Revision 992b2eeb
Added by Benoit Parmentier almost 11 years ago
| climate/research/oregon/interpolation/analyses_papers_methods_comparison_part5.R | ||
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| 75 | 75 |
in_dir8 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_kriging_fus_lst_comb3_09122013" |
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in_dir9 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_kriging_fus_lst_comb3_09132013" |
| 77 | 77 |
in_dir10 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_kriging_fus_lst_comb3_09142013" |
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in_dir11 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_gwr_CAI_lst_comb3_09162013" |
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in_dir12 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_gwr_CAI_lst_comb3_09172013" |
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in_dir13 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_gwr_cai_lst_comb3_09282013" |
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in_dir14 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_gwr_fus_lst_comb3_09232013" |
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in_dir15 <-"/data/project/layers/commons/Oregon_interpolation/output_data_365d_gwr_fus_lst_comb3_09262013" |
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| 78 | 83 |
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#better as list and load one by one specific element from the object |
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raster_prediction_obj1 <-load_obj(file.path(in_dir1,"raster_prediction_obj_gam_CAI_dailyTmax_365d_gam_CAI_lst_comb3_08312013.RData")) |
| 80 | 86 |
raster_prediction_obj2 <-load_obj(file.path(in_dir2,"raster_prediction_obj_gam_CAI_dailyTmax_365d_gam_CAI_lst_comb3_09012013.RData")) |
| 81 | 87 |
raster_prediction_obj3 <-load_obj(file.path(in_dir3,"raster_prediction_obj_gam_CAI_dailyTmax_365d_gam_CAI_lst_comb3_09032013.RData")) |
| ... | ... | |
| 87 | 93 |
raster_prediction_obj8 <-load_obj(file.path(in_dir8,"raster_prediction_obj_kriging_fusion_dailyTmax_365d_kriging_fus_lst_comb3_09122013.RData")) |
| 88 | 94 |
raster_prediction_obj9 <-load_obj(file.path(in_dir9,"raster_prediction_obj_kriging_fusion_dailyTmax_365d_kriging_fus_lst_comb3_09132013.RData")) |
| 89 | 95 |
raster_prediction_obj10 <-load_obj(file.path(in_dir10,"raster_prediction_obj_kriging_fusion_dailyTmax_365d_kriging_fus_lst_comb3_09142013.RData")) |
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raster_prediction_obj11 <-load_obj(file.path(in_dir11,"raster_prediction_obj_gwr_CAI_dailyTmax_365d_gwr_CAI_lst_comb3_09162013.RData")) |
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raster_prediction_obj12 <-load_obj(file.path(in_dir12,"raster_prediction_obj_gwr_CAI_dailyTmax_365d_gwr_CAI_lst_comb3_09172013.RData")) |
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raster_prediction_obj13 <-load_obj(file.path(in_dir13,"raster_prediction_obj_gwr_CAI_dailyTmax_365d_gwr_cai_lst_comb3_09282013.RData")) |
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raster_prediction_obj14 <-load_obj(file.path(in_dir14,"raster_prediction_obj_gwr_fusion_dailyTmax_365d_gwr_fus_lst_comb3_09232013.RData")) |
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raster_prediction_obj15 <-load_obj(file.path(in_dir15,"raster_prediction_obj_gwr_fusion_dailyTmax_365d_gwr_fus_lst_comb3_09262013.RData")) |
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#raster_prediction_obj_gwr_CAI_dailyTmax_365d_gwr_cai_lst_comb3_09282013.RData |
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#raster_prediction_obj_gwr_fusion_dailyTmax_365d_gwr_fus_lst_comb3_09262013.RData |
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| 90 | 104 |
