rerun deid

R/analysis/lmer.output-fun.R

@@ -3,80 +3,45 @@ library(lme4)
 library(MuMIn)
 
 
-read.lmer.output <- function(file.name){
-  tryCatch(readRDS(file.name), error = function(cond)return(NULL))   # Choose a return value in case of error
+read.lmer.output <- function(file.name){ #OK
+  tryCatch(readRDS(file.name), error = function(cond)return(NULL))
+  # Choose a return value in case of error
 }
 
 
-
-summarise.lmer.output <- function(x){
- list( nobs = nobs(x),
-       R2m =r.squaredGLMM(x)[['R2m']],
-       R2c =r.squaredGLMM(x)[['R2c']],
-       AIC = AIC(x),
-       deviance = deviance(x),
-       conv = x@optinfo$conv,
-       effect.response.var = variance.fixed.glmm.lmer.effect.and.response(x),
-       fixed.coeff.E = fixef(x),
-       fixed.coeff.Std.Error = sqrt(diag(vcov(x))),
-       fixed.var = variance.fixed.glmm.lmer(x))
+summarise.lmer.all.output <- function(x, random.name ){#OK
+ list(nobs = nobs(x),
+      R2m =r.squaredGLMM(x)[['R2m']],
+      R2c =r.squaredGLMM(x)[['R2c']],
+      AIC  =  AIC(x),
+      deviance = deviance(x),
+      conv = x@optinfo$conv,
+      fixed.coeff.E = fixef(x),
+      fixed.coeff.Std.Error = sqrt(diag(vcov(x))),
+      set.BLUP = coef(x)[[random.name]])
 }
 
-summarise.lmer.all.output <- function(x, random.name ){
- list( nobs = nobs(x),
-       R2m =r.squaredGLMM(x)[['R2m']],
-       R2c =r.squaredGLMM(x)[['R2c']],
-       AIC  =  AIC(x),
-       deviance = deviance(x),
-       conv = x@optinfo$conv,
-       effect.response.var = variance.fixed.glmm.lmer.effect.and.response(x),
-       fixed.coeff.E = fixef(x),
-       fixed.coeff.Std.Error = sqrt(diag(vcov(x))),
-       fixed.var = variance.fixed.glmm.lmer(x),
-       set.BLUP = coef(x)[[random.name]])
-}
-
-summarise.lmer.output.list <- function(f ){
-    list.output.lmer <- read.lmer.output(f)
-    output.lmer <- list.output.lmer[['output']]
-    relgrad <- list.output.lmer[['relgrad']]
-    list.sd <- list.output.lmer[['lits.sd']]
-    if(!is.null(output.lmer)){
-	res <- list(files.details = files.details(f) ,
-		    lmer.summary = summarise.lmer.output( output.lmer),
-                    terms = terms(output.lmer),
-                    vcov = vcov(output.lmer),
-                    relgrad = relgrad,
-                    list.sd = list.sd)
-    }else{
-        res <- NULL
-    }
-    return(res)
-}
 
 
-summarise.lmer.output.all.list <- function(f ){
+summarise.lmer.output.all.list <- function(f){#OK
     list.output.lmer <- read.lmer.output(f)
     output.lmer <- list.output.lmer[['output']]
     relgrad <- list.output.lmer[['relgrad']]
     list.sd <- list.output.lmer[['list.sd']]
     if(!is.null(output.lmer)){
         details <-  files.details.all(f)
-
         source(file.path('R','analysis', 'model.lmer',
                          paste('model',details$model,'R', sep = '.')))
         model <- load.model()
-
 	res <- list(files.details = details,
-		    lmer.summary = summarise.lmer.all.output( output.lmer,
-                                                              random.name =
-                                                              model$var.BLUP),
+		    lmer.summary = summarise.lmer.all.output(output.lmer,
+                                                             random.name = model$var.BLUP),
                     terms = terms(output.lmer),
                     vcov = vcov(output.lmer),
                     relgrad = relgrad,
                     list.sd = list.sd)
     }else{
-        res <- NULL
+       res <- NULL
     }
     rm(list.output.lmer)
     gc()
@@ -85,16 +50,8 @@ summarise.lmer.output.all.list <- function(f ){
 
 
 
