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618bf412
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Benoit Parmentier
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##################################### METHOD COMPARISON ##########################################
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#################################### Spatial Analysis ########################################
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#This script utilizes the R ojbects created during the interpolation phase.
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# R ojbects must be supplied in a text file with along with their names.
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# Five mehods are compared over set of year: Kriging, GWR, GAM, CAI and FUSION.
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# At this stage the script produces figures of various accuracy metrics and compare x: #
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#- boxplots for MAE, RMSE and other accuracy metrics
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#- MAE, RMSE plots per month
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#- visualization of map results for all predictions method
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#AUTHOR: Benoit Parmentier
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#DATE: 10/12/2012
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#491--
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618bf412
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Benoit Parmentier
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###################################################################################################
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###Loading R library and packages
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#library(gtools) # loading some useful tools
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library(mgcv) # GAM package by Wood 2006 (version 2012)
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library(sp) # Spatial pacakge with class definition by Bivand et al. 2008
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library(spdep) # Spatial package with methods and spatial stat. by Bivand et al. 2012
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library(rgdal) # GDAL wrapper for R, spatial utilities (Keitt et al. 2012)
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library(gstat) # Kriging and co-kriging by Pebesma et al. 2004
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library(automap) # Automated Kriging based on gstat module by Hiemstra et al. 2008
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library(spgwr)
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library(maptools)
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library(graphics)
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library(parallel) # Urbanek S. and Ripley B., package for multi cores & parralel processing
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library(raster)
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library(rasterVis)
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library(plotrix) #Draw circle on graph and additional plotting options
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library(reshape) #Data format and type transformation
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618bf412
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## Functions
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#loading R objects that might have similar names
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load_obj <- function(f)
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{
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env <- new.env()
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nm <- load(f, env)[1]
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env[[nm]]
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}
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###Parameters and arguments
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infile1<- "ghcn_or_tmax_covariates_06262012_OR83M.shp" #GHCN shapefile containing variables for modeling 2010
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#infile2<-"list_10_dates_04212012.txt" #List of 10 dates for the regression
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infile2<-"list_365_dates_04212012.txt" #list of dates
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infile3<-"LST_dates_var_names.txt" #LST dates name
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infile4<-"models_interpolation_05142012.txt" #Interpolation model names
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infile5<-"mean_day244_rescaled.rst" #mean LST for day 244
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inlistf<-"list_files_05032012.txt" #list of raster images containing the Covariates
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infile6<-"OR83M_state_outline.shp"
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#stat_loc<-read.table(paste(path,"/","location_study_area_OR_0602012.txt",sep=""),sep=",", header=TRUE)
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obj_list<-"list_obj_10172012.txt" #Results of fusion from the run on ATLAS
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#obj_list<-"list_obj_08262012.txt" #Results of fusion from the run on ATLAS
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path<-"/home/parmentier/Data/IPLANT_project/methods_interpolation_comparison" #Jupiter LOCATION on Atlas for kriging #Jupiter Location on XANDERS
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setwd(path)
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proj_str="+proj=lcc +lat_1=43 +lat_2=45.5 +lat_0=41.75 +lon_0=-120.5 +x_0=400000 +y_0=0 +ellps=GRS80 +units=m +no_defs";
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#Number of kriging model
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out_prefix<-"methods_10172012_" #User defined output prefix
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filename<-sub(".shp","",infile1) #Removing the extension from file.
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ghcn<-readOGR(".", filename) #reading shapefile
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### PREPARING RASTER COVARIATES STACK #######
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#CRS<-proj4string(ghcn) #Storing projection information (ellipsoid, datum,etc.)
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lines<-read.table(paste(path,"/",inlistf,sep=""), sep="") #Column 1 contains the names of raster files
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inlistvar<-lines[,1]
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inlistvar<-paste(path,"/",as.character(inlistvar),sep="")
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covar_names<-as.character(lines[,2]) #Column two contains short names for covaraites
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s_raster<- stack(inlistvar) #Creating a stack of raster images from the list of variables.