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out_dir<-"/home/parmentier/Data/IPLANT_project/paper_multitime_scale__analyses_tables_fig_09032013" |
| 92 | 106 |
setwd(out_dir) |
| 93 | 107 |
y_var_name <- "dailyTmax" |
| 94 | 108 |
y_var_month <- "TMax" |
| 95 | 109 |
#y_var_month <- "LSTD_bias" |
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out_suffix <- "_OR_09132013"
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out_suffix <- "_OR_09292013"
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#script_path<-"/data/project/layers/commons/data_workflow/env_layers_scripts/" |
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#### FUNCTION USED IN SCRIPT |
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function_analyses_paper <-"contribution_of_covariates_paper_interpolation_functions_09092013.R"
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function_analyses_paper <-"contribution_of_covariates_paper_interpolation_functions_09232013.R"
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script_path<-"/home/parmentier/Data/IPLANT_project/env_layers_scripts/" #path to script |
| 103 | 117 |
source(file.path(script_path,function_analyses_paper)) #source all functions used in this script. |
| ... | ... | |
| 112 | 126 |
tb_s_gam_CAI <-rbind(raster_prediction_obj1$tb_diagnostic_s,raster_prediction_obj2$tb_diagnostic_s,raster_prediction_obj3$tb_diagnostic_s) |
| 113 | 127 |
#prop_obj_gam_CAI_v <- calc_stat_prop_tb_diagnostic(names_mod,names_id,tb_v) |
| 114 | 128 |
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tb_mv_gwr_CAI <-rbind(raster_prediction_obj11$tb_month_diagnostic_v,raster_prediction_obj12$tb_month_diagnostic_v,raster_prediction_obj13$tb_month_diagnostic_v) |
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tb_ms_gwr_CAI <-rbind(raster_prediction_obj11$tb_month_diagnostic_s,raster_prediction_obj12$tb_month_diagnostic_s,raster_prediction_obj13$tb_month_diagnostic_s) |
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tb_v_gwr_CAI <-rbind(raster_prediction_obj11$tb_diagnostic_v,raster_prediction_obj12$tb_diagnostic_v,raster_prediction_obj13$tb_diagnostic_v) |
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tb_s_gwr_CAI <-rbind(raster_prediction_obj11$tb_diagnostic_s,raster_prediction_obj12$tb_diagnostic_s,raster_prediction_obj13$tb_diagnostic_s) |
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tb_mv_kriging_CAI <- raster_prediction_obj4$tb_month_diagnostic_v |
| 116 | 136 |
tb_ms_kriging_CAI <- raster_prediction_obj4$tb_month_diagnostic_s |
| 117 | 137 |
|
| ... | ... | |
| 126 | 146 |
tb_v_gam_fus <-rbind(raster_prediction_obj5$tb_diagnostic_v,raster_prediction_obj6$tb_diagnostic_v,raster_prediction_obj7$tb_diagnostic_v) |
| 127 | 147 |
tb_s_gam_fus <-rbind(raster_prediction_obj5$tb_diagnostic_s,raster_prediction_obj6$tb_diagnostic_s,raster_prediction_obj7$tb_diagnostic_s) |
| 128 | 148 |
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tb_mv_gwr_fus <-rbind(raster_prediction_obj14$tb_month_diagnostic_v,raster_prediction_obj15$tb_month_diagnostic_v) |
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tb_ms_gwr_fus <-rbind(raster_prediction_obj14$tb_month_diagnostic_s,raster_prediction_obj15$tb_month_diagnostic_s) |
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tb_v_gwr_fus <-rbind(raster_prediction_obj14$tb_diagnostic_v,raster_prediction_obj15$tb_diagnostic_v) |
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tb_s_gwr_fus <-rbind(raster_prediction_obj14$tb_diagnostic_s,raster_prediction_obj15$tb_diagnostic_s) |
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tb_mv_kriging_fus <-rbind(raster_prediction_obj8$tb_month_diagnostic_v,raster_prediction_obj9$tb_month_diagnostic_v,raster_prediction_obj10$tb_month_diagnostic_v) |
| 130 | 156 |
