercept_plot = rnorm(list$N_plot, 0, 0.3 + step),
+                      intercept_set = rnorm(list$N_set, 0, 0.1 + step),
+                      mean_logD = 2/3 + step,
+                      param_Tf = -0.1 + step,
+                      param_sumBn = -0.1 + step,
+                      param_sumTfBn = -0.1 + step,
+                      param_sumTnBn = -0.1 + step,
+                      param_sumTnTfBn_abs= -0.1 + step,
+                      sigma_inter_species = 0.5 + step,
+                      sigma_inter_plot = 0.3 + step,
+                      sigma_inter_set = 0.1 + step,
                       sigma = 0.5 +step,
-                      sigma_sumBn_species = 0.5 +step,
-                      sigma_logD_species = 0.5 +step)
+                      sigma_sumBn_species = 0.1 +step,
+                      sigma_logD_species = 0.3 +step)
                return(init)
               },
-             
+
  	     stan = "
 data {
     int<lower=0> N_indiv;
     int<lower=0> N_species;
     int<lower=0> N_plot;
     int<lower=0> N_set;
-  
+
     int species_id[N_indiv];
     int plot_id[N_indiv];
     int set_id[N_indiv];
-  
+
     real logG[N_indiv];
     real logD[N_indiv];
     real sumBn[N_indiv];
@@ -93,27 +93,31 @@ transformed parameters {
 
 model {
   # constants for prior
-  ## real sigma0;
-
-################################################################################
-######################## growth model with STAN #############################
+  real sigma0;
+  sigma0 <- 10;
 
   ###############################################
   ########### Hierarchical parameters ########
   ### species random param
-    param_logD_species ~ normal(0,sigma_logD_species);
-    param_sumBn_species ~ normal(0,sigma_sumBn_species);
-    intercept_species ~ normal(0, sigma_inter_species);
+  param_logD_species ~ normal(0,sigma_logD_species);
+  param_sumBn_species ~ normal(0,sigma_sumBn_species);
+  intercept_species ~ normal(0, sigma_inter_species);
   ### plot random param
-    intercept_plot ~ normal(0,sigma_inter_plot);
+  intercept_plot ~ normal(0,sigma_inter_plot);
   ### set random effect
-     intercept_set ~ normal(0, sigma_inter_set);
- ### biomes fixed param
-    param_Tf ~ normal(0,  sigma0);
-    param_sumBn ~ normal(0,sigma0);
-    param_sumTfBn ~ normal(0 ,sigma0);
-    param_sumTnBn ~ normal(0 ,sigma0);
-    param_sumTnTfBn_abs ~ normal(0 , sigma0);
+  intercept_set ~ normal(0, sigma_inter_set);
+
+  ###############################################
+  ########### Non-Hierarchical parameters ########
+  # slope and intercept
+  intercept ~ normal(0,sigma0);
+  mean_logD ~ normal(0,sigma0);
+  ### biomes fixed param
+  param_Tf ~ normal(0,  sigma0);
+  param_sumBn ~ normal(0,sigma0);
+  param_sumTfBn ~ normal(0 ,sigma0);
+  param_sumTnBn ~ normal(0 ,sigma0);
+  param_sumTnTfBn_abs ~ normal(0 , sigma0);
 
