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902f729b
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Benoit Parmentier
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runKriging <- function(i) { # loop over dates
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#This allows to change only one name of the
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date<-strptime(dates[i], "%Y%m%d")
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month<-strftime(date, "%m")
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LST_month<-paste("mm_",month,sep="")
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mod <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))]
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ghcn.subsets[[i]]$LST <-mod[[1]]
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#
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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) #This selects the index position for testing subset stations.
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data_s <- ghcn.subsets[[i]][ind.training, ]
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data_v <- ghcn.subsets[[i]][ind.testing, ]
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#adding to SpatialGridDataFrame
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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)
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s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame
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###BEFORE Kringing 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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#This allows to change only one name of the data.frame
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pos<-match("value",names(data_s)) #Find column with name "value"
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names(data_s)[pos]<-c("tmax")
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data_s$tmax<-data_s$tmax/10 #TMax is the average max temp for months
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pos<-match("value",names(data_v)) #Find column with name "value"
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names(data_v)[pos]<-c("tmax")
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data_v$tmax<-data_v$tmax/10
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#dstjan=dst[dst$month==9,] #dst contains the monthly averages for tmax for every station over 2000-2010
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##############
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###STEP 2 KRIGING###
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#Kriging tmax
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# hscat(tmax~1,data_s,(0:9)*20000) # 9 lag classes with 20,000m width
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# v<-variogram(tmax~1, data_s) # This plots a sample varigram for date 10 fir the testing dataset
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# plot(v)
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# v.fit<-fit.variogram(v,vgm(2000,"Sph", 150000,1000)) #Model variogram: sill is 2000, spherical, range 15000 and nugget 1000
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# plot(v, v.fit) #Compare model and sample variogram via a graphical plot
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# tmax_krige<-krige(tmax~1, data_s,mean_LST, v.fit) #mean_LST provides the data grid/raster image for the kriging locations to be predicted.
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# krmod1<-try(autoKrige(tmax~1, data_s,s_sgdf,data_s)) #Use autoKrige instead of krige: with data_s for fitting on a grid
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# krmod2<-try(autoKrige(tmax~x_OR83M+y_OR83M,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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# krmod3<-try(autoKrige(tmax~x_OR83M+y_OR83M+ELEV_SRTM,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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# krmod4<-try(autoKrige(tmax~x_OR83M+y_OR83M+DISTOC,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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# krmod5<-try(autoKrige(tmax~x_OR83M+y_OR83M+ELEV_SRTM+DISTOC,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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# krmod6<-try(autoKrige(tmax~x_OR83M+y_OR83M+Northness+Eastness,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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# krmod7<-try(autoKrige(tmax~x_OR83M+y_OR83M+Northness+Eastness,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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#
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krmod1<-try(autoKrige(tmax~1, data_s,s_sgdf,data_s)) #Use autoKrige instead of krige: with data_s for fitting on a grid
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krmod2<-try(autoKrige(tmax~lat+lon,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod3<-try(autoKrige(tmax~lat+lon+ELEV_SRTM,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod4<-try(autoKrige(tmax~lat+lon+DISTOC,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod5<-try(autoKrige(tmax~lat+lon+ELEV_SRTM+DISTOC,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod6<-try(autoKrige(tmax~lat+lon+Northness+Eastness,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod7<-try(autoKrige(tmax~lat+lon+Northness+Eastness,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod8<-try(autoKrige(tmax~LST,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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krmod9<-try(autoKrige(tmax~lat+lon+LST,input_data=data_s,new_data=s_sgdf,data_variogram=data_s))
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# krig1<-krmod1$krige_output #Extracting Spatial Grid Data frame
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# krig2<-krmod2$krige_output
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# krig3<-krmod3$krige_outpu
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# krig4<-krmod4$krige_output
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# krig5<-krmod5$krige_output
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# krig6<-krmod6$krige_output #Extracting Spatial Grid Data frame
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# krig7<-krmod7$krige_output
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#krig8<-krmod8$krige_outpu
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#krig9<-krmod9$krige_output
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#tmax_krig1_s <- overlay(krige,data_s) #This overlays the kriged surface tmax and the location of weather stations
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#tmax_krig1_v <- overlay(krige,data_v)
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#
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# #Cokriging tmax
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# g<-gstat(NULL,"tmax", tmax~1, data_s) #This creates a gstat object "g" that acts as container for kriging specifications.
