####################Interpolation of Tmax for 10 dates.#####################

#2)extract relevant variables from raster images before performing the regressions. 

###Parameters and arguments

infile1<-"ghcn_or_tmax_b_03032012_OR83M.csv"

ghcn = transform(ghcn,Eastness = sin(ASPECT))  #adding variable to the dataframe.

ghcn = transform(ghcn,Eastness_w = sin(slope)*sin(ASPECT))  #adding variable to the dataframe.

set.seed(100)

results_GCV<- matrix(1,length(dates),length(models)+3)

results_RMSE<- matrix(1,length(dates),length(models)+3)

#note that compare to the previous version date_ column was changed to date

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

  n<-nrow(ghcn.subsets[[i]])

  ns<-n-round(n*prop)  #Create a sample from the data frame with 70% of the rows

  nv<-n-ns             #create a sample for validation with prop of the rows

  #ns<-n-round(n*prop)  #Create a sample from the data frame with 70% of the rows

  ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly

  ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)

  data_s <- ghcn.subsets[[i]][ind.training, ]

  data_v <- ghcn.subsets[[i]][ind.testing, ]

  ####Regression part 2: GAM models

  mod2<- gam(tmax~ s(lat,lon,ELEV_SRTM), data=data_s)

  mod3<-gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM) +  s (Northness)+ s (Eastness) + s(DISTOC), data=data_s)

  mod4<-gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM) + s (Northness_w)+ s (Eastness_w) + s(DISTOC), data=data_s)

  mod5<- gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM, Northness) + s (Eastness) + s(DISTOC), data=data_s)

  mod6<- gam(tmax~ s(lat,lon) + s (ELEV_SRTM, Northness) + s (Eastness) + s(DISTOC), data=data_s)

  ####Regression part 3: Calculating and storing diagnostic measures

  results_AIC[i,2]<- ns        #number of stations used in the training stage

  results_AIC[i,3]<- AIC (mod1)

  results_AIC[i,4]<- AIC (mod2)

  results_AIC[i,5]<- AIC (mod3)

  results_AIC[i,6]<- AIC (mod4)

  results_AIC[i,7]<- AIC (mod5)

  results_AIC[i,8]<- AIC (mod6)

  results_GCV[i,2]<- ns        #number of stations used in the training stage

  results_GCV[i,3]<- mod1$gcv.ubre

  results_GCV[i,4]<- mod2$gcv.ubre

  results_GCV[i,5]<- mod3$gcv.ubre

  results_GCV[i,6]<- mod4$gcv.ubre

  results_GCV[i,7]<- mod5$gcv.ubre

  results_GCV[i,8]<- mod6$gcv.ubre

  results_DEV[i,2]<- ns        #number of stations used in the training stage

  results_DEV[i,3]<- mod1$deviance

  results_DEV[i,4]<- mod2$deviance

  results_DEV[i,5]<- mod3$deviance

  results_DEV[i,6]<- mod4$deviance

  results_DEV[i,7]<- mod5$deviance

  results_DEV[i,8]<- mod6$deviance

  #####VALIDATION: Prediction checking the results using the testing data########

  y_mod2<- predict(mod2, newdata=data_v, se.fit = TRUE)            

  y_mod3<- predict(mod3, newdata=data_v, se.fit = TRUE) 

  y_mod4<- predict(mod4, newdata=data_v, se.fit = TRUE) 

  y_mod5<- predict(mod5, newdata=data_v, se.fit = TRUE) 

  y_mod5<- predict(mod6, newdata=data_v, se.fit = TRUE)

  res_mod2<- data_v$tmax - y_mod2$fit   #Residuals for GAM model that resembles the PRISM interpolation                               

  res_mod3<- data_v$tmax - y_mod3$fit  

  res_mod4<- data_v$tmax - y_mod4$fit

  res_mod5<- data_v$tmax - y_mod5$fit

  res_mod6<- data_v$tmax - y_mod6$fit

  RMSE_mod2 <- sqrt(sum(res_mod2^2)/nv)

  RMSE_mod3 <- sqrt(sum(res_mod3^2)/nv)

  RMSE_mod4 <- sqrt(sum(res_mod4^2)/nv)

  RMSE_mod5 <- sqrt(sum(res_mod5^2)/nv)

  RMSE_mod6 <- sqrt(sum(res_mod6^2)/nv)

  results_RMSE[i,1]<- dates[i]  #storing the interpolation dates in the first column

  results_RMSE[i,2]<- ns        #number of stations used in the training stage

  results_RMSE[i,3]<- RMSE_mod1

  results_RMSE[i,4]<- RMSE_mod2

  results_RMSE[i,5]<- RMSE_mod3

  results_RMSE[i,6]<- RMSE_mod4

  results_RMSE[i,7]<- RMSE_mod5

  results_RMSE[i,8]<- RMSE_mod6

  data_name<-paste("ghcn_v_",dates[[i]],sep="")

  assign(data_name,data_v)

  #ghcn_v<-ls(pattern="ghcn_v_")

  # end of the for loop #1

  }

## Plotting and saving diagnostic measures

mode(results_RMSEnum)<- "numeric"

# Now turn it into a data.frame...

colnames(results_table_RMSE)<-c("dates","ns","mod1", "mod2","mod3", "mod4", "mod5", "mod6")

win.graph()

win.graph()

win.graph()

win.graph()

win.graph()

win.graph()

win.graph()

# ###############################

# 

# ############Diagnostic GAM plots#############

# win.graph()

# gam.check(GAM_ANUSPLIN1)

# savePlot("GAM_ANUSPLIN1_diagnostic1.emf", type="emf")

# win.graph()

# gam.check(GAM_PRISM1)   #This will produce basic plots of residuals

# savePlot("GAM_PRISM_diagnostic1.emf", type="emf")

# gam.check(GAM_ANUSPLIN1)

# win.graph()

# vis.gam(GAM_ANUSPLIN1)

# savePlot("GAM_ANUSPLIN1_prediction.emf", type="emf")        

# win.graph()

# vis.gam(GAM_PRISM1)

# savePlot("GAM_PRISM1_prediction.emf", type="emf")

# win.graph()

# vis.gam(GAM_ANUSPLIN1, view=c("lat","ELEV_SRTM"))

# #vis.gam(GAM_ANUSPLIN1, view=c("lat","ELEV_SRTM", theta=100,phi=200))

# savePlot("GAM_ANUSPLIN1_prediction2.emf", type="emf")