 
   ###############################################

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

@@ -13,26 +13,26 @@ load.model <- function(){
                step <- (chain_id-1)/10
                init <-
                  list(intercept = 0+step,
-                      intercept_species = rnorm(list$N_species, 0, 0.5+step),
-                      param_sumBn_species = rnorm(list$N_species, 0, 0.5+step),
-                      param_logD_species = rnorm(list$N_species, 0, 0.5+step),
-                      intercept_plot = rnorm(list$N_plot, 0, 0.5 +step),
-                      intercept_set = rnorm(list$N_set, 0, 0.5 +step),
-                      mean_logD = 2/3+step,
-                      param_Tf_biomes= rep(2/3+step, list$N_biomes),
-                      param_sumBn_biomes= rep(2/3+step, list$N_biomes),
-                      param_sumTfBn_biomes= rep(2/3+step, list$N_biomes),
-                      param_sumTnBn_biomes= rep(2/3+step, list$N_biomes),
-                      param_sumTnTfBn_abs_biomes= rep(2/3+step, list$N_biomes),
-                      sigma_inter_species = 0.5 +step,
-                      sigma_inter_plot = 0.5 + +step,
-                      sigma_inter_set = 0.5 + +step,
+                      intercept_species = rnorm(list$N_species, 0, 0.3+step),
+                      param_sumBn_species = rnorm(list$N_species, 0, 0.1+step),
+                      param_logD_species = rnorm(list$N_species, 0, 0.3+step),
+                      intercept_plot = rnorm(list$N_plot, 0, 0.3 +step),
+                      intercept_set = rnorm(list$N_set, 0, 0.1 +step),
+                      mean_logD = (2/3 - 0.1) +step,
+                      param_Tf_biomes= rep(-0.1+step, list$N_biomes),
+                      param_sumBn_biomes= rep(-0.1+step, list$N_biomes),
+                      param_sumTfBn_biomes= rep(-0.1+step, list$N_biomes),
+                      param_sumTnBn_biomes= rep(-0.1+step, list$N_biomes),
+                      param_sumTnTfBn_abs_biomes= rep(-0.1+step, list$N_biomes),
+                      sigma_inter_species = 0.3 +step,
+                      sigma_inter_plot = 0.3 + +step,
+                      sigma_inter_set = 0.1 + +step,
                       sigma = 0.5 +step,
-                      sigma_sumBn_species = 0.5 +step,
-                      sigma_logD_species = 0.5 +step)
+                      sigma_sumBn_species = 0.1 +step,
+                      sigma_logD_species = 0.3 +step)
                return(init)
               },
-             
+
  	     stan = "
 data {
     int<lower=0> N_indiv;
@@ -40,12 +40,12 @@ data {
     int<lower=0> N_plot;
     int<lower=0> N_biomes;
     int<lower=0> N_set;
-  
+
     int species_id[N_indiv];
     int plot_id[N_indiv];
     int biomes_id[N_indiv];
     int set_id[N_indiv];
-  
+
     real logG[N_indiv];
     real logD[N_indiv];
     real sumBn[N_indiv];
@@ -80,12 +80,12 @@ transformed parameters {
     vector[N_indiv] theo_g;
 
     for (i in 1:N_indiv){
-        theo_g[i] <- intercept + intercept_species[species_id[i]]
+       theo_g[i] <- intercept + intercept_species[species_id[i]]
           + intercept_plot[plot_id[i]]
           + intercept_set[set_id[i]]
           + (mean_logD + param_logD_species[species_id[i]])*logD[i]
           + (param_Tf_biomes[biomes_id[i]])*Tf[i]
-          + ( param_sumBn_biomes[biomes_id[i]] +  param_sumBn_species[species_id[i]])*sumBn[i]
+          + ( param_sumBn_biomes[biomes_id[i]] + param_sumBn_species[species_id[i]])*sumBn[i]
           + (param_sumTfBn_biomes[biomes_id[i]])*sumTfBn[i]
           + (param_sumTnBn_biomes[biomes_id[i]])*sumTnBn[i]
           + (param_sumTnTfBn_abs_biomes[biomes_id[i]])*sumTnTfBn_abs[i]
@@ -94,48 +94,36 @@ transformed parameters {
 }
 
 model {
-  # constants for prior
-  ## real sigma0;
-
-################################################################################
-######################## growth model with STAN #############################
+    # constants for prior
+    real sigma0;
+    sigma0 <- 10;
 
