#6 additional parameters for monthly climatology and more

  list_models<-list_param_raster_prediction$list_models

  lst_avg<-list_param_raster_prediction$lst_avg

  out_path<-list_param_raster_prediction$out_path

  script_path<-list_param_raster_prediction$script_path

  if (interpolation_method %in% c("gam_CAI","kriging_CAI","gwr_CAI","gam_fusion","kriging_fusion","gwr_fusion")){

    #input a list:note that ghcn.subsets is not sampling_obj$data_day_ghcn

    i<-1

    #method_mod_obj<-mclapply(1:length(sampling_obj$ghcn_data),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

    #debug(run_prediction_daily_deviation)

    #test <- run_prediction_daily_deviation(1,list_param=list_param_run_prediction_daily_deviation) #This is the end bracket from mclapply(...) statement

    method_mod_obj<-mclapply(1:nrow(daily_dev_sampling_dat),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

    save(method_mod_obj,file= file.path(out_path,paste("method_mod_obj_",interpolation_method,"_",y_var_name,out_prefix,".RData",sep="")))

  }

  #TODO : Same call for all functions!!! Replace by one "if" for all daily single time scale methods...

    save(validation_mod_month_obj,file= file.path(out_path,paste(interpolation_method,"_validation_mod_month_obj_",y_var_name,out_prefix,".RData",sep="")))

    ##Create data.frame with validation and fit metrics for a full year/full numbe of runs

    #tb_diagnostic_v contains accuracy metrics for models sample and proportion for every run...if full year then 365 rows maximum

    rownames(tb_month_diagnostic_v)<-NULL #remove row names

    tb_month_diagnostic_v$method_interp <- interpolation_method

#STAGE 5: Output analyses: assessment of results for specific dates...

#

#AUTHOR: Benoit Parmentier                                                                       

#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363, TASK$568--   

#met_stations_outfiles_obj_file<-"met_stations_outfiles_obj_gam_CAI__365d_gam_CAI_lst_comb3_08252013.RData"

var<-"TMAX" # variable being interpolated

out_suffix_modis <-"_05302013"                       #pattern to find tiles produced previously     

#interpolation_method<-c("gam_fusion","gam_CAI","gam_daily") #other otpions to be added later

#interpolation_method<-c("gam_fusion") #other otpions to be added later

#interpolation_method<-c("kriging_fusion") #other otpions to be added later

#interpolation_method<-c("gwr_fusion") #other otpions to be added later

#interpolation_method<-c("kriging_CAI") 

#interpolation_method<-c("gam_daily") #other otpions to be added later

nb_sample_month <-1           #number of time random sampling must be repeated for every hold out proportion

step_month <-0.1         

constant_month <- 0             #if value 1 then use the same samples as date one for the all set of dates

#dates_selected<-c("20100101","20100102","20100103","20100901") # Note that the dates set must have a specific format: yyymmdd

#dates_selected<-c("20100101","20100102","20100301","20100302","20100501","20100502","20100701","20100702","20100901","20100902","20101101","20101102")

#                "y_var ~ s(lat,lon) + s(elev_s) + s(LST) + ti(LST,CANHGHT)")

#Combination 4: for paper baseline=s(lat,lon)

#list_models<-c("y_var ~ lat*lon + elev_s")

#list_models<-c("y_var ~ s(lat,lon) + s(elev_s)")

#Default name of LST avg to be matched               

lst_avg<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09","mm_10","mm_11","mm_12")  

list_param_raster_prediction<-list(list_param_data_prep,screen_data_training,

                                seed_number,nb_sample,step,constant,prop_minmax,dates_selected,

                                seed_number_month,nb_sample_month,step_month,constant_month,prop_minmax_month,

                                interpolation_method)

names(list_param_raster_prediction)<-c("list_param_data_prep","screen_data_training",

                                "seed_number","nb_sample","step","constant","prop_minmax","dates_selected",

                                "seed_number_month","nb_sample_month","step_month","constant_month","prop_minmax_month",

                                "interpolation_method")

#debug(raster_prediction_fun)

raster_prediction_obj <-raster_prediction_fun(list_param_raster_prediction)

############## STAGE 5: OUTPUT ANALYSES ##################