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# g<-gstat(g, "SRTM_elev",ELEV_SRTM~1,data_s) #Adding variables to gstat object g
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# g<-gstat(g, "LST", LST~1,data_s)
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# vm_g<-variogram(g) #Visualizing multivariate sample variogram.
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# vm_g.fit<-fit.lmc(vm_g,g,vgm(2000,"Sph", 100000,1000)) #Fitting variogram for all variables at once.
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# plot(vm_g,vm_g.fit) #Visualizing variogram fit and sample
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# vm_g.fit$set <-list(nocheck=1) #Avoid checking and allow for different range in variogram
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# co_kriged_surf<-predict(vm_g.fit,mean_LST) #Prediction using co-kriging with grid location defined from input raster image.
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# #co_kriged_surf$tmax.pred #Results stored in SpatialGridDataFrame with tmax prediction accessible in dataframe.
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#spplot.vcov(co_kriged_surf) #Visualizing the covariance structure
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# tmax_cokrig1_s<- overlay(co_kriged_surf,data_s) #This overalys the cokriged surface tmax and the location of weather stations
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# tmax_cokrig1_v<- overlay(co_kriged_surf,data_v)
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for (j in 1:models){
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#mod<-paste("krig",j,sep="")
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mod<-paste("krmod",j,sep="")
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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(krmod_auto,"try-error")) {
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#Model assessment:results are NA
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results_RMSE[1,1]<- dates[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
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_MAE[1,1]<- dates[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
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_ME[1,1]<- dates[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
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_R2[1,1]<- dates[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
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_RMSE_f[1,1]<- dates[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]<- "RMSE_f"
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results_RMSE_f[1,j+3]<- NA
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_MAE_f[1,1]<- dates[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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name3<-paste("res_kr_mod",j,sep="")
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}
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#If mod "j" is not a model object
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if (inherits(krmod_auto,"autoKrige")) {
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krmod<-krmod_auto$krige_output #Extracting Spatial Grid Data frame
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krig_val_s <- overlay(krmod,data_s) #This overlays the kriged surface tmax and the location of weather stations
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krig_val_v <- overlay(krmod,data_v) #This overlays the kriged surface tmax and the location of weather stations
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pred_krmod<-paste("pred_krmod",j,sep="")
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#Adding the results back into the original dataframes.
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data_s[[pred_krmod]]<-krig_val_s$var1.pred
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data_v[[pred_krmod]]<-krig_val_v$var1.pred
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#Model assessment: RMSE and then krig the residuals....!