-  ###############################################
-  ########### Hierarchical parameters ########
-  ### species random param
+    ###############################################
+    ########### Hierarchical parameters ########
+    ### species random param
     param_logD_species ~ normal(0,sigma_logD_species);
     param_sumBn_species ~ normal(0,sigma_sumBn_species);
     intercept_species ~ normal(0, sigma_inter_species);
-  ### plot random param
+    ### plot random param
     intercept_plot ~ normal(0,sigma_inter_plot);
-  ### set random effect
-     intercept_set ~ normal(0, sigma_inter_set);
- ### biomes fixed param
-    ## param_Tf_biomes ~ normal(0,  sigma0);
-    ## param_sumBn_biomes ~ normal(0,sigma0);
-    ## param_sumTfBn_biomes ~ normal(0 ,sigma0);
-    ## param_sumTnBn_biomes ~ normal(0 ,sigma0);
-    ## param_sumTnTfBn_abs_biomes ~ normal(0 , sigma0);
+    ### set random effect
+    intercept_set ~ normal(0, sigma_inter_set);
 
-  ###############################################
-  ########### Non-Hierarchical parameters ########
-  # constant for prior
-  ## sigma0 <- 10;
-  # slope and intercept
-  ## intercept ~ normal(0,sigma0);
-  ## mean_logD ~ normal(0,sigma0);
-  ## # sigma
-  ## sigma_inter_species~ uniform(0,6);
-  ## sigma_inter_set~ uniform(0,6);
-  ## sigma_inter_plot~ uniform(0,6);
-  ## sigma_inter_tree~ uniform(0,6);
-  ## sigma_logD_species~ uniform(0,6);
-  ## sigma_sumBn_species~ uniform(0,6);
-  ## sigma~ uniform(0,6);
+    ###############################################
+    ########### Non-Hierarchical parameters ########
+    # slope and intercept
+    intercept ~ normal(0,sigma0);
+    mean_logD ~ normal(0,sigma0);
+    ### biomes fixed param
+    param_Tf_biomes ~ normal(0,  sigma0);
+    param_sumBn_biomes ~ normal(0,sigma0);
+    param_sumTfBn_biomes ~ normal(0 ,sigma0);
+    param_sumTnBn_biomes ~ normal(0 ,sigma0);
+    param_sumTnTfBn_abs_biomes ~ normal(0 , sigma0);
 
-  ###############################################
-  ############ Likelihood ###################
-  logG ~ normal(theo_g,sigma);
+    ###############################################
+    ############ Likelihood ###################
+    logG ~ normal(theo_g,sigma);
 }
 ")
 }

R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.fixed.oneset.R

@@ -37,10 +37,10 @@ data {
     int<lower=0> N_indiv;
     int<lower=0> N_species;
     int<lower=0> N_plot;
-  
+
     int species_id[N_indiv];
     int plot_id[N_indiv];
-  
+
     real logG[N_indiv];
     real logD[N_indiv];
     real sumBn[N_indiv];
@@ -90,28 +90,27 @@ model {
    real sigma0;
   sigma0 <- 10;
 
-################################################################################
-######################## growth model with STAN #############################
-
-  ###############################################
-  ########### Hierarchical parameters ########
-  ### species random param
+    ###############################################
+    ########### Hierarchical parameters ########
+    ### species random param
     param_logD_species ~ normal(0,sigma_logD_species);
     param_sumBn_species ~ normal(0,sigma_sumBn_species);
     intercept_species ~ normal(0, sigma_inter_species);
-  ### plot random param
+    ### plot random param
     intercept_plot ~ normal(0,sigma_inter_plot);
- ### biomes fixed param
+    ### fixed param
+    intercept ~ normal(0,sigma0);
+    mean_logD ~ normal(0,sigma0);
+    ### biomes fixed param
     param_Tf ~ normal(0,  sigma0);
     param_sumBn ~ normal(0,sigma0);
     param_sumTfBn ~ normal(0 ,sigma0);
     param_sumTnBn ~ normal(0 ,sigma0);
     param_sumTnTfBn_abs ~ normal(0 , sigma0);
 
-
-  ###############################################
-  ############ Likelihood ###################
-  logG ~ normal(theo_g,sigma);
+    ###############################################
+    ############ Likelihood ###################
+    logG ~ normal(theo_g,sigma);
 }
 ")
 }

