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######################### Raster prediction ####################################
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############################ Interpolation of temperature for given processing region ##########################################
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#This script interpolates temperature values using MODIS LST, covariates and GHCND station data.
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#It requires the text file of stations and a shape file of the study area.
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#Note that the projection for both GHCND and study area is lonlat WGS84.
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#Options to run this program are:
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#1) Multisampling: vary the porportions of hold out and use random samples for each run
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#2)Constant sampling: use the same sample over the runs
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#3)over dates: run over for example 365 dates without mulitsampling
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#4)use seed number: use seed if random samples must be repeatable
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#5)possibilty of running single and multiple time scale methods:
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# gam_daily, kriging_daily,gwr_daily,gam_CAI,gam_fusion,kriging_fusion,gwr_fusion and other options added.
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#For multiple time scale methods, the interpolation is done first at the monthly time scale then delta surfaces are added.
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#AUTHOR: Benoit Parmentier
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#DATE: 07/30/2013
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#568--
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#
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# TO DO:
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#Add methods to for CAI
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###################################################################################################
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raster_prediction_fun <-function(list_param_raster_prediction){
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##Function to predict temperature interpolation with 21 input parameters
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#9 parameters used in the data preparation stage and input in the current script
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#1)list_param_data_prep: used in earlier code for the query from the database and extraction for raster brick
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#2)infile_monthly: monthly averages with covariates for GHCND stations obtained after query
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#3)infile_daily: daily GHCND stations with covariates, obtained after query
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#4)infile_locs: vector file with station locations for the processing/study area (ESRI shapefile)
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#5)infile_covariates: raster covariate brick, tif file
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#6)covar_names: covar_names #remove at a later stage...
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#7)var: variable being interpolated-TMIN or TMAX
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#8)out_prefix
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#9)CRS_locs_WGS84
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#10)screen_data_training
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#
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#6 parameters for sampling function
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#10)seed_number
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#11)nb_sample
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#12)step
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#13)constant
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#14)prop_minmax
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#15)dates_selected
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#
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#6 additional parameters for monthly climatology and more
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#16)list_models: model formulas in character vector
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#17)lst_avg: LST climatology name in the brick of covariate--change later
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#18)n_path
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#19)out_path
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#20)script_path: path to script
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#21)interpolation_method: c("gam_fusion","gam_CAI") #other otpions to be added later
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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 Simon Wood
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library(sp) # Spatial pacakge with class definition by Bivand et al.
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library(spdep) # Spatial pacakge with methods and spatial stat. by Bivand et al.
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library(rgdal) # GDAL wrapper for R, spatial utilities
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library(gstat) # Kriging and co-kriging by Pebesma et al.
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library(fields) # NCAR Spatial Interpolation methods such as kriging, splines
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library(raster) # Hijmans et al. package for raster processing
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library(rasterVis)
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library(parallel) # Urbanek S. and Ripley B., package for multi cores & parralel processing
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library(reshape)
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library(plotrix)
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library(maptools)
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library(gdata) #Nesssary to use cbindX
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library(automap) #autokrige function
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library(spgwr) #GWR method
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### Parameters and arguments
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#PARSING INPUTS/ARGUMENTS
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#
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# names(list_param_raster_prediction)<-c("list_param_data_prep",
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# "seed_number","nb_sample","step","constant","prop_minmax","dates_selected",
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# "list_models","lst_avg","in_path","out_path","script_path",
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# "interpolation_method")
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#9 parameters used in the data preparation stage and input in the current script
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list_param_data_prep<-list_param_raster_prediction$list_param_data_prep
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infile_monthly<-list_param_data_prep$infile_monthly
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infile_daily<-list_param_data_prep$infile_daily
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infile_locs<-list_param_data_prep$infile_locs
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infile_covariates<-list_param_data_prep$infile_covariates #raster covariate brick, tif file
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covar_names<- list_param_data_prep$covar_names #remove at a later stage...