-files.details <- function(x){
-	s <- data.frame(t(strsplit(x, "/", fixed = TRUE)[[1]]),
-                        stringsAsFactors =  FALSE)
-	names(s)  <- c("d1", "d2", "data.type", "ecocode", "trait",
-                       "model", "file" )
-	s[-(1:2)]
-}
-
 
-files.details.all <- function(x){
+files.details.all <- function(x){#OK
 	s <- data.frame(t(strsplit(x, "/", fixed = TRUE)[[1]]),
                         stringsAsFactors =  FALSE)
 	names(s)  <- c("d1", "d2", "data.type", "trait",
@@ -102,89 +59,38 @@ files.details.all <- function(x){
 	s[-(1:2)]
 }
 
-BLUP.CI <- function(fm, var.BLUP = 'species.id'){ ## NOT WORKING WHEN MULTIPLE term per factor
-cV <- ranef(fm, condVar = TRUE, whichel = var.BLUP)
-return(cV)
-}
-
-#### R squared functions
-
-# Function rsquared.glmm requires models to be input as a list
-# (can include fixed-
-# effects only models,but not a good idea to mix models of
-# class "mer" with models
-# of class "lme") FROM
-#  http://jslefche.wordpress.com/2013/03/13/r2-for-linear-mixed-effects-models/
-
-Rsquared.glmm.lmer <- function(i){
-# Get variance of fixed effects by multiplying coefficients by design matrix
-      VarF <- var(as.vector(fixef(i) %*% t(i@pp$X)))
-      # Get variance of random effects by extracting variance components
-      VarRand <- colSums(do.call(rbind, lapply(VarCorr(i), function(j) j[1])))
-      # Get residual variance
-      VarResid <- attr(VarCorr(i), "sc")^2
-      # Calculate marginal R-squared (fixed effects/total variance)
-      Rm <- VarF/(VarF+VarRand+VarResid)
-      # Calculate conditional R-squared (fixed effects+random effects/total variance)
-      Rc <- (VarF+VarRand)/(VarF+VarRand+VarResid)
-      # Bind R^2s into a matrix and return with AIC values
-      Rsquared.mat <- data.frame(Class = class(i), Family = "Gaussian", Marginal = Rm,
-                              Conditional = Rc, AIC = AIC(i))
-      return(Rsquared.mat)
-}
-
 
-variance.fixed.glmm.lmer <- function(i){
 
-# Get variance of for each fixed effects by multiplying coefficients by design matrix
-var.vec <- apply(fixef(i) * t(i@pp$X), MARGIN = 1, var)
-# Get variance of fixed effects by multiplying coefficients by design matrix
-VarF <- var(as.vector(fixef(i) %*% t(i@pp$X)))
-# Get variance of random effects by extracting variance components
-VarRand <- colSums(do.call(rbind, lapply(VarCorr(i), function(j) j[1])))
-# Get residual variance
-VarResid <- attr(VarCorr(i), "sc")^2
-var.vec <- var.vec/(VarF+VarRand+VarResid)
-names(var.vec) <- paste(names(var.vec), "VAR", sep = ".")
-return(var.vec)
-}
 
-variance.fixed.glmm.lmer.effect.and.response <- function(i){
-if(sum(c("sumTfBn", "sumTnBn") %in% names(fixef(i)))==2){
-# Get variance of for each fixed effects by multiplying coefficients by design matrix
-var.vec <- var(as.vector(fixef(i)[c("sumTfBn", "sumTnBn")] %*%
-                         t(i@pp$X[, c("sumTfBn", "sumTnBn")])))
-# Get variance of fixed effects by multiplying coefficients by design matrix
-VarF <- var(as.vector(fixef(i) %*% t(i@pp$X)))
-# Get variance of random effects by extracting variance components
-VarRand <- colSums(do.call(rbind, lapply(VarCorr(i), function(j) j[1])))
-# Get residual variance
-VarResid <- attr(VarCorr(i), "sc")^2
-var.vec <- var.vec/(VarF+VarRand+VarResid)
-}else{
-var.vec <- NA
-}
-names(var.vec) <- paste("effect.response", "VAR", sep = ".")
-return(var.vec)
-}
 
+#### Function to proceed all output and save in a list
 
 
+format.all.output.lmer <- function(file.name,
+                               list.file.name,
+                               models,
+                               traits = c("SLA", "Wood.density", "Max.height",
+                                          "Leaf.N", "Seed.mass"),
+                               data.type = "simple"){
+files <-  c()
+for (trait in traits){
+    for(model in models){
+       source(model, local = TRUE)
+       model.obj <- load.model()
+       pathout <- output.dir('lmer', model.obj$name, trait, data.type)
+       files <- c(files,file.path(pathout,file.name))
+      }
+    }
 