tb_ms_kriging_fus <-rbind(raster_prediction_obj8$tb_month_diagnostic_s,raster_prediction_obj9$tb_month_diagnostic_s,raster_prediction_obj10$tb_month_diagnostic_s) |
| 131 | 157 |
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tb_v_kriging_fus <-rbind(raster_prediction_obj8$tb_diagnostic_v,raster_prediction_obj9$tb_diagnostic_v,raster_prediction_obj10$tb_diagnostic_v) |
| 133 | 159 |
tb_s_kriging_fus <-rbind(raster_prediction_obj8$tb_diagnostic_s,raster_prediction_obj9$tb_diagnostic_s,raster_prediction_obj10$tb_diagnostic_s) |
| 134 | 160 |
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#list_tb <-list(tb_v_gam_CAI,tb_v_kriging_CAI,tb_s_gam_CAI,tb_s_kriging_CAI,tb_mv_gam_CAI,tb_mv_kriging_CAI,tb_ms_gam_CAI,tb_ms_kriging_CAI) #Add fusion here |
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#names(list_tb) <- c("tb_v_gam_CAI","tb_v_kriging_CAI","tb_s_gam_CAI","tb_s_kriging_CAI","tb_mv_gam_CAI","tb_mv_kriging_CAI","tb_ms_gam_CAI","tb_ms_kriging_CAI") #Add fusion here
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list_tb <- list(tb_v_gam_CAI,tb_v_kriging_CAI,tb_v_gwr_CAI,tb_s_gam_CAI,tb_s_kriging_CAI,tb_s_gwr_CAI, |
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tb_mv_gam_CAI,tb_mv_kriging_CAI,tb_mv_gwr_CAI,tb_ms_gam_CAI,tb_ms_kriging_CAI,tb_ms_gwr_CAI, |
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tb_v_gam_fus,tb_v_kriging_fus,tb_v_gwr_fus,tb_s_gam_fus,tb_s_kriging_fus,tb_s_gwr_fus, |
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tb_mv_gam_fus,tb_mv_kriging_fus,tb_mv_gwr_fus,tb_ms_gam_fus,tb_ms_kriging_fus,tb_ms_gwr_fus) |
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names(list_tb) <- c("tb_v_gam_CAI","tb_v_kriging_CAI","tb_v_gwr_CAI","tb_s_gam_CAI","tb_s_kriging_CAI","tb_s_gwr_CAI",
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"tb_mv_gam_CAI","tb_mv_kriging_CAI","tb_mv_gwr_CAI","tb_ms_gam_CAI","tb_ms_kriging_CAI","tb_ms_gwr_CAI", |
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"tb_v_gam_fus","tb_v_kriging_fus","tb_v_gwr_fus","tb_s_gam_fus","tb_s_kriging_fus","tb_s_gwr_fus", |
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"tb_mv_gam_fus","tb_mv_kriging_fus","tb_mv_gwr_fus","tb_ms_gam_fus","tb_ms_kriging_fus","tb_ms_gwr_fus") |
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| 137 | 170 |
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list_tb <-list(tb_v_gam_CAI,tb_v_kriging_CAI,tb_s_gam_CAI,tb_s_kriging_CAI,tb_mv_gam_CAI,tb_mv_kriging_CAI,tb_ms_gam_CAI,tb_ms_kriging_CAI, #Add fusion here
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tb_v_gam_fus,tb_v_kriging_fus,tb_s_gam_fus,tb_s_kriging_fus,tb_mv_gam_fus,tb_mv_kriging_fus,tb_ms_gam_fus,tb_ms_kriging_fus) #Add fusion here |
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names(list_tb) <- c("tb_v_gam_CAI","tb_v_kriging_CAI","tb_s_gam_CAI","tb_s_kriging_CAI","tb_mv_gam_CAI","tb_mv_kriging_CAI","tb_ms_gam_CAI","tb_ms_kriging_CAI", #Add fusion here
|
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"tb_v_gam_fus","tb_v_kriging_fus","tb_s_gam_fus","tb_s_kriging_fus","tb_mv_gam_fus","tb_mv_kriging_fus","tb_ms_gam_fus","tb_ms_kriging_fus") #Add fusion here |
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#list_tb <-list(tb_v_gam_CAI,tb_v_kriging_CAI,tb_v_gwr_CAI,tb_s_gam_CAI,tb_s_kriging_CAI,tb_s_gwr_CAI,tb_mv_gam_CAI,tb_mv_kriging_CAI,tb_ms_gam_CAI,tb_ms_kriging_CAI,tb_ms_gwr_CAI,tb_ms_gwr_CAI #Add fusion here
|
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# tb_v_gam_fus,tb_v_kriging_fus,tb_s_gam_fus,tb_s_kriging_fus,tb_mv_gam_fus,tb_mv_kriging_fus,tb_ms_gam_fus,tb_ms_kriging_fus) #Add fusion here
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#names(list_tb) <- c("tb_v_gam_CAI","tb_v_kriging_CAI","tb_v_gwr_CAI","tb_s_gam_CAI","tb_s_kriging_CAI","tb_s_gwr_CAI","tb_mv_gam_CAI","tb_mv_kriging_CAI","tb_ms_gam_CAI","tb_ms_kriging_CAI","tb_ms_gwr_CAI","tb_ms_gwr_CAI" #Add fusion here
