#This functions performs predictions on a raster grid given input models.

    #Arguments: list of fitted models, raster stack of covariates

    #Output: spatial grid data frame of the subset of tiles

    list_rast_pred<-vector("list",length(in_models))

    for (i in 1:length(in_models)){

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

      raster_name<-out_filename[[i]]

        raster_pred<- predict(object=s_raster,model=mod,na.rm=FALSE) #Using the coeff to predict new values.

        names(raster_pred)<-"y_pred"  

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

      }

    }

    if (inherits(mod,"try-error")) {

    }

    return(list_rast_pred)

  }

    list_fitted_models<-vector("list",length(list_formulas))

    for (k in 1:length(list_formulas)){

      formula<-list_formulas[[k]]

      model_name<-paste("mod",k,sep="")

      assign(model_name,mod) 

      list_fitted_models[[k]]<-mod

  #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]]

  names(x)[pos]<-c("id")

  names(modst)[1]<-c("id")       #modst contains the average tmax per month for every stations...

  mod_pat<-glob2rx("*.y2")   

  var_pat<-grep(mod_pat,names(dmoday),value=FALSE) # using grep with "value" extracts the matching names

  dmoday<-dmoday[,-var_pat]

  mod_krtmp2<-fitdelta

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

  coordinates(data_s)<-cbind(data_s$x,data_s$y)

  proj4string(data_s)<-proj_str

  coordinates(data_v)<-cbind(data_v$x,data_v$y)