include fixed effect biomes code ready to be sent for jags and comparison between stan and jags

R/analysis/jags.run.R

@@ -10,16 +10,18 @@ source('R/analysis/stan.run.R')
 model.files.jags.Tf.1 <-
     c("R/analysis/model.jags/model.LOGLIN.ER.AD.Tf.r.biomes.R")
 
+model.files.jags.Tf.2 <-
+    c("R/analysis/model.jags/model.LOGLIN.ER.AD.Tf.r.ecocode.R")
 
 
 fun.call.jags.and.save <- function(jags.model,
                                    list.jags,
                                    path.out,
                                    var.sample,
-                                   iter = 100,
-                                   warmup = 50,
+                                   iter = 50000,
+                                   warmup = 5000,
                                    chains = 3,
-                                   thin = 5){
+                                   thin = 50){
  start <-  Sys.time()
  model.file <- 
  cat(jags.model$bug, file = file.path(path.out,"model.file.bug")

R/analysis/lmer.run.R

@@ -27,6 +27,10 @@ run.model.for.set.one.trait <- function(model.file, fun.model, trait,
 #=====================================================================
 # Run lmer() (in package lme4) for one ecoregion, one trait, one model
 #=====================================================================
+model.files.lmer.Tf.0 <-
+      c("R/analysis/model.lmer/model.lmer.LOGLIN.ER.Tf.r.biomes.species.R",
+        "R/analysis/model.lmer/model.lmer.LOGLIN.AD.Tf.r.biomes.species.R",
+        "R/analysis/model.lmer/model.lmer.LOGLIN.Tf.r.biomes.species.R")
 
 model.files.lmer.Tf.1 <-
       c("R/analysis/model.lmer/model.lmer.LOGLIN.ER.AD.Tf.r.species.R")
@@ -36,6 +40,8 @@ model.files.lmer.Tf.2 <-
 
 model.files.lmer.Tf.3 <-
       c("R/analysis/model.lmer/model.lmer.LOGLIN.ER.AD.Tf.r.biomes.species.R")
+model.files.lmer.Tf.3b <-
+      c("R/analysis/model.lmer/model.lmer.LOGLIN.ER.AD.Tf.fixed.biomes.species.R")
 model.files.lmer.Tf.4 <-
       c("R/analysis/model.lmer/model.lmer.LOGLIN.ER.AD.Tf.r.ecocode.species.R")
 
@@ -175,8 +181,7 @@ load.and.save.data.for.lmer <- function(trait,
                                         min.obs= 10, 
                                         type.filling = species,
                                         fname = 'data.all.no.log.rds',
-                                        base.dir = "output/processed",
-                                        data.table.TF = FALSE){
+                                        base.dir = "output/processed"){
 data.tree.tot <- readRDS(file.path(base.dir, fname))
 df <- fun.select.data.for.lmer(data.tree.tot, trait,
                         type.filling = type.filling)
@@ -190,7 +195,7 @@ fun.select.data.for.analysis <- function(data.tree, abs.CWM.tntf, perc.neigh.sp,
                                          perc.neigh.gs, BATOT, min.obs,
                                          min.perc.neigh.sp = 0.90,
                                          min.perc.neigh.gs = 0.95,
-                                         min.BA.G = -60,
+                                         min.BA.G = -100,
                                          max.BA.G = 500){
 
 select.temp <-(1:nrow(data.tree))[!is.na(data.tree[["BA.G"]])      &
@@ -232,8 +237,8 @@ return(x/sd(x))
 ### get variables for lmer
 fun.get.the.variables.for.lmer.tree.id <- function(data.tree, BATOT, CWM.tn,
                                                    abs.CWM.tntf, tf,
-                                                   min.BA.G = 60){
-logG <- fun.center.and.standardized.var(log(data.tree[["BA.G"]] + min.BA.G))
+                                                   min.BA.G = 100){
+logG <- fun.center.and.standardized.var(log(data.tree[["BA.G"]] + min.BA.G+1))
 logD <- fun.center.and.standardized.var(log(data.tree[["D"]]))
 MAT <- fun.center.and.standardized.var(data.tree[["MAT"]])
 MAP <- fun.center.and.standardized.var(data.tree[["MAP"]])
@@ -273,8 +278,8 @@ return(data.frame(logG =  logG,
 