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res_mod_kr_s<- data_s$tmax - data_s[[pred_krmod]] #Residuals from kriging training
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res_mod_kr_v<- data_v$tmax - data_v[[pred_krmod]] #Residuals from kriging validation
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RMSE_mod_kr_s <- sqrt(sum(res_mod_kr_s^2,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_s)))) #RMSE from kriged surface training
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RMSE_mod_kr_v <- sqrt(sum(res_mod_kr_v^2,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_v)))) #RMSE from kriged surface validation
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MAE_mod_kr_s<- sum(abs(res_mod_kr_s),na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_s))) #MAE from kriged surface training #MAE, Mean abs. Error FOR REGRESSION STEP 1: GAM
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MAE_mod_kr_v<- sum(abs(res_mod_kr_v),na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_v))) #MAE from kriged surface validation
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ME_mod_kr_s<- sum(res_mod_kr_s,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_s))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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ME_mod_kr_v<- sum(res_mod_kr_v,na.rm=TRUE)/(nv-sum(is.na(res_mod_kr_v))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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R2_mod_kr_s<- cor(data_s$tmax,data_s[[pred_krmod]],use="complete.obs")^2 #R2, coef. of determination FOR REGRESSION STEP 1: GAM
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R2_mod_kr_v<- cor(data_v$tmax,data_v[[pred_krmod]],use="complete.obs")^2 #R2, coef. of determinationFOR REGRESSION STEP 1: GAM
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#(nv-sum(is.na(res_mod2)))
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#Writing out results
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results_RMSE[1,1]<- dates[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_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_MAE[1,1]<- dates[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_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_ME[1,1]<- dates[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_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_R2[1,1]<- dates[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]<- R2_mod_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_RMSE_f[1,1]<- dates[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]<- "RMSE_f"
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results_RMSE_f[1,j+3]<- RMSE_mod_kr_s
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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results_MAE_f[1,1]<- dates[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_kr_s
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name3<-paste("res_kr_mod",j,sep="")
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#as.numeric(res_mod)
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#data_s[[name3]]<-res_mod_kr_s
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data_s[[name3]]<-as.numeric(res_mod_kr_s)
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#data_v[[name3]]<-res_mod_kr_v
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data_v[[name3]]<-as.numeric(res_mod_kr_v)
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#Writing residuals from kriging
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#Saving kriged surface in raster images
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data_name<-paste("mod",j,"_",dates[[i]],sep="")
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#krig_raster_name<-paste("krmod_",data_name,out_prefix,".tif", sep="")
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#writeGDAL(krmod,fname=krig_raster_name, driver="GTiff", type="Float32",options ="INTERLEAVE=PIXEL", overwrite=TRUE)
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krig_raster_name<-paste("krmod_",data_name,out_prefix,".rst", sep="")
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writeRaster(raster(krmod), filename=krig_raster_name, overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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#krig_raster_name<-paste("Kriged_tmax_",data_name,out_prefix,".tif", sep="")
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#writeGDAL(tmax_krige,fname=krig_raster_name, driver="GTiff", type="Float32",options ="INTERLEAVE=PIXEL")
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#X11()
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#plot(raster(co_kriged_surf))
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#title(paste("Tmax cokriging for date ",dates[[i]],sep=""))
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#savePlot(paste("Cokriged_tmax",data_name,out_prefix,".png", sep=""), type="png")
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#dev.off()
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#X11()
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244 |
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#plot(raster(tmax_krige))
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245 |
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#title(paste("Tmax Kriging for date ",dates[[i]],sep=""))
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246 |
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#savePlot(paste("Kriged_res_",data_name,out_prefix,".png", sep=""), type="png")
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247 |
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#dev.off()
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248 |
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# end of if krige object
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249 |
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}
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250 |
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|
251 |
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}
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252 |
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|
253 |
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# #Co-kriging only on the validation sites for faster computing
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254 |
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#
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255 |
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# cokrig1_dv<-predict(vm_g.fit,data_v)
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256 |
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# cokrig1_ds<-predict(vm_g.fit,data_s)
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257 |
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# # data_s$tmax_cokr<-cokrig1_ds$tmax.pred
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258 |
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# # data_v$tmax_cokr<-cokrig1_dv$tmax.pred
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259 |
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#
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260 |
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# #Calculate RMSE and then krig the residuals....!
