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69864891
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Benoit Parmentier
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runKriging <- function(i) { # loop over dates
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#date<-strptime(dates[i], "%Y%m%d") # interpolation date being processed
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date<-strptime(sampling_dat$date[i], "%Y%m%d") # interpolation date being processed
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month<-strftime(date, "%m") # current month of the date being processed
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LST_month<-paste("mm_",month,sep="") # name of LST month to be matched
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#i=1
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date_proc<-sampling_dat$date[i]
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date_proc<-strptime(sampling_dat$date[i], "%Y%m%d") # interpolation date being processed
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mo<-as.integer(strftime(date_proc, "%m")) # current month of the date being processed
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day<-as.integer(strftime(date_proc, "%d"))
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year<-as.integer(strftime(date_proc, "%Y"))
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#Adding layer LST to the raster stack
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pos<-match(LST_month,layerNames(s_raster)) #Find column with the current month for instance mm12
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r1<-raster(s_raster,layer=pos) #Select layer from stack
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layerNames(r1)<-"LST"
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s_raster<-addLayer(s_raster,r1) #Adding current month
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s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame
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###Regression part 1: Creating a validation dataset by creating training and testing datasets
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mod_LST <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))] #Match interpolation date and monthly LST average
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ghcn.subsets[[i]] = transform(ghcn.subsets[[i]],LST = mod_LST) #Add the variable LST to the subset dataset
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#n<-nrow(ghcn.subsets[[i]])
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#ns<-n-round(n*prop) #Create a sample from the data frame with 70% of the rows
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#nv<-n-ns #create a sample for validation with prop of the rows
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#ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly
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ind.training<-sampling[[i]]
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ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)
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data_s <- ghcn.subsets[[i]][ind.training, ] #Training dataset currently used in the modeling
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data_v <- ghcn.subsets[[i]][ind.testing, ] #Testing/validation dataset using input sampling
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ns<-nrow(data_s)
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nv<-nrow(data_v)
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###BEFORE model prediction the data object must be transformed to SDF
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coords<- data_v[,c('x_OR83M','y_OR83M')]
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coordinates(data_v)<-coords
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proj4string(data_v)<-CRS #Need to assign coordinates...
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coords<- data_s[,c('x_OR83M','y_OR83M')]
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coordinates(data_s)<-coords
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proj4string(data_s)<-CRS #Need to assign coordinates..
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ns<-nrow(data_s) #This is added to because some loss of data might have happened because of the averaging...
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nv<-nrow(data_v)
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### PREDICTION/ Interpolation
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pos<-match("value",names(data_s)) #Find column with name "value"
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names(data_s)[pos]<-y_var_name
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pos<-match("value",names(data_v)) #Find column with name "value"
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names(data_v)[pos]<-y_var_name
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#if y_var_name=="dailyTmax"
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data_v$y_var<-data_v[[y_var_name]]/10 #Note that values are divided by 10 because the var is temp
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data_s$y_var<-data_s[[y_var_name]]/10
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#Model and response variable can be changed without affecting the script
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formula1 <- as.formula("y_var ~1", env=.GlobalEnv)
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formula2 <- as.formula("y_var~ x_OR83M+y_OR83M", env=.GlobalEnv)
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formula3 <- as.formula("y_var~ x_OR83M+y_OR83M+ELEV_SRTM", env=.GlobalEnv)
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formula4 <- as.formula("y_var~ x_OR83M+y_OR83M+DISTOC", env=.GlobalEnv)
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formula5 <- as.formula("y_var~ x_OR83M+y_OR83M+ELEV_SRTM+DISTOC", env=.GlobalEnv)
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formula6 <- as.formula("y_var~ x_OR83M+y_OR83M+Northness+Eastness", env=.GlobalEnv)
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formula7 <- as.formula("y_var~ LST", env=.GlobalEnv)
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formula8 <- as.formula("y_var~ x_OR83M+y_OR83M+LST", env=.GlobalEnv)
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formula9 <- as.formula("y_var~ x_OR83M+y_OR83M+ELEV_SRTM+LST", env=.GlobalEnv)
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mod1<- try(autoKrige(formula1, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod2<- try(autoKrige(formula2, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod3<- try(autoKrige(formula3, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod4<- try(autoKrige(formula4, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod5<- try(autoKrige(formula5, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod6<- try(autoKrige(formula6, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod7<- try(autoKrige(formula7, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod8<- try(autoKrige(formula8, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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mod9<- try(autoKrige(formula9, input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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#tmax_predicted=themolst+daily_delta_rast-bias_rast #Final surface?? but daily_rst
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### Model assessment
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for (j in 1:nmodels){
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##Model assessment: specific diagnostic/metrics for GAM
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name<-paste("mod",j,sep="") #modj is the name of The "j" model (mod1 if j=1)