 fun.get.the.variables.for.lmer.tree.id.cat <- function(data.tree, BATOT, CWM.tn,
                                                    abs.CWM.tntf, tf,
-                                                   min.BA.G = 60){
-logG <- fun.center.and.standardized.var(log(data.tree[["BA.G"]]+min.BA.G))
+                                                   min.BA.G = 100){
+logG <- fun.center.and.standardized.var(log(data.tree[["BA.G"]]+min.BA.G+1))
 logD <- fun.center.and.standardized.var(log(data.tree[["D"]]))
 MAT <- fun.center.and.standardized.var(data.tree[["MAT"]])
 MAP <- fun.center.and.standardized.var(data.tree[["MAP"]])

R/analysis/model.jags/model.LOGLIN.ER.AD.Tf.r.ecocode.R

+
+
+load.model <- function(){
+
+	list(
+		name  = "LOGLIN.ER.AD.Tf.r.ecocode",
+		## parameters to save
+		pars = c('intercept' , 'mean_logD', 'mean_Tf',
+  'mean_sumBn', 'mean_sumTfBn', 'mean_sumTnBn',
+  'mean_sumTnTfBn_abs', 'sigma_inter_species',
+  'sigma_inter_set', 'sigma_inter_plot',
+  'sigma_inter_tree', 'sigma_logD_species',
+  'sigma_Tf_ecocode', 'sigma_sumBn_ecocode',
+  'sigma_sumBn_species', 'sigma_sumTfBn_ecocode',
+  'sigma_sumTnBn_ecocode', 'sigma_sumTnTfBn_abs_ecocode',
+  'sigma', 'param_Tf_ecocode',
+  'param_sumBn_ecocode', 'param_sumTfBn_ecocode', 
+  'param_sumTnBn_ecocode', 'param_sumTnTfBn_abs_ecocode'),
+		bug =
+"#######################################################
+ ######################## growth model with JAGS #######
+ model {
+     ############ Likelihood ###################
+for (i in 1:N_indiv) {
+logG[i] ~ dnorm(theo_g[i],tau)
+theo_g[i] <- inter[i] + DD[i] +TTf[i] + SUMBBN[i] +SUMTFBBN[i] + SUMTNBBN[i] + SUMABSBBN[i]
+
+inter[i] <-  intercept + intercept_species[species_id[i]] + intercept_tree[tree_id[i]]*tree_01[i]  + intercept_plot[plot_id[i]]  + intercept_set[set_id[i]]
+DD[i] <- (mean_logD + param_logD_species[species_id[i]])*logD[i]
+TTf[i] <- (mean_Tf + param_Tf_ecocode[ecocode_id[i]])*Tf[i]
+SUMBBN[i] <-(mean_sumBn + param_sumBn_ecocode[ecocode_id[i]] + param_sumBn_species[species_id[i]])*sumBn[i]
+SUMTFBBN[i] <- (mean_sumTfBn + param_sumTfBn_ecocode[ecocode_id[i]])*sumTfBn[i]
+SUMTNBBN[i] <- (mean_sumTnBn + param_sumTnBn_ecocode[ecocode_id[i]])*sumTnBn[i]
+SUMABSBBN[i] <- (mean_sumTnTfBn_abs + param_sumTnTfBn_abs_ecocode[ecocode_id[i]])*sumTnTfBn_abs[i] 
+}
+
+ ################################################
+ ########### Hierarchical parameters ########