-## function to turn lmer output from list to DF
-fun.format.in.data.frame <- function(list.res, names.param){
-dat.t <- data.frame(t(rep(NA, 3*length(names.param))))
-names(dat.t) <-  c(names.param, paste(names.param, "Std.Error", sep = ".")
-                   , paste(names.param, "VAR", sep = "."))
-dat.t[, match(names(list.res$lmer.summary$fixed.coeff.E), names(dat.t))] <-
-    list.res$lmer.summary$fixed.coeff.E
-dat.t[, length(names.param)+match(names(list.res$lmer.summary$fixed.coeff.E),
-                                 names(dat.t))] <-
-    list.res$lmer.summary$fixed.coeff.Std.Error
-dat.t[, match(names(list.res$lmer.summary$fixed.var), names(dat.t))] <-
-    list.res$lmer.summary$fixed.var
-res <- data.frame(list.res$files.details, list.res$lmer.summary[1:7], dat.t)
-return(res)
+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=file.path('output' , list.file.name))
+rm(out)
+gc()
 }
 
 
@@ -192,10 +98,13 @@ return(res)
 ###########################
 ### FUNCTIONS TO PLOTS RESULTS
 
-fun.plot.error.bar.horiz <- function(x,  y,  sd,  small.bar = (max(x) - min(x))/40, ...){
-segments( unlist(x - 1.96*sd), y,   unlist(x +1.96*sd), y, ...)
-segments( unlist(x - 1.96*sd), y-small.bar,   unlist(x -1.96*sd), y+small.bar, ...)
-segments( unlist(x + 1.96*sd), y-small.bar,   unlist(x +1.96*sd), y+small.bar, ...)
+fun.plot.error.bar.horiz <- function(x,  y,  sd, #OK
+                                     small.bar = (max(x) - min(x))/40, ...){
+segments(unlist(x - 1.96*sd), y, unlist(x +1.96*sd), y, ...)
+segments(unlist(x - 1.96*sd), y-small.bar,
+         unlist(x -1.96*sd), y+small.bar, ...)
+segments(unlist(x + 1.96*sd), y-small.bar,
+         unlist(x +1.96*sd), y+small.bar, ...)
 }
 
 fun.col.param <- function(){
@@ -952,88 +861,13 @@ ggplot(DF, aes_string(x = var.x, y = var.y, colour = var.quant)) +
 
 
 
-fun.axis.one.by.one <- function(x, side, labels, cols.vec , cex.axis = 2){
+fun.axis.one.by.one <- function(x, side, labels, cols.vec , cex.axis = 2){ # OK
     axis(side, x, labels =   bquote(.(labels[x])), las = 1, cex.axis = cex.axis,
                             mgp = c(1.5,0.55,0),col.axis = cols.vec[x])
 }
 
 
 