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mod<-get(name) #accessing GAM model ojbect "j"
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#krmod_auto<-get(mod)
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#If mod "j" is not a model object
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if (inherits(mod,"try-error")) {
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#results_m1[1,1]<- dates[i] #storing the interpolation dates in the first column
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results_m1[1,1]<- sampling_dat$date[i]
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results_m1[1,2]<- ns #number of stations used in the training stage
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results_m1[1,3]<- "SSERR"
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results_m1[1,j+3]<- NA
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results_m2[1,1]<- results_m1[1,1]<- #storing the interpolation dates in the first column
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results_m2[1,2]<- ns #number of stations used in the training
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results_m2[1,3]<- "GCV"
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results_m2[1,j+3]<- NA
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results_m3[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_m3[1,2]<- ns #number of stations used in the training stage
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results_m3[1,3]<- "DEV"
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results_m3[1,j+3]<- NA
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results_RMSE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_RMSE_f[1,2]<- ns #number of stations used in the training stage
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results_RMSE_f[1,3]<- "RSME_f"
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results_RMSE_f[1,j+3]<- NA
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results_MAE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_MAE_f[1,2]<- ns #number of stations used in the training stage
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results_MAE_f[1,3]<- "MAE_f"
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results_MAE_f[1,j+3]<-NA
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results_RMSE[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_RMSE[1,2]<- ns #number of stations used in the training stage
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results_RMSE[1,3]<- "RMSE"
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results_RMSE[1,j+3]<- NA #Storing RMSE for the model j
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results_MAE[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_MAE[1,2]<- ns #number of stations used in the training stage
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results_MAE[1,3]<- "MAE"
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results_MAE[1,j+3]<- NA #Storing MAE for the model j
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results_ME[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_ME[1,2]<- ns #number of stations used in the training stage
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results_ME[1,3]<- "ME"
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results_ME[1,j+3]<- NA #Storing ME for the model j
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results_R2[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_R2[1,2]<- ns #number of stations used in the training stage
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results_R2[1,3]<- "R2"
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results_R2[1,j+3]<- NA #Storing R2 for the model j
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}
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#If mod is a modelobject
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#If mod "j" is not a model object
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if (inherits(mod,"autoKrige")) {
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rpred<-mod$krige_output #Extracting the SptialGriDataFrame from the autokrige object
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#rpred<- predict(mod, newdata=s_sgdf, se.fit = TRUE) #Using the coeff to predict new values.
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y_pred<-rpred$var1.pred #is the order the same?
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#y_prederr<-rpred$var1.var
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raster_pred<-r1
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layerNames(raster_pred)<-"y_pred"
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clearValues(raster_pred) #Clear values in memory, just in case...
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values(raster_pred)<-as.numeric(y_pred) #Assign values to every pixels
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#data_name<-paste("predicted_mod",j,"_",dates[[i]],sep="")
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data_name<-paste("predicted_mod",j,"_",sampling_dat$date[i],"_",sampling_dat$prop[i],
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"_",sampling_dat$run_samp[i],sep="")
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raster_name<-paste("Kriging_",data_name,out_prefix,".rst", sep="")
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writeRaster(raster_pred, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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#writeRaster(r2, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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#Save png plot here...
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#data_name<-paste("predicted_mod",j,"_",dates[[i]],sep="")
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data_name<-paste("predicted_mod",j,"_",sampling_dat$date[i],"_",sampling_dat$prop[i],
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"_",sampling_dat$run_samp[i],sep="")
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png_name<-paste("Kriging_plot_",data_name,out_prefix,".png", sep="")
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png(png_name) #Create file to write a plot
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#png(paste("Delta_surface_LST_TMax_",sampling_dat$date[i],"_",sampling_dat$prop[i],
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# "_",sampling_dat$run_samp[i],out_prefix,".png", sep=""))
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#datelabel2=format(ISOdate(year,mo,day),"%B ") #Plot label
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plot(mod) #Plot to file the autokrige object
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#savePlot(paste("Bias_surface_LST_TMax_",dates[i],out_prefix,".png", sep=""), type="png")
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dev.off() #Release the hold to the file
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pred_sgdf<-as(raster_pred,"SpatialGridDataFrame" ) #Conversion to spatial grid data frame
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#rpred_val_s <- overlay(raster_pred,data_s) #This overlays the kriged surface tmax and the location of weather stations
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rpred_val_s <- overlay(pred_sgdf,data_s) #This overlays the kriged surface tmax and the location of weather stations
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rpred_val_v <- overlay(pred_sgdf,data_v) #This overlays the kriged surface tmax and the location of weather stations
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pred_mod<-paste("pred_mod",j,sep="")
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#Adding the results back into the original dataframes.