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layerNames(s_raster)<-covar_names #Assigning names to the raster layers
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projection(s_raster)<-proj_str
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#Create mask
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pos<-match("LC10",layerNames(s_raster))
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LC10<-subset(s_raster,pos)
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LC10[is.na(LC10)]<-0 #Since NA values are 0, we assign all zero to NA
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mask_land<-LC10<100
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mask_land_NA<-mask_land
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mask_land_NA[mask_land_NA==0]<-NA
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data_name<-"mask_land_OR"
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raster_name<-paste(data_name,".rst", sep="")
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writeRaster(mask_land, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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#writeRaster(r2, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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pos<-match("mm_01",layerNames(s_raster))
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mm_01<-subset(s_raster,pos)
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mm_01<-mm_01-273.15
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mm_01<-mask(mm_01,mask_land_NA)
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#mention this is the last... files
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############# METHODS COMPARISON ###########
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######################################################################
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# PART 1 : USING ACCURACY METRICS FOR FIVE METHODS COMPARISON
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# Boxplots and histograms
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lines<-read.table(paste(path,"/",obj_list,sep=""), sep=",") #Column 1 contains the names RData objects
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inlistobj<-lines[,1]
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inlistobj<-paste(path,"/",as.character(inlistobj),sep="")
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obj_names<-as.character(lines[,2]) #Column two contains short names for obj. model
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nel<-length(inlistobj)
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method_mod <-vector("list",nel) #list of one row data.frame
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method_tb <-vector("list",nel) #list of one row data.frame
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method_mean<-vector("list",nel)
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for (i in 1:length(inlistobj)){
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obj_tmp<-load_obj(inlistobj[i])
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method_mod[[i]]<-obj_tmp
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#names(method_mod[[i]])<-obj_names[i]
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}
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obj_tmp<-load_obj(inlistobj[i])
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names(method_mod)<-obj_names
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#Condense and add other comparison, transform in function??
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for(k in 1:length(method_mod)){ # start of the for main loop to all methods
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tb<-method_mod[[k]][[1]][[3]][0,] #copy
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mod_tmp<-method_mod[[k]]
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for (i in 1:365){ # Assuming 365 days of prediction
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tmp<-mod_tmp[[i]][[3]]
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tb<-rbind(tb,tmp)
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}
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rm(mod_tmp)
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for(i in 4:(ncol(tb))){ # start of the for loop #1
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tb[,i]<-as.numeric(as.character(tb[,i]))
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}
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method_tb[[k]]<-tb
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tb_RMSE<-subset(tb, metric=="RMSE")
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tb_MAE<-subset(tb,metric=="MAE")
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tb_ME<-subset(tb,metric=="ME")
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tb_R2<-subset(tb,metric=="R2")
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tb_RMSE_f<-subset(tb, metric=="RMSE_f")
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tb_MAE_f<-subset(tb,metric=="MAE_f")
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tb_diagnostic1<-rbind(tb_RMSE,tb_MAE,tb_ME,tb_R2)
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na_mod<-colSums(!is.na(tb_RMSE[,4:ncol(tb)]))
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for (j in 4:ncol(tb)){
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if (na_mod[j-3]<183){
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tb_RMSE<-tb_RMSE[,-j] #Remove columns that have too many missing values!!!
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}
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}
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na_mod<-colSums(!is.na(tb_MAE[,4:ncol(tb)]))
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for (j in 4:ncol(tb)){
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if (na_mod[j-3]<183){
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tb_MAE<-tb_MAE[,-j] #Remove columns that have too many missing values!!!
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}
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}
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na_mod<-colSums(!is.na(tb_MAE_f[,4:ncol(tb)]))
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for (j in 4:ncol(tb)){
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if (na_mod[j-3]<183){
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tb_MAE_f<-tb_MAE_f[,-j] #Remove columns that have too many missing values!!!
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}
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}
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na_mod<-colSums(!is.na(tb_ME[,4:ncol(tb)]))
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for (j in 4:ncol(tb)){
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if (na_mod[j-3]<183){
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tb_ME<-tb_ME[,-j] #Remove columns that have too many missing values!!!
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}
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}
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#Add assessment of missing prediction over the year.