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261 |
|
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#
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262 |
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# res_mod1<- data_v$tmax - data_v$tmax_kr #Residuals from kriging.
|
263 |
|
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# res_mod2<- data_v$tmax - data_v$tmax_cokr #Residuals from cokriging.
|
264 |
|
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#
|
265 |
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# RMSE_mod1 <- sqrt(sum(res_mod1^2,na.rm=TRUE)/(nv-sum(is.na(res_mod1)))) #RMSE from kriged surface.
|
266 |
|
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# RMSE_mod2 <- sqrt(sum(res_mod2^2,na.rm=TRUE)/(nv-sum(is.na(res_mod2)))) #RMSE from co-kriged surface.
|
267 |
|
|
# #(nv-sum(is.na(res_mod2)))
|
268 |
|
|
|
269 |
|
|
#Saving the subset in a dataframe
|
270 |
|
|
data_name<-paste("ghcn_v_",dates[[i]],sep="")
|
271 |
|
|
assign(data_name,data_v)
|
272 |
|
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data_name<-paste("ghcn_s_",dates[[i]],sep="")
|
273 |
|
|
assign(data_name,data_s)
|
274 |
|
|
|
275 |
|
|
# results[i,1]<- dates[i] #storing the interpolation dates in the first column
|
276 |
|
|
# results[i,2]<- ns #number of stations in training
|
277 |
|
|
# results[i,3]<- RMSE_mod1
|
278 |
|
|
# results[i,4]<- RMSE_mod2
|
279 |
|
|
#
|
280 |
|
|
# results_mod_n[i,1]<-dates[i]
|
281 |
|
|
# results_mod_n[i,2]<-(nv-sum(is.na(res_mod1)))
|
282 |
|
|
# results_mod_n[i,3]<-(nv-sum(is.na(res_mod2)))
|
283 |
|
|
|
284 |
|
|
#Specific diagnostic measures related to the testing datasets
|
285 |
|
|
#browser()
|
286 |
|
|
results_table_RMSE<-as.data.frame(results_RMSE)
|
287 |
|
|
results_table_MAE<-as.data.frame(results_MAE)
|
288 |
|
|
results_table_ME<-as.data.frame(results_ME)
|
289 |
|
|
results_table_R2<-as.data.frame(results_R2)
|
290 |
|
|
results_table_RMSE_f<-as.data.frame(results_RMSE_f)
|
291 |
|
|
results_table_MAE_f<-as.data.frame(results_MAE_f)
|
292 |
|
|
|
293 |
|
|
results_table_AIC<-as.data.frame(results_AIC) #Other tables for kriging
|
294 |
|
|
results_table_GCV<-as.data.frame(results_GCV)
|
295 |
|
|
results_table_DEV<-as.data.frame(results_DEV)
|
296 |
|
|
|
297 |
|
|
tb_metrics1<-rbind(results_table_RMSE,results_table_MAE, results_table_ME, results_table_R2,results_table_RMSE_f,results_table_MAE_f) #
|
298 |
|
|
tb_metrics2<-rbind(results_table_AIC,results_table_GCV, results_table_DEV)
|
299 |
|
|
cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7")
|
300 |
|
|
colnames(tb_metrics1)<-cname
|
301 |
|
|
cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7")
|
302 |
|
|
colnames(tb_metrics2)<-cname
|
303 |
|
|
#colnames(results_table_RMSE)<-cname
|
304 |
|
|
#colnames(results_table_RMSE_f)<-cname
|
305 |
|
|
#tb_diagnostic1<-results_table_RMSE #measures of validation
|
306 |
|
|
#tb_diagnostic2<-results_table_RMSE_f #measures of fit
|
307 |
|
|
|
308 |
|
|
#write.table(tb_diagnostic1, file= paste(path,"/","results_fusion_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
|
309 |
|
|
|
310 |
|
|
#}
|
311 |
|
|
print(paste(dates[i],"processed"))
|
312 |
|
|
mod_obj<-list(krmod1,krmod2,krmod3,krmod4,krmod5,krmod6,krmod7)
|
313 |
|
|
# end of the for loop1
|
314 |
|
|
#results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2)
|
315 |
|
|
results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2,mod_obj)
|
316 |
|
|
return(results_list)
|
317 |
|
|
#return(tb_diagnostic1)
|
318 |
|
|
}
|