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runGAMCAI <- 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$date[i], "%Y%m%d") # interpolation date being processed, converting the string using specific format
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month<-strftime(date, "%m") # current month of the date being processed
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LST_month<-paste("mm_",month,sep="") # name of LST month to be matched in the raster stack of covariates and data.frame
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#Adding layer LST to the raster stack
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pos<-match("LST",layerNames(s_raster)) #Find the position of the layer with name "LST", if not present pos=NA
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s_raster<-dropLayer(s_raster,pos) # If it exists drop layer
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pos<-match(LST_month,layerNames(s_raster)) #Find column with the current month for instance mm12
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r1<-raster(s_raster,layer=pos) #Select layer from stack
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layerNames(r1)<-"LST"
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s_raster<-addLayer(s_raster,r1) #Adding current month
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###Regression part 1: Creating a validation dataset by creating training and testing datasets
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mod_LST <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))] #Match interpolation date and monthly LST average
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ghcn.subsets[[i]] <- transform(ghcn.subsets[[i]],LST = mod_LST) #Add the variable LST to the subset dataset
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dst$LST<-dst[[LST_month]] #Add also to monthly 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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#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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datelabel=format(ISOdate(year,mo,day),"%b %d, %Y")
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###########
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# STEP 1 - LST 10 year monthly averages: THIS IS NOT USED IN CAI method
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###########
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themolst<-raster(molst,mo) #current month being processed saved in a raster image
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plot(themolst)
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###########
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# STEP 2 - Weather station means across same days: Monthly mean calculation
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###########
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modst=dst[dst$month==mo,] #Subsetting dataset for the relevant month of the date being processed
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##########
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# STEP 3 - get LST at stations
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##########
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sta_lola=modst[,c("lon","lat")] #Extracting locations of stations for the current month..
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proj_str="+proj=lcc +lat_1=43 +lat_2=45.5 +lat_0=41.75 +lon_0=-120.5 +x_0=400000 +y_0=0 +ellps=GRS80 +units=m +no_defs";
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lookup<-function(r,lat,lon) {
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xy<-project(cbind(lon,lat),proj_str);
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cidx<-cellFromXY(r,xy);
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return(r[cidx])
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}
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sta_tmax_from_lst=lookup(themolst,sta_lola$lat,sta_lola$lon) #Extracted values of LST for the stations
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#########
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# STEP 4 - bias at stations
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#########
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sta_bias=sta_tmax_from_lst-modst$TMax; #That is the difference between the monthly LST mean and monthly station mean
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#Added by Benoit
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modst$LSTD_bias<-sta_bias #Adding bias to data frame modst containning the monthly average for 10 years
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bias_xy=project(as.matrix(sta_lola),proj_str)
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# png(paste("LST_TMax_scatterplot_",dates[i],out_prefix,".png", sep=""))
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# plot(modst$TMax,sta_tmax_from_lst,xlab="Station mo Tmax",ylab="LST mo Tmax",main=paste("LST vs TMax for",datelabel,sep=" "))
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# abline(0,1)
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# dev.off()
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#added by Benoit
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#x<-ghcn.subsets[[i]] #Holds both training and testing for instance 161 rows for Jan 1
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x<-data_v
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d<-data_s
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pos<-match("value",names(d)) #Find column with name "value"
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#names(d)[pos]<-c("dailyTmax")
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names(d)[pos]<-y_var_name
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names(x)[pos]<-y_var_name
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#names(x)[pos]<-c("dailyTmax")
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d$dailyTmax=(as.numeric(d$dailyTmax))/10 #stored as 1/10 degree C to allow integer storage
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x$dailyTmax=(as.numeric(x$dailyTmax))/10 #stored as 1/10 degree C to allow integer storage
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pos<-match("station",names(d)) #Find column with name "value"
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names(d)[pos]<-c("id")
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names(x)[pos]<-c("id")
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names(modst)[1]<-c("id") #modst contains the average tmax per month for every stations...it has 193 rows
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dmoday=merge(modst,d,by="id",suffixes=c("",".y2")) #LOOSING DATA HERE!!! from 113 t0 103
