mod <-in_models[[i]] #accessing GAM model ojbect "j"

    raster_name<-out_filename[[i]]

    if (inherits(mod,"gam")) {           #change to c("gam","autoKrige")

      names(raster_pred)<-"y_pred"  

      writeRaster(raster_pred, filename=raster_name,overwrite=TRUE)  #Writing the data in a raster file format...(IDRISI)

      list_rast_pred[[i]]<-raster_name

    }

  }

#

runClim_KGFusion<-function(j){

  #Make this a function with multiple argument that can be used by mcmapply??

  #This creates clim fusion layers...

  #Model and response variable can be changed without affecting the script

  prop_month<-0 #proportion retained for validation

  run_samp<-1

  data_month<-dst[dst$month==j,] #Subsetting dataset for the relevant month of the date being processed

  LST_name<-lst_avg[j] # name of LST month to be matched

  data_month$LST<-data_month[[LST_name]]

  #Adding layer LST to the raster stack  

  pos<-match("LST",names(s_raster)) #Find the position of the layer with name "LST", if not present pos=NA

  s_raster<-dropLayer(s_raster,pos)      # If it exists drop layer

  LST<-subset(s_raster,LST_name)

  names(LST)<-"LST"

  s_raster<-addLayer(s_raster,LST)            #Adding current month

  #Now generate file names for the predictions...

  list_out_filename<-vector("list",length(mod_list))

  names(list_out_filename)<-cname  

    writeRaster(clim_fus_rast, filename=raster_name,overwrite=TRUE)  #Wri

  }

  bias_xy<-coordinates(data_month)

  fitbias<-Krig(bias_xy,data_month$LSTD_bias,theta=1e5) #use TPS or krige 

  mod_krtmp1<-fitbias

  model_name<-"mod_kr"

  bias_rast<-interpolate(LST,fitbias) #interpolation using function from raster package

  #Saving kriged surface in raster images

  rast_bias_list[[model_name]]<-raster_name_bias

  rast_clim_list[[model_name]]<-raster_name_clim

  clim_obj<-list(rast_bias_list,rast_clim_list,data_month,mod_list,list_formulas)

  names(clim_obj)<-c("bias","clim","data_month","mod","formulas")

## Run function for kriging...?

  #Arguments: 

  #1)list of climatology files for all models...(12*nb of models)

  #6)dst: data at the monthly time scale

  date<-strptime(sampling_dat$date[i], "%Y%m%d")   # interpolation date being processed

  month<-strftime(date, "%m")          # current month of the date being processed

  LST_month<-paste("mm_",month,sep="") # name of LST month to be matched

  ###Regression part 1: Creating a validation dataset by creating training and testing datasets

  data_day<-ghcn.subsets[[i]]

  day<-as.integer(strftime(date_proc, "%d"))

  year<-as.integer(strftime(date_proc, "%Y"))

  modst<-dst[dst$month==mo,] #Subsetting dataset for the relevant month of the date being processed

  #Change to y_var...could be TMin

  #modst$LSTD_bias <- modst$LST-modst$y_var

  modst$LSTD_bias <- modst$LST-modst$TMax; #That is the difference between the monthly LST mean and monthly station mean

  x<-as.data.frame(data_v)

  d<-as.data.frame(data_s)

  #x[x$value==-999.9]<-NA

  #xmoday contains the daily tmax values for validation with TMax being the monthly station tmax mean

  ##########

  ##########

  daily_delta<-dmoday$dailyTmax-dmoday$TMax

  daily_delta_xy<-as.matrix(cbind(dmoday$x,dmoday$y))

  fitdelta<-Krig(daily_delta_xy,daily_delta,theta=1e5) #use TPS or krige

  data_s$daily_delta<-daily_delta

  #########

  #########

  rast_clim_list<-rast_clim_yearlist[[mo]]  #select relevant month

    temp_list[[j]]<-raster_name

  }

  mod_krtmp2<-fitdelta

  model_name<-paste("mod_kr","day",sep="_")

  names(temp_list)<-names(rast_clim_list)