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data_s[[pred_mod]]<-rpred_val_s$y_pred
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data_v[[pred_mod]]<-rpred_val_v$y_pred
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#Model assessment: RMSE and then krig the residuals....!
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res_mod_s<- data_s$y_var - data_s[[pred_mod]] #Residuals from kriging training
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res_mod_v<- data_v$y_var - data_v[[pred_mod]] #Residuals from kriging validation
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####ADDED ON JULY 20th
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res_mod<-res_mod_v
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#RMSE_mod <- sqrt(sum(res_mod^2)/nv) #RMSE FOR REGRESSION STEP 1: GAM
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RMSE_mod<- sqrt(mean(res_mod^2,na.rm=TRUE))
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#MAE_mod<- sum(abs(res_mod),na.rm=TRUE)/(nv-sum(is.na(res_mod))) #MAE from kriged surface validation
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MAE_mod<- mean(abs(res_mod), na.rm=TRUE)
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#ME_mod<- sum(res_mod,na.rm=TRUE)/(nv-sum(is.na(res_mod))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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ME_mod<- mean(res_mod,na.rm=TRUE) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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#R2_mod<- cor(data_v$y_var,data_v[[pred_mod]])^2 #R2, coef. of var FOR REGRESSION STEP 1: GAM
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R2_mod<- cor(data_v$y_var,data_v[[pred_mod]], use="complete")^2
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R2_mod_f<- cor(data_s$y_var,data_s[[pred_mod]], use="complete")^2
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RMSE_mod_f<- sqrt(mean(res_mod_s^2,na.rm=TRUE))
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#MAE_mod<- sum(abs(res_mod),na.rm=TRUE)/(nv-sum(is.na(res_mod))) #MAE from kriged surface validation
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MAE_mod_f<- mean(abs(res_mod_s), na.rm=TRUE)
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results_m1[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_m1[1,2]<- ns #number of stations used in the training stage
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results_m1[1,3]<- "SSERR"
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results_m1[1,j+3]<- mod$sserr
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results_m2[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_m2[1,2]<- ns #number of stations used in the training
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results_m2[1,3]<- "GCV"
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results_m2[1,j+3]<- NA
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results_m3[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_m3[1,2]<- ns #number of stations used in the training stage
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results_m3[1,3]<- "DEV"
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results_m3[1,j+3]<- NA
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results_RMSE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_RMSE_f[1,2]<- ns #number of stations used in the training stage
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results_RMSE_f[1,3]<- "RSME_f"
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results_RMSE_f[1,j+3]<-RMSE_mod_f
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results_MAE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_MAE_f[1,2]<- ns #number of stations used in the training stage
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results_MAE_f[1,3]<- "MAE_f"
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results_MAE_f[1,j+3]<-MAE_mod_f
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results_R2_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_R2_f[1,2]<- ns #number of stations used in the training stage
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results_R2_f[1,3]<- "R2_f"
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results_R2_f[1,j+3]<- R2_mod_f #Storing R2 for the model j
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results_RMSE[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_RMSE[1,2]<- ns #number of stations used in the training stage
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results_RMSE[1,3]<- "RMSE"
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results_RMSE[1,j+3]<- RMSE_mod #Storing RMSE for the model j
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results_MAE[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_MAE[1,2]<- ns #number of stations used in the training stage
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results_MAE[1,3]<- "MAE"
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results_MAE[1,j+3]<- MAE_mod #Storing MAE for the model j
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results_ME[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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results_ME[1,2]<- ns #number of stations used in the training stage
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results_ME[1,3]<- "ME"
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results_ME[1,j+3]<- ME_mod #Storing ME for the model j
