Revision f684a69f
Added by Benoit Parmentier over 10 years ago
| climate/research/oregon/interpolation/multi_timescale_paper_interpolation.R | ||
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#### FUNCTION USED IN SCRIPT |
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function_analyses_paper1 <-"contribution_of_covariates_paper_interpolation_functions_07182014.R" #first interp paper |
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function_analyses_paper2 <-"multi_timescales_paper_interpolation_functions_08122014.R"
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function_analyses_paper2 <-"multi_timescales_paper_interpolation_functions_08132014.R"
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############################## |
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#### Parameters and constants |
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table3_paper <- subset(table3_paper, |
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select=grep("gam_fss",names(table3_paper),invert=T,value=T))
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list_tb <-lapply(list_raster_oj_files,FUN=function(x){x<-load_obj(x);x[["tb_diagnostic_v"]]})
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list_tb <-lapply(list_raster_obj_files,FUN=function(x){x<-load_obj(x);x[["tb_diagnostic_v"]]})
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stat<-"sd" |
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training <- "FALSE" |
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file_name<-paste("table6_correlation_multi_timescale_paper","_",out_prefix,".txt",sep="")
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write.table(table6,file=file_name,sep=",") |
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### Additiona information: |
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#model with LST and elev |
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raster_obj <- load_obj(list_raster_obj_files$gam_CAI) |
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list_tb_mod_month <- vector("list",length=3)
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interp_method_selected <- c("gam_CAI","kriging_CAI","gwr_CAI")
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for(i in 1:3){
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raster_obj <- load_obj(list_raster_obj_files[[interp_method_selected[i]]]) |
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tb1_mod1_month <- raster_obj$summary_month_metrics_v[[1]] #note that this is for model1 |
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tb_mod_month <- raster_obj$summary_month_metrics_v[1:7]#note that this is for model1 |
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tb_mod_month <- as.data.frame(do.call(rbind,tb_mod_month)) |
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tb_mod_month <- tb_mod_month[,c("month","rmse","pred_mod")]
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tb_mod_month$m <- as.numeric(tb_mod_month$month) |
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xyplot(rmse~m, group=pred_mod,data=tb_mod_month,type="b", |
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auto.key=list(space = "top", cex=1.0,columns=7)) |
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test <- list( |
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tb_mod_month[tb_mod_month$pred_mod=="mod1",c("rmse")],
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tb_mod_month[tb_mod_month$pred_mod=="mod2",c("rmse")],
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tb_mod_month[tb_mod_month$pred_mod=="mod3",c("rmse")],
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tb_mod_month[tb_mod_month$pred_mod=="mod4",c("rmse")],
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tb_mod_month[tb_mod_month$pred_mod=="mod5",c("rmse")],
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tb_mod_month[tb_mod_month$pred_mod=="mod6",c("rmse")],
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tb_mod_month[tb_mod_month$pred_mod=="mod7",c("rmse")])
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test<- as.data.frame(do.call(cbind,test)) |
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names(test) <- paste("mod",1:7,sep="")
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test$month <- tb_mod_month$month[1:12] |
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test[,c(3,6,7)] |
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list_tb_mod_month[[i]] <- test |
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} |
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list_tb_mod_month[[1]][,c(3,6,7)] |
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list_tb_mod_month[[2]][,c(3,6,7)] |
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list_tb_mod_month[[3]][,c(3,6,7)] |
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#xyplot(test) |
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list_tb_mod_month[[1]][,c(6,7)]-list_tb_mod_month[[1]][,c(3)] |
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list_tb_mod_month[[2]][,c(6,7)]-list_tb_mod_month[[1]][,c(3)] |
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list_tb_mod_month[[3]][,c(6,7)]-list_tb_mod_month[[1]][,c(3)] |
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#model with lev only |
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tb1_mod1_month <- raster_prediction_obj_1$summary_month_metrics_v[[1]] #note that this is for model1 |
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tb1_mod1_month<-raster_prediction_obj_1$summary_month_metrics_v[[1]] #note that this is for model1 |
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rmse_dif <- (tb1_mod4_month$rmse) - tb1_mod1_month$rmse |
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plot(rmse_dif) |
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####################################################### |
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####### PART 2: generate figures for paper ############# |
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Also available in: Unified diff
revisions2 multi-timescale paper adding comp between mod1 and mod4 (LST and elev) rmse