+
+### species random param
+for (n in 1:N_species)
+{
+    param_logD_species[n] ~ dnorm(0,tau_logD_species)
+    param_sumBn_species[n] ~ dnorm(0,tau_sumBn_species)
+    intercept_species[n] ~ dnorm(0,tau_inter_species)
+}
+
+## plot effect
+for (j in 1:N_plot)
+  {
+     intercept_plot[j] ~ dnorm(0,tau_inter_plot)      
+  }
+## tree effect
+for (j in 1:N_tree)
+  {
+  intercept_tree[j] ~ dnorm(0,tau_inter_tree)      
+  }
+
+## tree effect
+for (j in 1:N_set)
+  {
+  intercept_set[j] ~ dnorm(0,tau_inter_set)      
+  }
+
+## ecocode 
+for (j in 1:N_ecocode)
+  {
+  param_Tf_ecocode[j] ~ dnorm(0, tau_Tf_ecocode)
+  param_sumBn_ecocode[j] ~ dnorm(0, tau_sumBn_ecocode)
+  param_sumTfBn_ecocode[j] ~ dnorm(0 ,tau_sumTfBn_ecocode)
+  param_sumTnBn_ecocode[j] ~ dnorm(0 ,tau_sumTnBn_ecocode)
+  param_sumTnTfBn_abs_ecocode[j] ~ dnorm(0 ,tau_sumTnTfBn_abs_ecocode)
+  }
+###############################################
+########### Non-hierarchical parameters ########
+
+# constants for prior
+tau0 <- 1.0E-4
+
+  intercept ~ dnorm(0,tau0)T(-10, 10)
+  mean_logD ~ dnorm(0,tau0)T(-10, 10)
+  mean_Tf ~ dnorm(0,tau0)T(-10, 10)
+  mean_sumBn ~ dnorm(0,tau0)T(-10, 10)
+  mean_sumTfBn ~ dnorm(0,tau0)T(-10, 10)
+  mean_sumTnBn ~ dnorm(0,tau0)T(-10, 10)
+  mean_sumTnTfBn_abs ~ dnorm(0,tau0)T(-10, 10)
+
+
+# variance error
+tau <-  pow(sigma,-2)
+sigma ~ dunif(0.00001,5)
+tau_inter_plot <-  pow(sigma_inter_plot,-2)
+sigma_inter_plot ~ dunif(0.00001,5)
+tau_inter_tree <-  pow(sigma_inter_tree,-2)
+sigma_inter_tree ~ dunif(0.00001,5)
+tau_inter_set <-  pow(sigma_inter_set,-2)
+sigma_inter_set ~ dunif(0.00001,5)
+tau_inter_species <-  pow(sigma_inter_species,-2)
+sigma_inter_species ~ dunif(0.00001,5)
+tau_logD_species <-  pow(sigma_logD_species,-2)
+sigma_logD_species ~ dunif(0.00001,5)
+tau_sumBn_species <-  pow(sigma_sumBn_species,-2)
+sigma_sumBn_species ~ dunif(0.00001,5)
+tau_Tf_ecocode <-  pow(sigma_Tf_ecocode,-2)
+sigma_Tf_ecocode ~ dunif(0.00001,5)
+tau_sumBn_ecocode <-  pow(sigma_sumBn_ecocode,-2)
+sigma_sumBn_ecocode ~ dunif(0.00001,5)
+tau_sumTfBn_ecocode <-  pow(sigma_sumTfBn_ecocode,-2)
+sigma_sumTfBn_ecocode ~ dunif(0.00001,5)
+tau_sumTnBn_ecocode <-  pow(sigma_sumTnBn_ecocode,-2)
+sigma_sumTnBn_ecocode ~ dunif(0.00001,5)
+tau_sumTnTfBn_abs_ecocode <-  pow(sigma_sumTnTfBn_abs_ecocode,-2)
+sigma_sumTnTfBn_abs_ecocode ~ dunif(0.00001,5)
+
+
+} # End of the jags model
+ ")
+}