R/analysis/stan.run.R

@@ -26,16 +26,16 @@ fun.call.stan.parallel.and.save <- function(stan.model, list.stan, path.out,
                                    var.sample){
  start <-  Sys.time()
  set_cppo(mode = "fast")
- inits <- stan.model$init.fun(chain_id,
-                            list.stan)
+ inits <- list(stan.model$init.fun(chains,
+                            list.stan))
  stan.output <- stan(model_code = stan.model$stan,
                      data = list.stan,
                      pars = stan.model$pars,
-                     init = 'random',
+                     init = inits,
                      iter = iter,
                      warmup = warmup,
                      chains = chains,
-                     chain_id = chain_id,
+                     ## chain_id = chain_id,
                      thin = thin,
                      verbose = FALSE)
  end <- Sys.time()
@@ -57,7 +57,7 @@ fun.call.stan.and.save <- function(stan.model, list.stan, path.out,
  stan.output <- stan(model_code = stan.model$stan,
                      data = list.stan,
                      pars = stan.model$pars,
-                     init = 'random',
+                     init = inits,
                      iter = iter,
                      warmup = warmup,
                      chains = chains,

R/analysis/test.stan.R

 ### test stan
 
-## I changed the coding of the random effect with norm(0,10*sigma it seems more slow!
+## I changed the coding of the random effect
+## with norm(0,10*sigma it seems more slow!
 
 ## PROBLEM WITH STANDARDIZED VARIABLE DO I NEED TO INCLUDE AN INTERCEPT??
 ## VERY BAD CONVERGENCE ???
@@ -12,11 +13,12 @@ source('R/analysis/stan.run.R')
 
 ### TEST simple model on France only
 
-  df.lmer <- load.data.for.lmer(trait = 'SLA',fname = 'data.all.no.log.rds', cat.TF = FALSE,
-                                sample.size = NA,
-                                var.sample = NA,
-                                sample.vec.TF. = FALSE,
-                                select.set. = 'France')
+  df.lmer <- load.data.for.lmer(trait = 'SLA',
+                                fname = 'data.all.no.log.rds',
+                                cat.TF = FALSE,
+                                sample.size = 500,
+                                var.sample = 'ecocode',
+                                sample.vec.TF. = FALSE)
 
   stan.list <- fun.turn.in.list.for.jags.stan(df.lmer,
                                               cat.TF = FALSE)
@@ -26,8 +28,12 @@ source('R/analysis/model.stan/model.stan.LOGLIN.size.fixed.R', local = TRUE)
 
 fun.init.stan <- function(chain_id= 1, stan.list){
 init <- list(intercept = 0+(chain_id-1)/10,
-             intercept_species = rnorm(stan.list$N_species, 0, 0.5+(chain_id-1)/10),
-             intercept_plot = rnorm(stan.list$N_plot, 0, 0.5 +(chain_id-1)/10),
+             intercept_species = rnorm(stan.list$N_species,
+                                       0,
+                                       0.5+(chain_id-1)/10),
+             intercept_plot = rnorm(stan.list$N_plot,
+                                    0,
+                                    0.5 +(chain_id-1)/10),
              mean_logD = 2/3+(chain_id-1)/10,
              sigma_inter_species = 0.5 +(chain_id-1)/10,
              sigma_inter_plot = 0.5 + +(chain_id-1)/10,
@@ -48,33 +54,12 @@ system.time( stan.output <- stan(model_code = model$stan,
                      chains = 3,
                      verbose = FALSE))
 
-source('R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.fixed.oneset.R', local = TRUE)
+source('R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.fixed.biomes.R', local = TRUE)
     model <- load.model()
 