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# "tb_v_gam_fus","tb_v_kriging_fus","tb_s_gam_fus","tb_s_kriging_fus","tb_mv_gam_fus","tb_mv_kriging_fus","tb_ms_gam_fus","tb_ms_kriging_fus") #Add fusion here
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##### DAILY AVERAGE ACCURACY : PLOT AND DIFFERENCES... |
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##### DAILY AVERAGE ACCURACY : PLOT AND DIFFERENCES...Cd
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for(i in 1:length(list_tb)){
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i <- i+1 |
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#i <- i+1
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tb <-list_tb[[i]] |
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plot_name <- names(list_tb)[i] |
| 149 | 182 |
pat_str <- "tb_m" |
| ... | ... | |
| 167 | 200 |
png(paste("Figure__accuracy_rmse_prop_month_",plot_name,out_suffix,".png", sep=""),
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| 168 | 201 |
height=480*layout_m[1],width=480*layout_m[2]) |
| 169 | 202 |
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xyplot(as.formula(plot_formula),group=pred_mod,type="b", |
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p<- xyplot(as.formula(plot_formula),group=pred_mod,type="b",
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data=avg_tb, |
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main=paste("rmse ",plot_name,sep=" "),
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pch=1:length(avg_tb$pred_mod), |
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par.settings=list(superpose.symbol = list( |
| 175 | 208 |
pch=1:length(avg_tb$pred_mod))), |
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auto.key=list(columns=5)) |
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print(p) |
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dev.off() |
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|
| ... | ... | |
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metric_names <- c("mae","rmse","me","r")
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| 200 | 234 |
diff_kriging_CAI <- diff_df(tb_s_kriging_CAI,tb_v_kriging_CAI,metric_names) |
| 201 | 235 |
diff_gam_CAI <- diff_df(tb_s_gam_CAI,tb_v_gam_CAI,metric_names) |
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diff_gwr_CAI <- diff_df(tb_s_gwr_CAI,tb_v_gwr_CAI,metric_names) |
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layout_m<-c(1,1) #one row two columns |
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par(mfrow=layout_m) |
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boxplot(diff_kriging_CAI$rmse,diff_gam_CAI$rmse,names=c("kriging_CAI","gam_CAI"),
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png(paste("Figure__accuracy_rmse_prop_month_",plot_name,out_suffix,".png", sep=""),
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height=480*layout_m[1],width=480*layout_m[2]) |
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boxplot(diff_kriging_CAI$rmse,diff_gam_CAI$rmse,diff_gwr_CAI$rmse,names=c("kriging_CAI","gam_CAI","gwr_CAI"),
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main="Difference between training and testing daily rmse") |
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dev.off() |
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#remove prop 0, |
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diff_kriging_CAI <- diff_df(tb_s_kriging_CAI[tb_s_kriging_CAI$prop_month!=0,],tb_v_kriging_CAI[tb_v_kriging_CAI$prop_month!=0,],metric_names) |