R/analysis/model.lmer/model.lmer.LOGLIN.AD.Tf.r.biomes.species.R

+load.model <- function () {
+    list(name="lmer.LOGLIN.AD.Tf.r.biomes.species",
+         var.BLUP = 'biomes.id',
+         lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+Tf+logD+sumBn+sumTnTfBn.abs+(1|species.id) +(logD-1|species.id) +(sumBn-1|species.id)+(Tf-1|biomes.id)+(sumBn-1|biomes.id)+(sumTnTfBn.abs-1|biomes.id)"))
+}
+

R/analysis/model.lmer/model.lmer.LOGLIN.ER.AD.Tf.fixed.biomes.species.R

+load.model <- function () {
+    list(name="lmer.LOGLIN.ER.AD.Tf.r.biomes.species",
+         var.BLUP = 'biomes.id',
+         lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+biomes.id+Tf+Tf:biomes.id+logD+sumBn+sumBn:biomes.id+sumTfBn+sumTfBn:biomes.id+sumTnBn+sumTnBn:biomes.id+sumTnTfBn.abs+sumTnTfBn.abs:biomes.id+(1|species.id) +(logD-1|species.id) +(sumBn-1|species.id)))
+}
+

R/analysis/model.lmer/model.lmer.LOGLIN.ER.Tf.r.biomes.species.R

+load.model <- function () {
+    list(name="lmer.LOGLIN.ER.Tf.r.biomes.species",
+         var.BLUP = 'biomes.id',
+         lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+Tf+logD+sumBn+sumTfBn+sumTnBn+(1|species.id) +(logD-1|species.id) +(sumBn-1|species.id)+(Tf-1|biomes.id)+(sumBn-1|biomes.id)+(sumTfBn-1|biomes.id)+(sumTnBn-1|biomes.id)"))
+}
+

R/analysis/model.lmer/model.lmer.LOGLIN.Tf.r.biomes.species.R

+load.model <- function () {
+    list(name="lmer.LOGLIN.Tf.r.biomes.species",
+         var.BLUP = 'biomes.id',
+         lmer.formula.tree.id=formula("logG~1+(1|set.id)+(1|species.id)+(1|plot.id)+Tf+logD+sumBn+(1|species.id) +(logD-1|species.id) +(sumBn-1|species.id)+(Tf-1|biomes.id)+(sumBn-1|biomes.id)"))
+}
+

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

 
-  
-
-
 load.model <- function(){
 
 	list(name  = "LOGLIN.ER.AD.Tf.r.biomes",
@@ -68,14 +65,14 @@ parameters{
     vector[N_biomes] param_sumTnBn_biomes;
     vector[N_biomes] param_sumTnTfBn_abs_biomes;
     vector[N_species] param_logD_species;
-}
-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;
+}
+transformed parameters {
+    # vector for prediction
+    vector[N_indiv] theo_g;
  