-plot.param <-  function(list.res,
-                        model = 'lmer.LOGLIN.ER.AD.Tf',
-                        traits = c('Wood.density', 'SLA', 'Max.height'),
-                        traits.names = c(Wood.density= 'Wood density',
-                                         SLA = 'Specific leaf area',
-                                         Max.height = 'Maximum height'),
-                        param.vec = c("Tf","sumBn", "sumTnBn",
-                                      "sumTfBn", "sumTnTfBn.abs"),
-                        param.names = c('Direct trait',
-                                        'Compet',
-                                        'Compet effect x trait',
-                                        'Compet response x trait',
-                                        'Compet x trait dissimilarity'),
-                        param.print = 1:5,
-                        traits.print = 1:3,
-                        col.names = fun.col.param(),
-                        data.type = "simple",
-                        add.param.descrip.TF = TRUE,
-                        intra.TF = FALSE,
-                        ...){
- if(!intra.TF) x.line <- -0.63
- if(intra.TF) x.line = -0.7
- m <- matrix(c(1:3), 1, 3)
- big.m <-  2.8
- small.m <-  0.42
- wid <-  c(big.m ,0, small.m) +
-         rep((14-big.m-small.m)/3, each= 3)
-layout(m, widths=wid)
-for (i in traits[traits.print]){
-    list.temp <- list.res[[paste(data.type, "_", i ,
-                                 "_", model,
-                                 sep = '')]]$lmer.summary
-    param.mean <- list.temp$fixed.coeff.E[param.vec]
-    param.mean[!names(param.mean) %in% c("Tf", "sumTfBn")] <-
-        -param.mean[!names(param.mean) %in% c("Tf", "sumTfBn")]
-    param.std <- list.temp$fixed.coeff.Std.Error
-    names(param.std) <- names(list.temp$fixed.coeff.E)
-    param.std <- param.std[param.vec]
-    param.BLUP <- list.temp$set.BLUP
-    if(i == traits[1]) {
-        par(mai=c(1.2, big.m,0.6,0), xpd = TRUE)
-    }else{
-        par(mai=c(1.2,0,0.6,0), xpd = TRUE)
-    }
-    if(i == traits[length(traits)]) {
-        par(mai=c(1.2,0,0.6,small.m), xpd = TRUE)
-    }
-      plot(param.mean[param.print], (1:length(param.vec))[param.print],
-         yaxt = 'n', xlab = NA, ylab = NA,
-         pch = 16, cex = 2, cex.lab = 1.7, cex.axis = 1.5,
-         ylim = range(1-0.21, length(param.vec)+0.21), ...)
-    mtext(traits.names[i], side=3,  cex =1.7, line = 1)
-    box(lwd= 2)
-    lines(c(0, 0), c(par()$usr[3], par()$usr[4]))
-    if(i == traits[1]) {lapply(1:length(param.vec),
-                               fun.axis.one.by.one,
-                               side = 2,
-                               labels = param.names,
-                               cols.vec = col.names[param.vec])
-        if(add.param.descrip.TF){
-                        fun.param.descrip(c(-0.1,0.1),
-                                          length(param.vec),  x.line)
-                    }
-                        }
-        if(i == traits[2]){
-        mtext('Standardized coefficients', side=1,  cex =1.7,
-                             line = 4)
-        print(i)
-    }
-    fun.plot.error.bar.horiz(param.mean[param.print],
-                             (1:length(param.vec))[param.print],
-                             param.std[param.print])
-   }
-}
-
 
 
 
@@ -1048,30 +882,6 @@ extract.param <-  function(trait, list.res,
     return(param.mean)
 }
 
-extract.param.b <-  function(trait, list.res,
-                           model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
-                           param.vec = c("logD", "Tf","sumBn", "sumTnBn",
-                                         "sumTfBn", "sumTnTfBn.abs"),
-                           data.type = 'simple',
-                           biomes = biomes.factor.selected()  ){
-   require(reshape2)
-   list.temp <- list.res[[paste0(data.type, "_", trait ,
-                                 "_", model)]]
-   res <- lapply(param.vec, fun.get.fixed.biomes, list.temp, biomes)
-   param <- do.call('rbind', lapply(res, function(x) x[[1]]))
-   rownames(param) <- param.vec
-   vec.Neg <- !row.names(param) %in% c("(Intercept)", "logD",
-                                       "Tf", 'MAT', 'MAP', "sumTfBn")
-   param[vec.Neg,] <-  - param[vec.Neg,]
-   df.param <- data.frame(param=rownames(param), param)
-   df.melt <- melt(df.param, id.vars = 'param')
-   param.sd <- do.call('rbind', lapply(res, function(x) x[[2]]))
-   df.param.sd <- data.frame(param=rownames(param), param.sd)
-   names(df.param.sd) <- names(df.param)
-   df.melt.sd <- melt(df.param.sd, id.vars = 'param')
-
-   return(list(df.melt, df.melt.sd))
-}
 
 
 extract.param.sd <-  function(trait, list.res,
@@ -1081,71 +891,67 @@ extract.param.sd <-  function(trait, list.res,
                            data.type = 'simple'){
    list.temp <- list.res[[paste0(data.type, "_", trait ,
                                  "_", model)]]$lmer.summary
-    param.sd <- list.temp$fixed.coeff.Std.Error
-    names(param.sd) <- names(list.temp$fixed.coeff.E)
-    return(param.sd[param.vec])
+   param.sd <- list.temp$fixed.coeff.Std.Error
+   names(param.sd) <- names(list.temp$fixed.coeff.E)
+   return(param.sd[param.vec])
 }
 