-fun.init.stan <- function(chain_id= 1, stan.list){
-    step <- (chain_id-1)/10
-init <- list(intercept = 0+step,
-             intercept_species = rnorm(stan.list$N_species, 0, 0.5+step),
-             param_sumBn_species = rnorm(stan.list$N_species, 0, 0.5+step),
-             param_logD_species = rnorm(stan.list$N_species, 0, 0.5+step),
-             intercept_plot = rnorm(stan.list$N_plot, 0, 0.5 +step),
-             mean_logD = 2/3+step,
-             param_Tf = 2/3+step,
-             param_sumBn = 2/3+step,
-             param_sumTfBn = 2/3+step,
-             param_sumTnBn = 2/3+step,
-             param_sumTnTfBn_abs= 2/3+step,
-             sigma_inter_species = 0.5 +step,
-             sigma_inter_plot = 0.5 + +step,
-             sigma = 0.5 +step,
-             sigma_sumBn_species = 0.5 +step,
-             sigma_logD_species = 0.5 +step
-             )
-}
-
-inits <- list(fun.init.stan(1, stan.list),
-              fun.init.stan(2, stan.list),
-              fun.init.stan(3, stan.list))
+inits <- list(model$init.fun(1, stan.list),
+              model$init.fun(2, stan.list),
+              model$init.fun(3, stan.list))
 
 
 library(rstan)
@@ -96,8 +81,35 @@ pairs(stan.output)
 
 
 library(ggmcmc)
-S <- ggs(stan.output)
-extract(stan.output, permuted = FALSE, inc_warmup=FALSE))
+S <- ggs(stan.output2)
+ggs_crosscorrelation(S)
+ggs_crosscorrelation(S, absolute_scale = FALSE)
+ggs_density(S)
+ggs_traceplot(S)
+ggs_running(S)
+ggs_autocorrelation(S)
+ggs_Rhat(S)
+ggs_geweke(S)
+ggs_caterpillar(S)
+
+#######
+## read results of simul on cluster
+source("R/analysis/stan.output-fun.R")
+
+stan.out.clust <- fun.merge.chain(
+  path.out = "output/stan/all.no.log/SLA/species/LOGLIN.ER.AD.Tf.fixed.biomes/",
+  var.sample = 'ecocode', chains.vec = 1:3)
+
+stan.out.clust
+plot(stan.out.clust)
+pairs(stan.out.clust)
+traceplot(stan.out.clust, ask = TRUE)
+
+## base on traceplot the initial value seems to be
+## a big problem for the lack of convergence
+library(ggmcmc)
+S <- ggs(stan.out.clust)
+ggs_crosscorrelation(S)
 ggs_crosscorrelation(S, absolute_scale = FALSE)
 ggs_density(S)
 ggs_traceplot(S)
@@ -108,3 +120,23 @@ ggs_geweke(S)
 ggs_caterpillar(S)
 
 
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1, run.stan, 'SLA',
+                                     type.filling='species', sample.size = 10000, var.sample = 'ecocode',
+                                     iter = 1000, warmup = 500, chains = 1, chain_id = 1,
+                                     init.TF = FALSE)
+
+
+run.multiple.model.for.set.one.trait(model.files.stan.Tf.1, run.stan, 'SLA',
+                                     type.filling='species', sample.size = 1000, var.sample = 'ecocode',
+                                     iter = 1000, warmup = 500, chains = 3, parallel.TF = FALSE,
+                                     init.TF = FALSE)
+out.stan3 <- readRDS('output/stan/all.no.log/SLA/species/LOGLIN.ER.AD.Tf.fixed.biomes/ecocode.results.stan.rds')
+traceplot(out.stan3, ask = TRUE)
+plot(out.stan3)
+pairs(out.stan3)
+
+library(ggmcmc)
+S <- ggs(out.stan3)
+ggs_crosscorrelation(S)
+ggs_crosscorrelation(S, absolute_scale = FALSE)
+ggs_density(S, family = c('param_sumBn_biomes'))

R/process.data/test.tree.CWM-fun.R

@@ -8,7 +8,9 @@ load.processed.data <- function(path, file.name = "data.tree.tot.no.std.csv"){
 require(data.table)
     fname <- file.path(path, file.name )
     if(file.exists(fname)){
+        cat('loading file', path, file.name)
         data <- fread(fname, stringsAsFactors = FALSE)
+        print(warnings())
       return(data)
   }else{return(NULL)}
 }

launch.cluster/launch_all_data.lmer.bash

@@ -6,11 +6,11 @@ mkdir -p trait.workshop
 
 for trait in "'SLA'" "'Leaf.N'" "'Wood.density'" "'Max.height'" "'Seed.mass'"; do
 