     for (i in 1:N_indiv){
         theo_g[i] <- intercept + intercept_species[species_id[i]]
@@ -83,8 +80,8 @@ transformed parameters {
           + intercept_plot[plot_id[i]]
           + intercept_set[set_id[i]]
           + (mean_logD + param_logD_species[species_id[i]])*logD[i] 
-          + (mean_Tf + param_Tf_biomes[biomes_id[i]])*Tf[i] 
-          + (mean_sumBn + param_sumBn_biomes[biomes_id[i]] + param_sumBn_species[species_id[i]])*sumBn[i] 
+          + (mean_Tf +param_Tf_biomes[biomes_id[i]])*Tf[i] 
+          + (mean_sumBn + param_sumBn_biomes[biomes_id[i]] +  param_sumBn_species[species_id[i]])*sumBn[i] 
           + (mean_sumTfBn + param_sumTfBn_biomes[biomes_id[i]])*sumTfBn[i] 
           + (mean_sumTnBn + param_sumTnBn_biomes[biomes_id[i]])*sumTnBn[i] 
           + (mean_sumTnTfBn_abs + param_sumTnTfBn_abs_biomes[biomes_id[i]])*sumTnTfBn_abs[i] 
@@ -104,19 +101,19 @@ model {
   ### 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);
+    intercept_species ~ normal(0, sigma_inter_species);
   ### plot random param
     intercept_plot ~ normal(0,sigma_inter_plot);
   ### tree random effect
-    intercept_tree ~ normal(0,sigma_inter_tree);
-  ### biomes random param
-    param_Tf_biomes ~ normal(0, sigma_Tf_biomes);
-    param_sumBn_biomes ~ normal(0, sigma_sumBn_biomes);
+    intercept_tree ~ normal(0, sigma_inter_tree);
+     intercept_set ~ normal(0, sigma_inter_set);
+ ### biomes random param
+    param_Tf_biomes ~ normal(0,  sigma_Tf_biomes);
+    param_sumBn_biomes ~ normal(0,sigma_sumBn_biomes);
     param_sumTfBn_biomes ~ normal(0 ,sigma_sumTfBn_biomes);
     param_sumTnBn_biomes ~ normal(0 ,sigma_sumTnBn_biomes);
-    param_sumTnTfBn_abs_biomes ~ normal(0 ,sigma_sumTnTfBn_abs_biomes);
-    intercept_set ~ normal(0,sigma_inter_set);
-
+    param_sumTnTfBn_abs_biomes ~ normal(0 , sigma_sumTnTfBn_abs_biomes);
+ 
   ###############################################
   ########### Non-Hierarchical parameters ########
   # constant for prior