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diff_gam_CAI <- diff_df(tb_s_gam_CAI[tb_s_gam_CAI$prop_month!=0,],tb_v_gam_CAI[tb_v_gam_CAI$prop_month!=0,],metric_names) |
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diff_gwr_CAI <- diff_df(tb_s_gwr_CAI[tb_s_gwr_CAI$prop_month!=0,],tb_v_gwr_CAI[tb_v_gwr_CAI$prop_month!=0,],metric_names) |
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boxplot(diff_kriging_CAI$rmse,diff_gam_CAI$rmse,diff_gwr_CAI$rmse,names=c("kriging_CAI","gam_CAI","gwr_CAI"),
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main="Difference between training and testing daily rmse") |
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#now monthly accuracy |
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metric_names <- c("mae","rmse","me","r")
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diff_kriging_m_CAI <- diff_df(tb_ms_kriging_CAI,tb_mv_kriging_CAI,metric_names) |
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diff_gam_m_CAI <- diff_df(tb_ms_gam_CAI,tb_mv_gam_CAI,metric_names) |
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diff_kriging_m_CAI <- diff_df(tb_ms_kriging_CAI[tb_ms_kriging_CAI$prop!=0,],tb_mv_kriging_CAI,metric_names) |
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diff_gam_m_CAI <- diff_df(tb_ms_gam_CAI[tb_ms_gam_CAI$prop!=0,],tb_mv_gam_CAI,metric_names) |
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diff_gwr_m_CAI <- diff_df(tb_ms_gwr_CAI[tb_ms_gwr_CAI$prop!=0,],tb_mv_gwr_CAI,metric_names) |
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layout_m<-c(1,1) #one row two columns |
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par(mfrow=layout_m) |
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boxplot(diff_kriging_m_CAI$rmse,diff_gam_m_CAI$rmse,names=c("kriging_CAI","gam_CAI"),
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png(paste("Figure__accuracy_rmse_prop_month_",plot_name,out_suffix,".png", sep=""),
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height=480*layout_m[1],width=480*layout_m[2]) |
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boxplot(diff_kriging_m_CAI$rmse,diff_gam_m_CAI$rmse,diff_gwr_m_CAI$rmse,names=c("kriging_CAI","gam_CAI","gwr_CAI"),
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main="Difference between training and monhtly testing rmse") |
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dev.off() |
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#boxplot(diff_kriging_m_CAI$rmse,diff_gam_m_CAI$rmse,diff_gwr_CAI,names=c("kriging_CAI","gam_CAI","gwr_CAI"),
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# main="Difference between training and monhtly testing rmse") |
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### For fusion |
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metric_names <- c("mae","rmse","me","r")
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diff_kriging_fus <- diff_df(tb_s_kriging_fus,tb_v_kriging_fus,metric_names) |
| 217 | 276 |
diff_gam_fus <- diff_df(tb_s_gam_fus,tb_v_gam_fus,metric_names) |
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diff_gwr_fus <- diff_df(tb_s_gwr_fus,tb_v_gwr_fus,metric_names) |
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layout_m<-c(1,1) #one row two columns |
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par(mfrow=layout_m) |
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boxplot(diff_kriging_fus$rmse,diff_gam_fus$rmse,names=c("kriging_fus","gam_fus"),
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png(paste("Figure__accuracy_rmse_prop_month_",plot_name,out_suffix,".png", sep=""),
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height=480*layout_m[1],width=480*layout_m[2]) |
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boxplot(diff_kriging_fus$rmse,diff_gam_fus$rmse,diff_gwr_fus$rmse,names=c("kriging_fus","gam_fus","gwr_fus"),