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var<-list_param_data_prep$var
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out_prefix<-list_param_data_prep$out_prefix
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CRS_locs_WGS84<-list_param_data_prep$CRS_locs_WGS84
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#6 parameters for sampling function
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seed_number<-list_param_raster_prediction$seed_number
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nb_sample<-list_param_raster_prediction$nb_sample
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step<-list_param_raster_prediction$step
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constant<-list_param_raster_prediction$constant
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prop_minmax<-list_param_raster_prediction$prop_minmax
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dates_selected<-list_param_raster_prediction$dates_selected
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#6 additional parameters for monthly climatology and more
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list_models<-list_param_raster_prediction$list_models
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lst_avg<-list_param_raster_prediction$lst_avg
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out_path<-list_param_raster_prediction$out_path
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script_path<-list_param_raster_prediction$script_path
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interpolation_method<-list_param_raster_prediction$interpolation_method
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screen_data_training <-list_param_raster_prediction$screen_data_training
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setwd(out_path)
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###################### START OF THE SCRIPT ########################
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if (var=="TMAX"){
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y_var_name<-"dailyTmax"
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}
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if (var=="TMIN"){
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y_var_name<-"dailyTmin"
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}
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################# CREATE LOG FILE #####################
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#create log file to keep track of details such as processing times and parameters.
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#log_fname<-paste("R_log_raster_prediction",out_prefix, ".log",sep="")
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log_fname<-paste("R_log_raster_prediction",out_prefix, ".log",sep="")
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#sink(log_fname) #create new log file
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file.create(file.path(out_path,log_fname)) #create new log file
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time1<-proc.time() #Start stop watch
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cat(paste("Starting script at this local Date and Time: ",as.character(Sys.time()),sep=""),
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file=log_fname,sep="\n")
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cat("Starting script process time:",file=log_fname,sep="\n",append=TRUE)
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cat(as.character(time1),file=log_fname,sep="\n",append=TRUE)
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############### READING INPUTS: DAILY STATION DATA AND OTEHR DATASETS #################
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ghcn<-readOGR(dsn=dirname(infile_daily),layer=sub(".shp","",basename(infile_daily)))
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CRS_interp<-proj4string(ghcn) #Storing projection information (ellipsoid, datum,etc.)
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stat_loc<-readOGR(dsn=dirname(infile_locs),layer=sub(".shp","",basename(infile_locs)))
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#dates2 <-readLines(file.path(in_path,dates_selected)) #dates to be predicted, now read directly from the file
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if (dates_selected==""){
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dates<-as.character(sort(unique(ghcn$date))) #dates to be predicted
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}
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if (dates_selected!=""){
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dates<-dates_selected #dates to be predicted
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}
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#Reading in covariate brickcan be changed...
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s_raster<-brick(infile_covariates) #read in the data brck
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names(s_raster)<-covar_names #Assigning names to the raster layers: making sure it is included in the extraction
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#Reading monthly data
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dst<-readOGR(dsn=dirname(infile_monthly),layer=sub(".shp","",basename(infile_monthly)))
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########### CREATE SAMPLING -TRAINING AND TESTING STATIONS ###########
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#Input for sampling function...
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#dates #list of dates for prediction
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#ghcn_name<-"ghcn" #infile daily data
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list_param_sampling<-list(seed_number,nb_sample,step,constant,prop_minmax,dates,ghcn)
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#list_param_sampling<-list(seed_number,nb_sample,step,constant,prop_minmax,dates,ghcn_name)
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names(list_param_sampling)<-c("seed_number","nb_sample","step","constant","prop_minmax","dates","ghcn")
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#run function, note that dates must be a character vector!!
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sampling_obj<-sampling_training_testing(list_param_sampling)
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########### PREDICT FOR MONTHLY SCALE ##################
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#First predict at the monthly time scale: climatology
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cat("Predictions at monthly scale:",file=log_fname,sep="\n", append=TRUE)
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cat(paste("Local Date and Time: ",as.character(Sys.time()),sep=""),
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file=log_fname,sep="\n")
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t1<-proc.time()
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j=12
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#browser() #Missing out_path for gam_fusion list param!!!
