#Functions used in the script

calc_val_metrics<-function(x,y){

  #This functions calculates accurayc metrics on given two vectors.

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

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

  #s_sgdf<-as(r_stack,"SpatialGridDataFrame") #Conversion to spatial grid data frame

  residuals<-x-y

  mae<-mean(abs(residuals),na.rm=T)

  rmse<-sqrt(mean((residuals)^2,na.rm=T))

  me<-mean(residuals,na.rm=T)

  r<-cor(x,y,use="complete")

  m50<-median(residuals,na.rm=T)

  metrics_dat<-as.data.frame(cbind(mae,rmse,me,r,m50))

  names(metrics_dat)<-c("mae","rmse","me","r","m50")

  metrics_obj<-list(metrics_dat,as.data.frame(residuals))

  names(metrics_obj)<-c("metrics_dat","residuals")

  return(metrics_obj)

}

calc_val_metrics_rast <-function(df,y_ref,pred_names){

  #Input parameters:

  #1) df: data frame containing the observed and predicted variables (data_s or data_v)

  #2) y_ref: observed variable correspond to y_var_name??

  #3) pred_names: models run containig predicted values

  # library

  library(maptools)

  ## START SCRIPT

  list_metrics<-vector("list",length(pred_names))

  list_residuals<-vector("list",length(pred_names))

  names(list_metrics)<-pred_names

  names(list_residuals)<-pred_names

  for (j in 1:length(pred_names)){

    pred_var<-pred_names[j]

    metrics<-calc_val_metrics(df[[pred_var]],df[[y_ref]])

    list_metrics[[j]]<-metrics[[1]]

    list_residuals[[j]]<-metrics[[2]]

  }

  metrics_df<-do.call(rbind,list_metrics)

  metrics_df$pred_mod <- pred_names #adding name column

  residuals_df<-do.call(cbind,list_residuals) #creating data frame for residuals

  names(residuals_df)<-paste("res",pred_names,sep="_")

  accuracy_obj<-list(metrics_df,residuals_df) #output object

  names(accuracy_obj)<-c("metrics","residuals") 

  return(accuracy_obj)

}  

### Main function to compute training and testing accuracy statistics

  if(class(day_list[[1]])=="list"){

    day_list<-unlist(day_list)

  }