-  	echo "/usr/local/R/R-3.0.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); load.and.save.data.for.lmer($trait);print('done')\"" > trait.workshop/data1$trait.sh
+  	echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); load.and.save.data.for.lmer($trait);print('done')\"" > trait.workshop/data1$trait.sh
 	qsub trait.workshop/data1$trait.sh -l nodes=1:ppn=1,mem=8gb -N "data1$trait" -q opt32G -j oe
 
 
-  	echo "/usr/local/R/R-3.0.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); load.and.save.data.for.lmer($trait, fname = 'data.all.log.rds');print('done')\"" > trait.workshop/data3$trait.sh
+  	echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/lmer.run.R'); load.and.save.data.for.lmer($trait, fname = 'data.all.log.rds');print('done')\"" > trait.workshop/data3$trait.sh
 	qsub trait.workshop/data3$trait.sh -l nodes=1:ppn=1,mem=8gb -N "data3$trait" -q opt32G -j oe
 
 done

launch.cluster/launch_all_jags.init.bash

+#!/bin/bash
+# Georges Kunstler 14/05/2014
+# read one variable
+export LD_LIBRARY_PATH=/usr/lib64/R/library
+
+mkdir -p trait.workshop
+
+iter=50000
+warmup=5000
+thin=50
+# test parameter
+nbargs=$#
+echo "number of arguments="$nbargs
+if [ $nbargs -ne 1 ]
+then
+  echo "need one and only one argument"
+  echo " usage :"
+  echo " ./launch_all_lmer.sh  sample.size"
+  exit 100
+fi
+
+
+samplesize=$1
+# 
+for trait in "'SLA'" "'Wood.density'" "'Max.height'" "'Seed.mass'" "'Leaf.N'"; do
+
+  	echo "/usr/local/R/R-3.1.1/bin/Rscript -e \"source('R/analysis/jags.run.R'); run.multiple.model.for.set.one.trait(c(model.files.jags.Tf.1), run.jags,$trait,type.filling='species', sample.size = $samplesize, var.sample = 'ecocode.id',iter = $iter, warmup = $warmup, chains = 3, thin = $thin, init.TF = TRUE);print('done')\"" > trait.workshop/speciesjags${trait}.sh
+	qsub trait.workshop/speciesjags${trait}.sh -l nodes=1:ppn=1,mem=8gb -N "jags${trait}" -q opt32G -j oe
+
+
+
+done
+

launch.cluster/launch_all_stan.bash

@@ -5,8 +5,8 @@ export LD_LIBRARY_PATH=/usr/lib64/R/library
 
 mkdir -p trait.workshop
 
-iter=1000
-warmup=500
+iter=2000
+warmup=1000
 # test parameter
 nbargs=$#
 echo "number of arguments="$nbargs

scripts/format.data/BCI.R

@@ -5,18 +5,19 @@
 rm(list = ls())
 source("R/format.data/format-fun.R")
 dir.create("output/formatted/BCI", recursive = TRUE, showWarnings = FALSE)
+
 library(reshape, quietly = TRUE)
 ### READ DATA read individuals tree data Requires careful formatting of 7 census
 ## datasets The raw data is such that, once a tree dies in census X, then it no
 ## longer exists in census X+1, X+2 etc...
 data.bci1 <- read.csv("data/raw/BCI/stem.data/BCI.all.plots.census.allstems.1.csv",
                       header = TRUE,
-    stringsAsFactors = FALSE)
+                      stringsAsFactors = FALSE)
 
 
 
-    print(table(data.bci1$Stem))
-    print(sum(is.na(data.bci1$Stem)))
+print(table(data.bci1$Stem))
+print(sum(is.na(data.bci1$Stem)))
 data.bci1$Date1 <- data.bci1$Date; data.bci1$Date <- NULL
 data.bci1$DBH1 <- data.bci1$DBH; data.bci1$DBH <- NULL
 data.bci1$HOM1 <- data.bci1$HOM; data.bci1$HOM <- NULL

scripts/format.data/Sweden.R

 #!/usr/bin/env Rscript