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main="Difference between training and testing daily rmse") |
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dev.off() |
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metric_names <- c("mae","rmse","me","r")
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diff_kriging_m_fus <- diff_df(tb_ms_kriging_fus,tb_mv_kriging_fus,metric_names) |
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diff_gam_m_fus <- diff_df(tb_ms_gam_fus,tb_mv_gam_fus,metric_names) |
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diff_kriging_m_fus <- diff_df(tb_ms_kriging_fus[tb_ms_kriging_fus$prop!=0,],tb_mv_kriging_fus[tb_mv_kriging_fus$prop!=0,],metric_names) |
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diff_gam_m_fus <- diff_df(tb_ms_gam_fus[tb_ms_gam_fus$prop!=0,],tb_mv_gam_fus[tb_mv_gam_fus$prop!=0,],metric_names) |
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diff_gwr_m_fus <- diff_df(tb_ms_gwr_fus[tb_ms_gwr_fus$prop!=0,],tb_mv_gwr_fus[tb_mv_gwr_fus$prop!=0,],metric_names) |
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layout_m<-c(1,1) #one row two columns |
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par(mfrow=layout_m) |
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boxplot(diff_kriging_m_fus$rmse,diff_gam_m_fus$rmse,names=c("kriging_fus","gam_fus"),
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png(paste("Figure__accuracy_rmse_prop_month_",plot_name,out_suffix,".png", sep=""),
|
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height=480*layout_m[1],width=480*layout_m[2]) |
|
| 298 |
boxplot(diff_kriging_m_fus$rmse,diff_gam_m_fus$rmse,diff_gwr_m_fus$rmse, names=c("kriging_fus","gam_fus","gwr_fus"),
|
|
| 227 | 299 |
main="Difference between training and testing FUS rmse") |
| 300 |
dev.off() |
|
| 228 | 301 |
|
| 229 | 302 |
### NOW PLOT OF COMPARISON BETWEEN Kriging and GAM |
| 230 | 303 |
|
| ... | ... | |
| 235 | 308 |
tb_v_kriging_CAI |
| 236 | 309 |
tb_v_kriging_fus |
| 237 | 310 |
|
| 238 |
methods_names <- c("tb_v_gam_CAI","tb_v_gam_fus","tb_v_kriging_CAI","tb_v_kriging_fus")
|
|
| 311 |
methods_names <- c("tb_v_gam_CAI","tb_v_gam_fus","tb_v_kriging_CAI","tb_v_kriging_fus","tb_v_gwr_CAI","tb_v_gwr_fus")
|
|
| 239 | 312 |
list_prop_obj <- vector("list",length=length(methods_names))
|
| 240 | 313 |
for(i in 1:length(methods_names)){
|
| 241 | 314 |
tb <- list_tb[[methods_names[i]]] |
| ... | ... | |
| 248 | 321 |
|
| 249 | 322 |
ac_prop_tb_list <- extract_list_from_list_obj(list_prop_obj,"avg_tb") |
| 250 | 323 |
nb_rows <- sapply(ac_prop_tb_list,FUN=nrow) |
| 251 |
method_interp_names<-c("gam_CAI","gam_fus","kriging_CAI","kriging_fus")
|
|
| 324 |
method_interp_names<-c("gam_CAI","gam_fus","kriging_CAI","kriging_fus","gwr_CAI","gwr_fus")
|
|
| 252 | 325 |
for(i in 1:length(methods_names)){
|
| 253 | 326 |
avg_tb<-ac_prop_tb_list[[i]] |
| 254 | 327 |
avg_tb$method_interp <-rep(x=method_interp_names[i],times=nb_rows[i]) |
| ... | ... | |
| 258 | 331 |
|
| 259 | 332 |
#names(ac_prop_tb_list) <- names(list_prop_obj) |
| 260 | 333 |
|
| 261 |
t44 <- do.call(rbind,ac_prop_tb_list) |
|
| 334 |
t44 <- do.call(rbind,ac_prop_tb_list) #contains all accuracy by method, proportion, model, sample etc.
|
|
| 262 | 335 |
View(t44) |
| 263 | 336 |
|
| 264 |
t44[which.min(t44$rmse),] |
|
| 337 |
t44[which.min(t44$rmse),] #Find the mimum rmse across all models and methods...
|
|
| 265 | 338 |
|
| 266 | 339 |
test <- t44[order(t44$rmse),] |
| 340 |
test[1:24,] |
|
| 267 | 341 |
|
| 268 | 342 |
test2<-test[test$method_interp%in% c("gam_fus","gam_CAI"),]
|
| 269 | 343 |
test2[1:24,] |
Also available in: Unified diff
analyses for multi-time scale paper, monthly hold-out proportions (0-70%) for all methods