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mean_RMSE<-sapply(tb_RMSE[,4:ncol(tb_RMSE)],mean,na.rm=T
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mean_MAE<-sapply(tb_MAE[,4:ncol(tb_MAE)],mean,na.rm=T)
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mean_R2<-sapply(tb_R2[,4:ncol(tb_R2)],mean, n.rm=T)
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mean_ME<-sapply(tb_ME[,4:ncol(tb_ME)],mean,na.rm=T)
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mean_MAE_f<-sapply(tb_MAE[,4:ncol(tb_MAE_f)],mean,na.rm=T)
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mean_RMSE_f<-sapply(tb_RMSE_f[,4:ncol(tb_RMSE_f)],mean,na.rm=T)
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mean_list<-list(mean_RMSE,mean_MAE,mean_R2,mean_ME,mean_MAE_f,mean_RMSE_f)
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names(mean_list)<-c("RMSE","MAE","R2","ME","MAE_f","RMSE_f")
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method_mean[[k]]<-mean_list
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names_methods<-obj_names
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sd_RMSE<-sapply(tb_RMSE[,4:ncol(tb_RMSE)],sd,na.rm=T)
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sd_MAE<-sapply(tb_MAE[,4:ncol(tb_MAE)],sd,na.rm=T)
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# Now create plots
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png(paste("RMSE_for_",names_methods[k],out_prefix,".png", sep=""))
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boxplot(tb_RMSE[,4:ncol(tb_RMSE)],main=names_methods[k],ylim=c(1,4.5),
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ylab= "RMSE", outline=FALSE) #ADD TITLE RELATED TO METHODS...
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dev.off()
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#boxplot(tb_RMSE[,4:ncol(tb_RMSE)],main=names_methods[k],outline=FALSE) #ADD TITLE RELATED TO METHODS...
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png(paste("MAE_for_",names_methods[k],out_prefix,".png", sep=""))
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boxplot(tb_MAE[,4:ncol(tb_MAE)],main=names_methods[k], ylim=c(1,3.5),
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ylab= "MAE", outline=FALSE) #ADD TITLE RELATED TO METHODS...
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dev.off()
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#boxplot(tb_RMSE[,4:ncol(tb_RMSE)],main=names_methods[k],outline=FALSE) #ADD TITLE RELATED TO METHODS...
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png(paste("ME_for_",names_methods[k],out_prefix,".png", sep=""))
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boxplot(tb_ME[,4:ncol(tb_MAE)],main=names_methods[k],
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ylab= "ME", outline=FALSE) #ADD TITLE RELATED TO METHODS...
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dev.off()
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# OVER THE YEAR
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#...
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for(i in 1:nrow(tb)){
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date<-tb$dates[i]
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date<-strptime(date, "%Y%m%d")
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tb$month[i]<-as.integer(strftime(date, "%m"))
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}
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# USE RESHAPE...
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mod_pat<-glob2rx("mod*")
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var_pat<-grep(mod_pat,names(tb),value=TRUE) # using grep with "value" extracts the matching names
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tb_melt<-melt(tb,
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measure=var_pat,
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id=c("metric","month"),
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na.rm=F)
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tb_cast<-cast(tb_melt,metric+month~variable,mean)
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metrics<-as.character(unique(tb$metric)) #Name of accuracy metrics (RMSE,MAE etc.)
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tb_metric_list<-vector("list",length(metrics))
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png(paste("MAE_for_",names_methods[k],out_prefix,".png", sep=""))
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boxplot(tb__m_MAE[,4:ncol(tb_MAE)],main=names_methods[k], ylim=c(1,3.5),
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ylab= "MAE", outline=FALSE) #ADD TITLE RELATED TO METHODS...
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dev.off()
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metrics<-as.character(unique(tb$metric)) #Name of accuracy metrics (RMSE,MAE etc.)