 
-extract.R2c <-  function(trait, list.res,
+extract.R2c <-  function(trait, list.res, #OK
                          model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
                          data.type = 'simple'){
    list.temp <- list.res[[paste0(data.type,"_", trait ,
                                  "_", model)]]$lmer.summary
-    return(list.temp$R2c)
+   return(list.temp$R2c)
 }
 
-extract.R2m <-  function(trait, list.res,
+extract.R2m <-  function(trait, list.res, #OK
                          model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
                          data.type = 'simple'){
    list.temp <- list.res[[paste0(data.type,"_", trait ,
                                  "_", model)]]$lmer.summary
-    return(list.temp$R2m)
-}
-
-extract.AIC <-  function(trait, list.res,
-                         model = 'lmer.LOGLIN.ER.AD.Tf.r.biomes.species',
-                         data.type = 'simple'){
-   list.temp <- list.res[[paste0(data.type,"_", trait ,
-                                 "_", model)]]$lmer.summary
-    return(list.temp$AIC)
+   return(list.temp$R2m)
 }
 
 ## get fixed biomes
-fun.get.fixed.biomes <-  function(var, list,
+fun.get.fixed.biomes <-  function(var, list, #OK
                                   biomes.vec ){
-param <- list$lmer.summary$fixed.coeff.E
-remaining <- grep(paste0(':',var), names(param))
-boreal <- seq_len(length(param))[names(param) == var]
-select <- c(boreal, remaining)
-## create design matrix
-ff <-  ~ biomes.id
-mm <- model.matrix(ff, data.frame(biomes.id = biomes.vec))
-# compute mean and std based on Bolker post
-param.biomes <- drop(mm %*% param[select])
-names(param.biomes) <-  biomes.vec
-std.biomes <- sqrt(diag(mm %*% tcrossprod(list$vcov[select, select],mm)))
-return(list(fixed.biomes = param.biomes,
-            fixed.biomes.std = std.biomes))
-}
-
-
-
-biomes.factor.selected <- function(idx.select = c(4:7, 9)){
-
-all.fact <- factor(c("Subtropical desert", "Temperate grassland desert", "Tundra",
-                     "Boreal forest", "Temperate forest", "Temperate rain forest",
-                     "Tropical forest savanna", "Tropical rain forest", "Woodland shrubland"))
-levels.name <- levels(all.fact)
-levels.name[levels.name == 'Tundra'] <- 'Boreal forest'
-levels.name[levels.name == 'Subtropical desert'] <- 'Temperate grassland desert'
-levels.name[levels.name == "Tropical rain forest"] <- "Tropical forest savanna"
-levels(all.fact) <- levels.name
-
+ param <- list$lmer.summary$fixed.coeff.E
+ remaining <- grep(paste0(':',var), names(param))
+ boreal <- seq_len(length(param))[names(param) == var]
+ select <- c(boreal, remaining)
+ ## create design matrix
+ ff <-  ~ biomes.id
+ mm <- model.matrix(ff, data.frame(biomes.id = biomes.vec))
+ # compute mean and std based on Bolker post
+ param.biomes <- drop(mm %*% param[select])
+ names(param.biomes) <-  biomes.vec
+ std.biomes <- sqrt(diag(mm %*% tcrossprod(list$vcov[select, select],mm)))
+ return(list(fixed.biomes = param.biomes,
+             fixed.biomes.std = std.biomes))
+}
+
+
+
+biomes.factor.selected <- function(idx.select = c(4:7, 9)){#ok
+ all.fact <- factor(c("Subtropical desert",
+                      "Temperate grassland desert",
+                      "Tundra",
+                      "Boreal forest",
+                      "Temperate forest",
+                      "Temperate rain forest",
+                      "Tropical forest savanna",
+                      "Tropical rain forest",
+                      "Woodland shrubland"))
+ levels.name <- levels(all.fact)
+ levels.name[levels.name == 'Tundra'] <- 'Boreal forest'
+ levels.name[levels.name == 'Subtropical desert'] <- 'Temperate grassland desert'
+ levels.name[levels.name == "Tropical rain forest"] <- "Tropical forest savanna"
+ levels(all.fact) <- levels.name
 return(all.fact[idx.select])
 }
 