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#if (interpolation_method=="gam_fusion"){
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if (interpolation_method %in% c("gam_fusion","kriging_fusion","gwr_fusion")){
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list_param_runClim_KGFusion<-list(j,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, out_prefix,out_path)
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names(list_param_runClim_KGFusion)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","out_prefix","out_path")
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#source(file.path(script_path,"GAM_fusion_function_multisampling_03122013.R"))
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clim_method_mod_obj<-mclapply(1:12, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement
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#clim_method_mod_obj<-mclapply(1:6, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement
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#test<-runClim_KGFusion(1,list_param=list_param_runClim_KGFusion)
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save(clim_method_mod_obj,file= file.path(out_path,paste(interpolation_method,"_mod_",y_var_name,out_prefix,".RData",sep="")))
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list_tmp<-vector("list",length(clim_method_mod_obj))
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for (i in 1:length(clim_method_mod_obj)){
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tmp<-clim_method_mod_obj[[i]]$clim
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list_tmp[[i]]<-tmp
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}
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clim_yearlist<-list_tmp
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}
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if (interpolation_method %in% c("gam_CAI","kriging_CAI", "gwr_CAI")){
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list_param_runClim_KGCAI<-list(j,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, out_prefix,out_path)
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names(list_param_runClim_KGCAI)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","out_prefix","out_path")
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clim_method_mod_obj<-mclapply(1:12, list_param=list_param_runClim_KGCAI, runClim_KGCAI,mc.preschedule=FALSE,mc.cores = 6) #This is the end bracket from mclapply(...) statement
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#test<-runClim_KGCAI(1,list_param=list_param_runClim_KGCAI)
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#gamclim_fus_mod<-mclapply(1:6, list_param=list_param_runClim_KGFusion, runClim_KGFusion,mc.preschedule=FALSE,mc.cores = 6)
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save(clim_method_mod_obj,file= file.path(out_path,paste(interpolation_method,"_mod_",y_var_name,out_prefix,".RData",sep="")))
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list_tmp<-vector("list",length(clim_method_mod_obj))
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for (i in 1:length(clim_method_mod_obj)){
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tmp<-clim_method_mod_obj[[i]]$clim
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list_tmp[[i]]<-tmp
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}
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clim_yearlist<-list_tmp
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}
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t2<-proc.time()-t1
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cat(as.character(t2),file=log_fname,sep="\n", append=TRUE)
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################## PREDICT AT DAILY TIME SCALE #################
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#Predict at daily time scale from single time scale or multiple time scale methods: 2 methods availabe now
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#put together list of clim models per month...
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#rast_clim_yearlist<-list_tmp
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#Second predict at the daily time scale: delta
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#method_mod_obj<-mclapply(1:1, runGAMFusion,mc.preschedule=FALSE,mc.cores = 1) #This is the end bracket from mclapply(...) statement
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cat("Predictions at the daily scale:",file=log_fname,sep="\n", append=TRUE)
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t1<-proc.time()
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cat(paste("Local Date and Time: ",as.character(Sys.time()),sep=""),
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file=log_fname,sep="\n")
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#TODO : Same call for all functions!!! Replace by one "if" for all multi time scale methods...
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#The methods could be defined earlier as constant??
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if (interpolation_method %in% c("gam_CAI","kriging_CAI","gwr_CAI","gam_fusion","kriging_fusion","gwr_fusion")){
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#input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn
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i<-1
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list_param_run_prediction_daily_deviation <-list(i,clim_yearlist,sampling_obj,dst,var,y_var_name, interpolation_method,out_prefix,out_path)
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names(list_param_run_prediction_daily_deviation)<-c("list_index","clim_yearlist","sampling_obj","dst","var","y_var_name","interpolation_method","out_prefix","out_path")
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#test<-mclapply(1:18, runGAMFusion,list_param=list_param_runGAMFusion,mc.preschedule=FALSE,mc.cores = 9)
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#test<-runGAMFusion(1,list_param=list_param_runGAMFusion)
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method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data_day),list_param=list_param_run_prediction_daily_deviation,run_prediction_daily_deviation,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))
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}
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#TODO : Same call for all functions!!! Replace by one "if" for all daily single time scale methods...