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tb_metric_list<-vector("list",length(metrics))
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for(i in 1:length(metrics)){ # Reorganizing information in terms of metrics
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metric_name<-paste("tb_t_",metrics[i],sep="")
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tb_metric<-subset(tb, metric==metrics[i])
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assign(metric_name,tb_metric)
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tb_metric_list[[i]]<-tb_metric
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}
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tb_processed<-tb_metric_list[[i]]
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mod_pat<-glob2rx("mod*")
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var_pat<-grep(mod_pat,names(tb_processed),value=FALSE) # using grep with "value" extracts the matching names
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na_mod<-colSums(!is.na(tb_processed[,var_pat]))
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for (j in 4:ncol(tb)){
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if (na_mod[j-3]<183){
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tb_ME<-tb_ME[,-j] #Remove columns that have too many missing values!!!
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}
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}
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mod_formulas<-vector("list",length(method_mod))
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for(k in 1:length(method_mod)){ # start of the for main loop to all methods
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models_tmp<-method_mod[[k]][[1]][[5]] #day 1 for model k
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list_formulas<-vector("list",length(models_tmp))
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for (j in 1:length(models_tmp)){ #
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formula<-try(formula(models_tmp[[j]]))
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list_formulas[[j]]<-formula
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}
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names(list_formulas)<-names(models_tmp)
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mod_formulas[[k]]<-list_formulas
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}
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names(method_mean)<-obj_names
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#Add summary mean graphs!! HERE
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write.table(as.data.frame(method_mean$gam_fus_mod1$MAE), "methods_mean_gam_MAE_test1.txt", sep=",")
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write.table(as.data.frame(method_mean$fus_CAI$MAE), "methods_mean_fus_CAI_MAE_test1.txt", sep=",")
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######### Average per month
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#Add code here...
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gam_fus_mod1<-method_mod[[1]]
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##################### PART II #######################
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# VISUALIZATION OF RESULTS PLOTS ACROSS MODELS FOR METHODS
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date_selected<-"20100103"
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lf_krig<-list.files(pattern=paste("*",date_selected,"_07312012_365d_Kriging_autokrig2.rst$",sep=""))
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lf_gwr<-list.files(pattern=paste("*",date_selected,".*08152012_1d_gwr4.rst$",sep=""))
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lf_gam1<-list.files(pattern=paste("^GAM.*",date_selected,"_07242012_365d_GAM_fusion5.rst$",sep=""))
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lf_fus1<-list.files(pattern=paste("*.tmax_predicted.*.",date_selected,".*._365d_GAM_fusion_lstd_10062012.rst$",sep=""))
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lf_cai1<-list.files(pattern=paste("*CAI_tmax_pred.*",date_selected,"*.08072012_365d_GAM_CAI2.rst$",sep="")) #Search for files in relation to fusion
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lf_gam2<-list.files(pattern=paste("^GAM.*",date_selected,"_08122012_365d_GAM_fusion6.rst$",sep=""))
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#lf2_fus<-list.files(pattern=paste("*",date_selected,"*._365d_GAM_fusion_lstd_10062012.rst$",sep=""))
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#lf2_fus<-list.files(pattern=paste("*.20100103.*._365d_GAM_fusion_lstd_10062012.rst$",sep=""))
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lf2_fus<-list.files(pattern=paste("^fusion_tmax.*",date_selected,"_07242012_365d_GAM_fusion5.rst$",sep="")) #Search for files in relation to fusion
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d_krig_rast<-stack(lf_krig)
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d_gwr_rast<-stack(lf_gwr)
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d_gam1_rast<-stack(lf_gam1)
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d_fus1_rast<-stack(lf_fus1)
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d_cai1_rast<-stack(lf_cai1)
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d_gam2_rast<-stack(lf_gam2)
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list_day_method<-list(d_krig_rast,d_gwr_rast,d_gam1_rast,d_fus1_rast,d_cai1_rast,d_gam2_rast)
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names(list_day_method)<-paste(c("krig_","gwr_","gam1_","fus1_","cai1_","gam2_"),date_selected,sep="")
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out_prefix2<-"_10172012"
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for (k in 1:length(list_day_method)){
|
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|
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|
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predictions<-list_day_method[[k]]
|
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|
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projection(predictions)<-proj_str
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predictions<-mask(predictions,mask_elev_NA)
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#layerNames(predictions)<-c(paste('fusion',date_selected,sep=" "),paste('CAI',date_list2[[k]],sep=" "))
|
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# use overall min and max values to generate an nice, consistent set
|
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# of breaks for both colors (50 values) and legend labels (5 values)
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#s.range <- c(min(minValue(predictions)), max(maxValue(predictions)))
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s.range<-c(-12,18)
|
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|
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col.breaks <- pretty(s.range, n=60)
|
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|
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lab.breaks <- pretty(s.range, n=6)
|
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|
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temp.colors <- colorRampPalette(c('blue', 'white', 'red'))
|
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|
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|
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|
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# plot using these (common) breaks; note use of _reverse_ heat.colors,
|
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|
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# making it so that larger numbers are redder
|
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|
|
X11(6,12)
|
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|
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plot(predictions, breaks=col.breaks, col=temp.colors(length(col.breaks)-1),
|
331 |
|
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axis=list(at=lab.breaks, labels=lab.breaks))
|
332 |
|
|
|
333 |
|
|
savePlot(paste(names(list_day_method)[[k]],"_method_prediction_",out_prefix2,".png", sep=""), type="png")
|
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|
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dev.off()
|
335 |
|
|
}
|
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|
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|
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|
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##PLOTING OF ONE DATE TO COMPARE METHODS!!!