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

+
+load.model <- function(){
+
+	list(name  = "LOGLIN.ER.AD.Tf.r.biomes",
+	     pars = c('intercept' , 'mean_logD', 'mean_Tf',
+  'mean_sumBn', 'mean_sumTfBn', 'mean_sumTnBn',
+  'mean_sumTnTfBn_abs', 'sigma_inter_species',
+  'sigma_inter_set', 'sigma_inter_plot',
+  'sigma_inter_tree', 'sigma_logD_species',
+  'sigma_Tf_biomes', 'sigma_sumBn_biomes',
+  'sigma_sumBn_species', 'sigma_sumTfBn_biomes',
+  'sigma_sumTnBn_biomes', 'sigma_sumTnTfBn_abs_biomes',
+  'sigma', 'param_Tf_biomes',
+  'param_sumBn_biomes', 'param_sumTfBn_biomes', 
+  'param_sumTnBn_biomes', 'param_sumTnTfBn_abs_biomes'),## parameters to save
+ 	     stan = "
+data {
+    int<lower=0> N_indiv;
+    int<lower=0> N_species;
+    int<lower=0> N_plot;
+    int<lower=0> N_biomes;
+    int<lower=0> N_set;
+    int<lower=0> N_tree;
+
+    int species_id[N_indiv];
+    int plot_id[N_indiv];
+    int biomes_id[N_indiv];
+    int set_id[N_indiv];
+    int tree_id[N_indiv];
+
+    real logG[N_indiv];
+    real logD[N_indiv];
+    real sumBn[N_indiv];
+    real Tf[N_indiv];
+    real sumTfBn[N_indiv];
+    real sumTnBn[N_indiv];
+    real sumTnTfBn_abs[N_indiv];
+    real tree_01[N_indiv];
+}
+
+parameters{
+    real<lower=-1,upper=1> intercept;
+    real<lower=-10,upper=10> mean_logD;
+    real<lower=-10,upper=10> mean_Tf;
+    real<lower=-10,upper=10> mean_sumBn;
+    real<lower=-10,upper=10> mean_sumTfBn;
+    real<lower=-10,upper=10> mean_sumTnBn;
+    real<lower=-10,upper=10> mean_sumTnTfBn_abs;
+    real<lower=0.0001,upper=5> sigma_inter_species;
+    real<lower=0.0001,upper=5> sigma_inter_set;
+    real<lower=0.0001,upper=5> sigma_inter_plot;
+    real<lower=0.0001,upper=5> sigma_inter_tree;
+    real<lower=0.0001,upper=5> sigma_logD_species;
+    real<lower=0.0001,upper=5> sigma_Tf_biomes;
+    real<lower=0.0001,upper=5> sigma_sumBn_biomes;
+    real<lower=0.0001,upper=5> sigma_sumBn_species;
+    real<lower=0.0001,upper=5> sigma_sumTfBn_biomes;
+    real<lower=0.0001,upper=5> sigma_sumTnBn_biomes;
+    real<lower=0.0001,upper=5> sigma_sumTnTfBn_abs_biomes;
+    real<lower=0.0001,upper=5> sigma;
+    vector[N_biomes] param_Tf_biomes;
+    vector[N_biomes] param_sumBn_biomes;
+    vector[N_species] param_sumBn_species;
+    vector[N_biomes] param_sumTfBn_biomes;
+    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;
+ 
+    for (i in 1:N_indiv){
+        theo_g[i] <- intercept + sigma_inter_species*intercept_species[species_id[i]]
+          + sigma_inter_tree*intercept_tree[tree_id[i]]*tree_01[i]
+          + sigma_inter_plot*intercept_plot[plot_id[i]]
+          + sigma_inter_set*intercept_set[set_id[i]]
+          + (mean_logD + sigma_logD_species*param_logD_species[species_id[i]])*logD[i] 
+          + (mean_Tf + sigma_Tf_biomes*param_Tf_biomes[biomes_id[i]])*Tf[i] 
+          + (mean_sumBn + sigma_sumBn_biomes*param_sumBn_biomes[biomes_id[i]] +  sigma_sumBn_species*param_sumBn_species[species_id[i]])*sumBn[i] 
+          + (mean_sumTfBn + sigma_sumTfBn_biomes*param_sumTfBn_biomes[biomes_id[i]])*sumTfBn[i] 
+          + (mean_sumTnBn + sigma_sumTnBn_biomes*param_sumTnBn_biomes[biomes_id[i]])*sumTnBn[i] 
+          + (mean_sumTnTfBn_abs + sigma_sumTnTfBn_abs_biomes*param_sumTnTfBn_abs_biomes[biomes_id[i]])*sumTnTfBn_abs[i] 
+          ;  
+       }
+}
+
+model {
+  # constants for prior
+  real sigma0;
+
+################################################################################
+######################## growth model with STAN #############################
+
+  ###############################################
+  ########### Hierarchical parameters ########
+  ### species random param
+    param_logD_species ~ normal(0,1);
+    param_sumBn_species ~ normal(0,1);
+    intercept_species ~ normal(0,1);
+  ### plot random param 
+    intercept_plot ~ normal(0,1);
+  ### tree random effect
+    intercept_tree ~ normal(0,1);
+  ### biomes random param
+    param_Tf_biomes ~ normal(0, 1);
+    param_sumBn_biomes ~ normal(0,1);
+    param_sumTfBn_biomes ~ normal(0 ,1);
+    param_sumTnBn_biomes ~ normal(0 ,1);
+    param_sumTnTfBn_abs_biomes ~ normal(0 , 1);
+    intercept_set ~ normal(0, 1);
+
+  ###############################################
+  ########### Non-Hierarchical parameters ########
+  # constant for prior
+  sigma0 <- 10;
+  # slope and intercept
+  intercept ~ normal(0,sigma0);
+  mean_logD ~ normal(0,sigma0);
+  mean_Tf ~ normal(0,sigma0);
+  mean_sumBn ~ normal(0,sigma0);
+  mean_sumTfBn ~ normal(0,sigma0);
+  mean_sumTnBn ~ 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_Tf_biomes~ uniform(0,6);
+  sigma_sumBn_biomes~ uniform(0,6);
+  sigma_sumTfBn_biomes~ uniform(0,6);
+  sigma_sumTnBn_biomes~ uniform(0,6);
+  sigma_sumTnTfBn_abs_biomes~ uniform(0,6);
+  sigma~ uniform(0,6);
+
+  ###############################################
+  ############ Likelihood ###################
+  logG ~ normal(theo_g,sigma);
+}
+")
+}
+

