Revision 1d0a0b81
Added by Benoit Parmentier over 11 years ago
| climate/research/oregon/interpolation/GAM_fusion_function_multisampling_validation_metrics.R | ||
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#Function used in the script |
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calculate_accuracy_metrics<-function(i,list_param){
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library(plyr) |
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### Caculate accuracy metrics |
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calc_val_metrics<-function(x,y){
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#This functions calculates accurayc metrics on given two vectors. |
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boxplot_from_tb <-function(tb_diagnostic,metric_names,out_prefix){
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#now boxplots and mean per models |
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mod_names<-unique(tb_diagnostic$pred_mod) #models that have accuracy metrics
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mod_names<-sort(unique(tb_diagnostic$pred_mod)) #models that have accuracy metrics
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t<-melt(tb_diagnostic, |
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#measure=mod_var, |
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id=c("date","pred_mod","prop"),
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median_tb<-cast(t,pred_mod~variable,median) |
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tb<-tb_diagnostic |
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tb_mod_list<-vector("list",length(mod_names))
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for(i in 1:length(mod_names)){ # Reorganizing information in terms of metrics
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mod_name_tb<-paste("tb_",mod_names[i],sep="")
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tb_mod<-subset(tb, pred_mod==mod_names[i]) |
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assign(mod_name_tb,tb_mod) |
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tb_mod_list[[i]]<-tb_mod |
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} |
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#mod_names<-sort(unique(tb$pred_mod)) #kept for clarity |
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tb_mod_list<-lapply(mod_names, function(k) subset(tb, pred_mod==k)) #this creates a list of 5 based on models names |
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names(tb_mod_list)<-mod_names |
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mod_metrics<-do.call(cbind,tb_mod_list) |
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#mod_metrics<-do.call(cbind,tb_mod_list) |
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mod_metrics<-do.call(cbindX,tb_mod_list) |
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test_names<-lapply(1:length(mod_names),function(k) paste(names(tb_mod_list[[1]]),mod_names[k],sep="_")) |
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names(mod_metrics)<-unlist(test_names) |
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rows_total<-lapply(tb_mod_list,nrow) |
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for (j in 1:length(metric_names)){
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metric_ac<-metric_names[j] |
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mod_pat<-glob2rx(paste("*.",metric_ac,sep=""))
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mod_pat<-glob2rx(paste(metric_ac,"_*",sep=""))
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mod_var<-grep(mod_pat,names(mod_metrics),value=TRUE) # using grep with "value" extracts the matching names |
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#browser() |
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test<-mod_metrics[mod_var] |
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png(paste("boxplot_metric_",metric_ac, out_prefix,".png", sep=""))
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boxplot(test,outline=FALSE,horizontal=FALSE,cex=0.5, |
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ylab=paste(metric_ac,"in degree C",sep=" ")) |
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#legend("bottomleft",legend=paste(names(rows_total),":",rows_total,sep=""),cex=0.7,bty="n")
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title(as.character(t(paste(t(names(rows_total)),":",rows_total,sep=""))),cex=0.8) |
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dev.off() |
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} |
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avg_tb$n<-rows_total #total number of predictions on which the mean is based |
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median_tb$n<-rows_total |
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summary_obj<-list(avg_tb,median_tb) |
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return(summary_obj) |
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} |
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#Add code here... |
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} |
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#################################### |
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############ END OF SCRIPT ######### |
Also available in: Unified diff
VValidation script, average over 365 days take into account no predictions and number of predictions