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if (interpolation_method=="gam_daily"){
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#input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn
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i<-1
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list_param_run_prediction_gam_daily <-list(i,s_raster,covar_names,lst_avg,list_models,dst,screen_data_training,var,y_var_name, sampling_obj,interpolation_method,out_prefix,out_path)
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names(list_param_run_prediction_gam_daily)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","screen_data_training","var","y_var_name","sampling_obj","interpolation_method","out_prefix","out_path")
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#test <- runGAM_day_fun(1,list_param_run_prediction_gam_daily)
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method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data_day),list_param=list_param_run_prediction_gam_daily,runGAM_day_fun,mc.preschedule=FALSE,mc.cores = 11) #This is the end bracket from mclapply(...) statement
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#method_mod_obj<-mclapply(1:11,list_param=list_param_run_prediction_gam_daily,runGAM_day_fun,mc.preschedule=FALSE,mc.cores = 11) #This is the end bracket from mclapply(...) statement
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save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))
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}
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if (interpolation_method=="kriging_daily"){
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#input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn
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i<-1
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list_param_run_prediction_kriging_daily <-list(i,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, sampling_obj,interpolation_method,out_prefix,out_path)
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names(list_param_run_prediction_kriging_daily)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","sampling_obj","interpolation_method","out_prefix","out_path")
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#test <- runKriging_day_fun(1,list_param_run_prediction_kriging_daily)
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method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data_day),list_param=list_param_run_prediction_kriging_daily,runKriging_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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#method_mod_obj<-mclapply(1:18,list_param=list_param_run_prediction_kriging_daily,runKriging_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))
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}
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if (interpolation_method=="gwr_daily"){
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#input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn
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i<-1
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list_param_run_prediction_gwr_daily <-list(i,s_raster,covar_names,lst_avg,list_models,dst,var,y_var_name, sampling_obj,interpolation_method,out_prefix,out_path)
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names(list_param_run_prediction_gwr_daily)<-c("list_index","covar_rast","covar_names","lst_avg","list_models","dst","var","y_var_name","sampling_obj","interpolation_method","out_prefix","out_path")
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#test <- run_interp_day_fun(1,list_param_run_prediction_gwr_daily)
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method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data_day),list_param=list_param_run_prediction_gwr_daily,run_interp_day_fun,mc.preschedule=FALSE,mc.cores = 11) #This is the end bracket from mclapply(...) statement
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#method_mod_obj<-mclapply(1:9,list_param=list_param_run_prediction_gwr_daily,run_interp_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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#method_mod_obj<-mclapply(1:18,list_param=list_param_run_prediction_kriging_daily,runKriging_day_fun,mc.preschedule=FALSE,mc.cores = 9) #This is the end bracket from mclapply(...) statement
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save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))
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}
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t2<-proc.time()-t1
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cat(as.character(t2),file=log_fname,sep="\n", append=TRUE)
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#browser()
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############### NOW RUN VALIDATION #########################
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#SIMPLIFY THIS PART: one call
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list_tmp<-vector("list",length(method_mod_obj))
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for (i in 1:length(method_mod_obj)){
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tmp<-method_mod_obj[[i]][[y_var_name]] #y_var_name is the variable predicted (dailyTmax or dailyTmin)
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list_tmp[[i]]<-tmp
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}
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rast_day_yearlist<-list_tmp #list of predicted images over full year...
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cat("Validation step:",file=log_fname,sep="\n", append=TRUE)
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t1<-proc.time()
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cat(paste("Local Date and Time: ",as.character(Sys.time()),sep=""),
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file=log_fname,sep="\n")
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list_param_validation<-list(i,rast_day_yearlist,method_mod_obj,y_var_name, out_prefix, out_path)
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names(list_param_validation)<-c("list_index","rast_day_year_list","method_mod_obj","y_var_name","out_prefix", "out_path") #same names for any method
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validation_mod_obj <-mclapply(1:length(method_mod_obj), list_param=list_param_validation, calculate_accuracy_metrics,mc.preschedule=FALSE,mc.cores = 9)
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#test_val<-calculate_accuracy_metrics(1,list_param_validation)
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save(validation_mod_obj,file= file.path(out_path,paste(interpolation_method,"_validation_mod_obj_",y_var_name,out_prefix,".RData",sep="")))
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t2<-proc.time()-t1
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cat(as.character(t2),file=log_fname,sep="\n", append=TRUE)
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#################### ASSESSMENT OF PREDICTIONS: PLOTS OF ACCURACY METRICS ###########
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##Create data.frame with validation and fit metrics for a full year/full numbe of runs
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tb_diagnostic_v<-extract_from_list_obj(validation_mod_obj,"metrics_v")
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#tb_diagnostic_v contains accuracy metrics for models sample and proportion for every run...if full year then 365 rows maximum
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rownames(tb_diagnostic_v)<-NULL #remove row names
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tb_diagnostic_v$method_interp <- interpolation_method
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tb_diagnostic_s<-extract_from_list_obj(validation_mod_obj,"metrics_s")
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rownames(tb_diagnostic_s)<-NULL #remove row names
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tb_diagnostic_s$method_interp <- interpolation_method #add type of interpolation...out_prefix too??