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xmoday=merge(modst,x,by="id",suffixes=c("",".y2")) #LOOSING DATA HERE!!! from 48 t0 43
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mod_pat<-glob2rx("*.y2")
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var_pat<-grep(mod_pat,names(dmoday),value=FALSE) # using grep with "value" extracts the matching names
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dmoday<-dmoday[,-var_pat]
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mod_pat<-glob2rx("*.y2")
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var_pat<-grep(mod_pat,names(xmoday),value=FALSE) # using grep with "value" extracts the matching names
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xmoday<-xmoday[,-var_pat] #Removing duplicate columns
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#dmoday=merge(modst,d,by="id") #LOOSING DATA HERE!!! from 113 t0 103
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#xmoday=merge(modst,x,by="id") #LOOSING DATA HERE!!! from 48 t0 43
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#names(dmoday)[4]<-c("lat")
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#names(dmoday)[5]<-c("lon") #dmoday contains all the the information: BIAS, monn
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#names(xmoday)[4]<-c("lat")
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#names(xmoday)[5]<-c("lon") #dmoday contains all the the information: BIAS, monn
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data_v<-xmoday
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###
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#dmoday contains the daily tmax values for training with TMax being the monthly station tmax mean
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#xmoday contains the daily tmax values for validation with TMax being the monthly station tmax mean
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# windows()
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#png(paste("LST_TMax_scatterplot_",dates[i],out_prefix,".png", sep=""))
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png(paste("Daily_tmax_monthly_TMax_scatterplot_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],
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out_prefix,".png", sep=""))
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plot(dailyTmax~TMax,data=dmoday,xlab="Mo Tmax",ylab=paste("Daily for",datelabel),main="across stations in OR")
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#savePlot(paste("Daily_tmax_monthly_TMax_scatterplot_",dates[i],out_prefix,".png", sep=""), type="png")
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#png(paste("LST_TMax_scatterplot_",dates[i],out_prefix,".png", sep=""))
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dev.off()
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########
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# STEP 5 - interpolate bias/climatology
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########
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# ?? include covariates like elev, distance to coast, cloud frequency, tree height
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#library(fields)
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#windows()
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#quilt.plot(sta_lola,sta_bias,main="Bias at stations",asp=1)
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#US(add=T,col="magenta",lwd=2)
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#fitbias<-Tps(bias_xy,sta_bias) #use TPS or krige
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#Adding options to use only training stations: 07/11/2012
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bias_xy<-project(as.matrix(sta_lola),proj_str)
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clim_xy<-project(as.matrix(sta_lola),proj_str) #This is the coordinates of monthly station location (193)
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#bias_xy2=project(as.matrix(c(dmoday$lon,dmoday$lat),proj_str)
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if(bias_val==1){
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sta_bias<-dmoday$LSTD_bias
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bias_xy<-cbind(dmoday$x_OR83M,dmoday$y_OR83M) #This will use only stations from training daily samples for climatology step if bias_val=1
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}
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sta_clim<-modst$TMax #This contains the monthly climatology...used in the prediction of the monthly surface
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#fitbias<-Krig(bias_xy,sta_bias,theta=1e5) #use TPS or krige
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fitclim<-Krig(clim_xy,sta_clim,theta=1e5)
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#The output is a krig object using fields
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#mod9a<-fitbias
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mod9a<-fitclim
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# Creating plot of bias surface and saving it
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#X11()
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png(paste("Climtology_surface_LST_TMax_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],
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out_prefix,".png", sep="")) #Create file to write a plot
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datelabel2=format(ISOdate(year,mo,day),"%B ") #added by Benoit, label
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surface(fitclim,col=rev(terrain.colors(100)),asp=1,main=paste("Interpolated clim for",datelabel2,sep=" ")) #Plot to file
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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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#US(add=T,col="magenta",lwd=2)
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##########
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# STEP 7 - interpolate delta across space: this is the daily deviation from the monthly average
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##########
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daily_sta_lola=dmoday[,c("lon","lat")] #could be same as before but why assume merge does this - assume not
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daily_sta_xy=project(as.matrix(daily_sta_lola),proj_str)
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daily_delta=dmoday$dailyTmax-dmoday$TMax
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daily_deltaclim<-dmoday$dailyTmax-dmoday$TMax #For daily surface interpolation...