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255 |
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results_R2[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
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256 |
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results_R2[1,2]<- ns #number of stations used in the training stage
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257 |
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results_R2[1,3]<- "R2"
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258 |
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results_R2[1,j+3]<- R2_mod #Storing R2 for the model j
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259 |
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|
260 |
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#Saving residuals and prediction in the dataframes: tmax predicted from GAM
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261 |
|
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|
262 |
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name2<-paste("res_mod",j,sep="")
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263 |
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data_v[[name2]]<-as.numeric(res_mod_v)
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264 |
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data_s[[name2]]<-as.numeric(res_mod_s)
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265 |
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#end of loop calculating RMSE
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266 |
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}
|
267 |
|
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}
|
268 |
|
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|
269 |
|
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#if (i==length(dates)){
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270 |
|
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|
271 |
|
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#Specific diagnostic measures related to the testing datasets
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272 |
|
|
|
273 |
|
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results_table_RMSE<-as.data.frame(results_RMSE)
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274 |
|
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results_table_MAE<-as.data.frame(results_MAE)
|
275 |
|
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results_table_ME<-as.data.frame(results_ME)
|
276 |
|
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results_table_R2<-as.data.frame(results_R2)
|
277 |
|
|
results_table_RMSE_f<-as.data.frame(results_RMSE_f)
|
278 |
|
|
results_table_MAE_f<-as.data.frame(results_MAE_f)
|
279 |
|
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results_table_R2_f<-as.data.frame(results_R2_f)
|
280 |
|
|
|
281 |
|
|
results_table_m1<-as.data.frame(results_m1)
|
282 |
|
|
results_table_m2<-as.data.frame(results_m2)
|
283 |
|
|
results_table_m3<-as.data.frame(results_m3)
|
284 |
|
|
|
285 |
|
|
tb_metrics1<-rbind(results_table_RMSE,results_table_MAE, results_table_ME,
|
286 |
|
|
results_table_R2,results_table_RMSE_f,results_table_MAE_f,results_table_R2_f) #
|
287 |
|
|
tb_metrics2<-rbind(results_table_m1,results_table_m2, results_table_m3)
|
288 |
|
|
cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8","mod9")
|
289 |
|
|
colnames(tb_metrics1)<-cname
|
290 |
|
|
cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8","mod9")
|
291 |
|
|
colnames(tb_metrics2)<-cname
|
292 |
|
|
#colnames(results_table_RMSE)<-cname
|
293 |
|
|
#colnames(results_table_RMSE_f)<-cname
|
294 |
|
|
#tb_diagnostic1<-results_table_RMSE #measures of validation
|
295 |
|
|
#tb_diagnostic2<-results_table_RMSE_f #measures of fit
|
296 |
|
|
|
297 |
|
|
#write.table(tb_diagnostic1, file= paste(path,"/","results_fusion_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
|
298 |
|
|
|
299 |
|
|
#}
|
300 |
|
|
#print(paste(date_proc,"processed"))
|
301 |
|
|
print(paste(sampling_dat$date[i],"processed"))
|
302 |
|
|
|
303 |
|
|
# Kriging object may need to be modified...because it contains the full image of prediction!!
|
304 |
|
|
##loop through model objects data frame and set field to zero...
|
305 |
|
|
|
306 |
|
|
mod_obj<-list(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9)
|
307 |
|
|
names(mod_obj)<-c("mod1","mod2","mod3","mod4","mod5","mod6","mod7","mod8","mod9") #generate names automatically??
|
308 |
|
|
#results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2)
|
309 |
|
|
#save(mod_obj,file= paste(path,"/","results_list_mod_objects_",dates[i],out_prefix,".RData",sep=""))
|
310 |
|
|
|
311 |
|
|
for (j in 1:nmodels){
|
312 |
|
|
if (inherits(mod_obj[[j]],"autoKrige")){
|
313 |
|
|
mod_obj[[j]]$krige_output<-NULL
|
314 |
|
|
}
|
315 |
|
|
}
|
316 |
|
|
results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2,mod_obj)
|
317 |
|
|
names(results_list)<-c("data_s","data_v","tb_metrics1","tb_metrics2","mod_obj")
|
318 |
|
|
save(results_list,file= paste(path,"/","results_list_metrics_objects_",sampling_dat$date[i],"_",sampling_dat$prop[i],
|
319 |
|
|
"_",sampling_dat$run_samp[i],out_prefix,".RData",sep=""))
|
320 |
|
|
return(results_list)
|
321 |
|
|
#return(tb_diagnostic1)
|
322 |
|
|
}
|