R/analysis/stan.run.R

@@ -10,6 +10,7 @@ model.files.stan.Tf.1 <-
     c("R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.r.no.R",
       "R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.r.set.R",
       "R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.r.biomes.R",
+      "R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.r.biomes.bis.R",
       "R/analysis/model.stan/model.stan.LOGLIN.ER.AD.Tf.r.ecocode.R")
 
 

R/analysis/test.jags.R

+### test jags
+
+
+source('R/analysis/jags.run.R')
+
+
+run.multiple.model.for.set.one.trait(model.files.jags.Tf.1, run.jags,
+                                     trait = 'Wood.density',type.filling='species', sample.size = 1000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 200, warmup = 20, chains = 3, 
+                                     init.TF = TRUE)
+
+t1000 <- system.time(
+res <- run.multiple.model.for.set.one.trait(model.files.jags.Tf.1,
+                                                                                 run.jags,
+                                     trait = 'Wood.density',type.filling='species', sample.size = 1000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 1000, warmup = 20, chains = 3,
+                                     init.TF = FALSE))
+
+
+t1000.4 <- system.time(
+res <- run.multiple.model.for.set.one.trait(model.files.jags.Tf.1,
+                                                                                 run.jags,
+                                     trait = 'Wood.density',type.filling='species', sample.size = 1000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 5000, warmup = 20, chains = 3,
+                                     init.TF = FALSE))
+
+t1000.8 <- system.time(
+res <- run.multiple.model.for.set.one.trait(model.files.jags.Tf.1,
+                                                                                 run.jags,
+                                     trait = 'Wood.density',type.filling='species', sample.size = 1000,
+                                     var.sample = 'ecocode.id',
+                                     iter = 10000, warmup = 20, chains = 3,
+                                     init.TF = FALSE, thin = 10))
+
+
+
+t1000.4 <- 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 = 400, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+t1000.8 <- 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 = 800, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+
+
+
+sample.vec <- c(1000, 10000, 50000, 100000)
+t <- c(t1000['elapsed'], t10000['elapsed'], t50000['elapsed'], t100000['elapsed'])
+t4 <- c(t1000.4['elapsed'], t10000.4['elapsed'], t50000.4['elapsed'], t100000.4['elapsed'])
+t8 <- c(t1000.8['elapsed'], t10000.8['elapsed'], t50000.8['elapsed'])
+
+pdf('time.stan.pdf')
+plot(sample.vec, t, ylim = range(t, t4, t.b, t4.b), type = 'b')
+lines(sample.vec, t4, type = 'b', col = 'green')
+lines(sample.vec[1:3], t8, type = 'b', col = 'pink')
+dev.off()
+###

R/analysis/test.stan.R

 ### test stan
+
+## 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 ???
+
+## Need to compare the two code
+
+
 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,
@@ -6,63 +15,114 @@ run.multiple.model.for.set.one.trait(model.files.stan.Tf.1[3], run.stan,
                                      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,
+t1000 <- 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))
- 
+
+
+
+t1000.4 <- 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 = 400, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+t1000.8 <- 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 = 800, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+
+t1000.20 <- 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 = 2000, 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,
+t10000 <- 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))
 
+
+t10000.4 <- 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 = 400, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))
+t10000.8 <- 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 = 800, warmup = 20, chains = 2, chain_id = '1',
+                                     init.TF = FALSE))