-fun.biomes.names <- function(){
+fun.biomes.names <- function(){#OK
 c("Subtropical desert" = "desert",
   "Temperate grassland desert" = "desert",
   "Tundra" = "tundra",
@@ -1158,231 +964,99 @@ c("Subtropical desert" = "desert",
 }
 
 
-plot.param.biomes.fixed <-  function(list.res,
-                        model,
-                        biomes.names = fun.biomes.names(),
-                        biomes = biomes.factor.selected(),
-                        traits = c('Wood.density', 'SLA', 'Max.height'),
-                        traits.names = c(Wood.density= 'Wood density',
-                                         SLA = 'Specific leaf area',
-                                         Max.height = 'Maximum height'),
-                        param.vec = c("Tf", "sumTnBn",
-                                      "sumTfBn", "sumTnTfBn.abs"),
-                        param.names = c('Direct trait',
-                                        'Compet effect x trait',
-                                        'Compet response x trait',
-                                        'Compet x trait dissimilarity'),
-                        param.print = 1:5,
-                        col.names = fun.col.param() ,
-                        data.type = "all.no.log",
-                        col.vec,
-                        pch.vec,
-                        names.bio,
-                        legend.pos = 1,
-                        add.param.descrip.TF = 1,
-                               ...){
- if(! add.param.descrip.TF %in% 0:2) stop("add.param.descrip.TF need to be in 0 1 2")
- col.vec[2] <-  col.vec[1]
- biomes.c <-  as.character(biomes)
- big.m <-  3.0
- small.m <-  0
- legend.m <- 1.9
- if (legend.pos==1){
- wid <-  c(big.m ,0, small.m) +
-         rep((14-big.m-small.m-legend.m)/3, each= 3)
- m <- matrix(c(1:4), 1, 4)
-layout(m, widths=c(wid, legend.m))
-}
-if (legend.pos==2){
- wid <-  c(big.m ,0, small.m) +
-         rep((14-big.m-small.m)/3, each= 3)
- m <- matrix(c(1:3, 4, 4, 4), 2, 3, byrow = TRUE)
-layout(m, widths=c(wid), height = c(5,1))
-}
-if(legend.pos==0) {
- wid <-  c(big.m ,0, small.m) +
-         rep((14-big.m-small.m)/3, each= 3)
- m <- matrix(c(1:3), 1, 3)
-layout(m, widths=c(wid))
-}
-for (i in traits){
-    list.temp <- list.res[[paste0(data.type, "_", i ,
-                                 "_", model)]]
-## NEED VAR
-  for (n.vars in seq_len(length(param.vec))){
-        list.fixed <- fun.get.fixed.biomes(param.vec[n.vars], list.temp,
-                                           biomes.vec = biomes)
-        param.mean <- list.fixed$fixed.biomes
-        param.std <- list.fixed$fixed.biomes.std
-    if(i == traits[1]) {
-        par(mai=c(0.8, big.m,0.7,0), xpd = TRUE)
-    }else{
-        par(mai=c(0.8,0,0.7,0), xpd = TRUE)
-    }
-    if(i == traits[length(traits)]) {
-        par(mai=c(0.8,0,0.7,small.m), xpd = TRUE)
-    }
-    seq.jitter <- seq(25, -25, length.out = length(biomes))/120
-    if(n.vars == 1){
-        plot(param.mean, seq.jitter+n.vars,
-             yaxt = 'n', xlab = NA, ylab = NA,
-             , pch = pch.vec[biomes.c] , cex = 2, cex.axis = 1.5, cex.lab = 1.5,
-             ylim = range(1-0.21, length(param.vec)+0.21),
-             col = col.vec[biomes.c], ...)
-        if(i == traits[2]) mtext('Standardized coefficients', side=1,  cex =1.5,
-                             line = 4)
-    }
-    if(n.vars != 1){
-        points(param.mean, seq.jitter+n.vars,
-               col = col.vec[biomes.c], pch = pch.vec[biomes.c], cex = 2)}
-        mtext(traits.names[i], side=3,  cex =1.5, line = 1)
-        box(lwd= 2)
-        lines(c(0, 0), c(1-0.35, length(param.vec)+0.35), lwd= 1)
-        if(i == traits[1]) {
-            lapply(1:length(param.vec),
-                   fun.axis.one.by.one,
-                   side = 2,
-                   labels = param.names,
-                   cols.vec = col.names[param.vec],
-                   cex.axis = 3)
-                   if(add.param.descrip.TF == 2){