|
339 |
|
|
|
340 |
|
|
pos<-match("ELEV_SRTM",layerNames(s_raster))
|
341 |
|
|
ELEV_SRTM<-raster(s_raster,pos)
|
342 |
|
|
elev<-ELEV_SRTM
|
343 |
|
|
elev[-0.050<elev]<-NA #Remove all negative elevation lower than 50 meters...
|
344 |
|
|
|
345 |
|
|
mask_elev_NA<-elev> (-0.050)
|
346 |
|
|
|
347 |
|
|
date_selected<-"20100103"
|
348 |
|
|
lf_fus<-list.files(pattern=paste("^fusion_tmax.*",date_selected,"_07242012_365d_GAM_fusion5.rst$",sep="")) #Search for files in relation to fusion
|
349 |
|
|
lf_cai<-list.files(pattern=paste("*CAI_tmax_pred.*",date_selected,"*.08072012_365d_GAM_CAI2.rst$",sep="")) #Search for files in relation to fusion
|
350 |
|
|
|
351 |
|
|
r11<-raster(lf_fus) #Fusion
|
352 |
|
|
r12<-raster(lf_cai[1]) #CAI
|
353 |
|
|
predictions<-stack(r11,r12)
|
354 |
|
|
predictions<-mask(predictions,mask_land_elev_NA)
|
355 |
|
|
layerNames(predictions)<-c(paste('fusion',"20100103",sep=" "),paste('CAI',"20100103",sep=" "))
|
356 |
|
|
|
357 |
|
|
s.range <- c(min(minValue(predictions)), max(maxValue(predictions)))
|
358 |
|
|
col.breaks <- pretty(s.range, n=50)
|
359 |
|
|
lab.breaks <- pretty(s.range, n=5)
|
360 |
|
|
temp.colors <- colorRampPalette(c('blue', 'white', 'red'))
|
361 |
|
|
|
362 |
|
|
# plot using these (common) breaks; note use of _reverse_ heat.colors,
|
363 |
|
|
# making it so that larger numbers are redder
|
364 |
|
|
X11(6,12)
|
365 |
|
|
#plot(predictions, breaks=col.breaks, col=rev(heat.colors(length(col.breaks)-1)),
|
366 |
|
|
# axis=list(at=lab.breaks, labels=lab.breaks))
|
367 |
|
|
plot(predictions, breaks=col.breaks, col=temp.colors(length(col.breaks)-1),
|
368 |
|
|
axis=list(at=lab.breaks, labels=lab.breaks))
|
369 |
|
|
#plot(reg_outline, add=TRUE)
|
370 |
|
|
savePlot(paste("comparison_one_date_CAI_fusion_tmax_prediction_",date_selected,out_prefix,".png", sep=""),type="png")
|
371 |
|
|
|
372 |
|
|
#results2_fusion_Assessment_measure_all_365d_GAM_fusion_lstd_10062012.RData
|
373 |
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#### END OF THE SCRIPT
|