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323
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#Call functions to create plots of metrics for validation dataset
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metric_names<-c("rmse","mae","me","r","m50")
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summary_metrics_v<- boxplot_from_tb(tb_diagnostic_v,metric_names,out_prefix,out_path) #if adding for fit need to change outprefix
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names(summary_metrics_v)<-c("avg","median")
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summary_month_metrics_v<- boxplot_month_from_tb(tb_diagnostic_v,metric_names,out_prefix,out_path)
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328
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|
329
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#################### CLOSE LOG FILE ####################
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330
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331
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#close log_file connection and add meta data
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332
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cat("Finished script process time:",file=log_fname,sep="\n", append=TRUE)
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333
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time2<-proc.time()-time1
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334
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cat(as.character(time2),file=log_fname,sep="\n", append=TRUE)
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335
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#later on add all the parameters used in the script...
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336
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cat(paste("Finished script at this local Date and Time: ",as.character(Sys.time()),sep=""),
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337
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file=log_fname,sep="\n", append=TRUE)
|
338
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cat("End of script",file=log_fname,sep="\n", append=TRUE)
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339
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#close(log_fname)
|
340
|
|
341
|
################### PREPARE RETURN OBJECT ###############
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342
|
#Will add more information to be returned
|
343
|
|
344
|
if (interpolation_method %in% c("gam_CAI","kriging_CAI","gwr_CAI","gam_fusion","kriging_fusion","gwr_fusion")){
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345
|
raster_prediction_obj<-list(clim_method_mod_obj,method_mod_obj,validation_mod_obj,tb_diagnostic_v,
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346
|
tb_diagnostic_s,summary_metrics_v,summary_month_metrics_v)
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347
|
names(raster_prediction_obj)<-c("clim_method_mod_obj","method_mod_obj","validation_mod_obj","tb_diagnostic_v",
|
348
|
"tb_diagnostic_s","summary_metrics_v","summary_month_metrics_v")
|
349
|
save(raster_prediction_obj,file= file.path(out_path,paste("raster_prediction_obj_",interpolation_method,"_", y_var_name,out_prefix,".RData",sep="")))
|
350
|
|
351
|
}
|
352
|
|
353
|
#use %in% instead of "|" operator
|
354
|
if (interpolation_method=="gam_daily" | interpolation_method=="kriging_daily" | interpolation_method=="gwr_daily"){
|
355
|
raster_prediction_obj<-list(method_mod_obj,validation_mod_obj,tb_diagnostic_v,
|
356
|
tb_diagnostic_s,summary_metrics_v,summary_month_metrics_v)
|
357
|
names(raster_prediction_obj)<-c("method_mod_obj","validation_mod_obj","tb_diagnostic_v",
|
358
|
"tb_diagnostic_s","summary_metrics_v","summary_month_metrics_v")
|
359
|
save(raster_prediction_obj,file= file.path(out_path,paste("raster_prediction_obj_",interpolation_method,"_", y_var_name,out_prefix,".RData",sep="")))
|
360
|
|
361
|
}
|
362
|
|
363
|
return(raster_prediction_obj)
|
364
|
}
|
365
|
|
366
|
####################################################################
|
367
|
######################## END OF SCRIPT/FUNCTION #####################
|