Revision 75b4e894
Added by Benoit Parmentier almost 9 years ago
| climate/research/oregon/interpolation/global_run_scalingup_assessment_part2.R | ||
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#Analyses, figures, tables and data are also produced in the script. |
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#AUTHOR: Benoit Parmentier |
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#CREATED ON: 03/23/2014 |
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#MODIFIED ON: 02/03/2016
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#MODIFIED ON: 02/07/2016
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#Version: 5 |
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#PROJECT: Environmental Layers project |
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#COMMENTS: analyses for run 10 global analyses,all regions 1500x4500km with additional tiles to increase overlap |
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tb_month_s <- read.table(file.path(in_dir,basename(df_assessment_files$files[3])),sep=",") |
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pred_data_month_info <- read.table(file.path(in_dir, basename(df_assessment_files$files[10])),sep=",") |
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pred_data_day_info <- read.table(file.path(in_dir, basename(df_assessment_files$files[11])),sep=",") |
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df_tile_processed <- read.table(file.path(in_dir, basename(df_assessment_files$files[12])),sep=",") |
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df_tile_processed <- read.table(file.path(in_dir, basename(df_assessment_files$files[12])),stringsAsFactors=F,sep=",")
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##Screen for non shapefiles tiles due to dir |
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df_tile_processed <- df_tile_processed[!is.na(df_tile_processed$shp_files),] |
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#add column for tile size later on!!! |
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tb$pred_mod <- as.character(tb$pred_mod) |
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png(filename=paste("Figure1_tile_processed_region_",region_name,"_",out_suffix,".png",sep=""),
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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plot(reg_layer) |
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plot(reg_layer,border="black",usePolypath = FALSE)
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#Add polygon tiles... |
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for(i in 1:length(shps_tiles)){
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shp1 <- shps_tiles[[i]] |
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as.character(unique(test$tile_id)) #141 tiles |
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dim(subset(test,test$predicted==365 & test$pred_mod=="mod1")) |
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histogram(subset(test, test$pred_mod=="mod1")$predicted) |
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#histogram(subset(test, test$pred_mod=="mod1")$predicted)
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unique(subset(test, test$pred_mod=="mod1")$predicted) |
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table((subset(test, test$pred_mod=="mod1")$predicted)) |
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| ... | ... | |
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col_mfrow <- 1 |
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row_mfrow <- 1 |
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png(filename=paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",out_suffix,".png",sep=""),
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fig_filename <- paste("Figure8a_boxplot_overall_separated_by_region_with_oultiers_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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p<- bwplot(rmse~pred_mod | reg, data=tb, |
| ... | ... | |
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print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure8a_boxplot_overall_accuracy_by_model_separated_by_region_with_oultiers_",out_suffix,".png",sep="")
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list_outfiles[[counter_fig+1]] <- fig_filename
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counter_fig <- counter_fig + 1 |
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## Figure 8b |
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png(filename=paste("Figure8b_boxplot_overall_separated_by_region_scaling_",out_suffix,".png",sep=""),
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fig_filename <- paste("Figure8b_boxplot_overall_separated_by_region_scaling_",out_suffix,".png",sep="")
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png(filename=fig_filename, |
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width=col_mfrow*res_pix,height=row_mfrow*res_pix) |
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#boxplot(rmse~pred_mod,data=tb,ylim=c(0,5),outline=FALSE)#,names=tb$pred_mod) |
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print(p) |
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dev.off() |
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list_outfiles[[counter_fig+1]] <- paste("Figure8b_boxplot_overall_accuracy_by_model_separated_by_region_scaling_",out_suffix,".png",sep="")
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list_outfiles[[counter_fig+1]] <- fig_filename
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counter_fig <- counter_fig + 1 |
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| ... | ... | |
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} |
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r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[20]])
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r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[21]])
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r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[20]])
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r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[21]])
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r22 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod1",metric_name,region_name,year_predicted,list_outfiles[[22]])
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r23 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod1",metric_name,region_name,year_predicted,list_outfiles[[23]])
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r24 <-c("figure 8c","Boxplot overall accuracy separated by region with outliers","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[24]])
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r25 <-c("figure 8d","Boxplot overall accuracy separated by region with scaling","mod_kr",metric_name,region_name,year_predicted,list_outfiles[[25]])
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##################################################### |
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#### Figure 9: plotting boxplot by year and regions ########### |
Also available in: Unified diff
adding figures 8 related to summary of accuracy