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daily_deltaclim_v<-data_v$dailyTmax-data_v$TMax #For validation...
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#dmoday$daily_deltaclim <-daily_deltaclim
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#fitdelta<-Tps(daily_sta_xy,daily_delta) #use TPS or krige
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fitdelta<-Krig(daily_sta_xy,daily_delta,theta=1e5) #use TPS or krige
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fitdeltaclim<-Krig(daily_sta_xy,daily_deltaclim,theta=1e5) #use TPS or krige
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#Kriging using fields package
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#mod9b<-fitdelta
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mod9b<-fitdeltaclim
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# Creating plot of bias surface and saving it
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#X11()
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png(paste("Deltaclim_surface_TMax_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],
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out_prefix,".png", sep=""))
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surface(fitdeltaclim,col=rev(terrain.colors(100)),asp=1,main=paste("Interpolated deltaclim for",datelabel,sep=" "))
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#savePlot(paste("Delta_surface_LST_TMax_",dates[i],out_prefix,".png", sep=""), type="png")
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dev.off()
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#US(add=T,col="magenta",lwd=2)
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#
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#### Added by Benoit on 06/19
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data_s<-dmoday #put the
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#data_s$daily_delta<-daily_delta
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data_s$daily_deltaclim<-daily_deltaclim
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data_v$daily_deltaclim<-daily_deltaclim_v
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#data_s$y_var<-daily_delta #y_var is the variable currently being modeled, may be better with BIAS!!
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#data_s$y_var<-data_s$LSTD_bias
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#### Added by Benoit ends
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#########
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# STEP 8 - assemble final answer - T= LST-Bias(interpolated)+delta(interpolated) (This is for fusion not implemented in this script...)
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# T= clim(interpolated) + deltaclim(interpolated) (This is for CAI)
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#########
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#bias_rast=interpolate(themolst,fitbias) #interpolation using function from raster package
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clim_rast=interpolate(themolst,fitclim) #interpolation using function from raster package
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#themolst is raster layer, fitbias is "Krig" object from bias surface
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#plot(bias_rast,main="Raster bias") #This not displaying...
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#Saving kriged surface in raster images
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data_name<-paste("clim_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],sep="")
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raster_name<-paste("CAI_",data_name,out_prefix,".rst", sep="")
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writeRaster(clim_rast, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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#daily_delta_rast=interpolate(themolst,fitdelta) #Interpolation of the bias surface...
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daily_deltaclim_rast=interpolate(themolst,fitdeltaclim) #Interpolation of the bias surface...
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#plot(daily_delta_rast,main="Raster Daily Delta")
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#Saving kriged surface in raster images
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data_name<-paste("deltaclim_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],sep="")
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raster_name<-paste("CAI_",data_name,out_prefix,".rst", sep="")
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writeRaster(daily_deltaclim_rast, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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234
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#tmax_predicted=themolst+daily_delta_rast-bias_rast #Final surface as a raster layer...eqt ok
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tmax_predicted<-daily_deltaclim_rast + clim_rast #Final surface as a raster layer...
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#tmp6<-data_s$daily_deltaclim +data_s$TMax
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#tmp7<-extract(tmax_predicted,data_s)
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#plot(tmax_predicted,main="Predicted daily")
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240
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#Saving kriged surface in raster images
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data_name<-paste("tmax_predicted_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],sep="")
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raster_name<-paste("CAI_",data_name,out_prefix,".rst", sep="")
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writeRaster(tmax_predicted, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
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244
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245
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########
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246
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# check: assessment of results: validation
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247
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########
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248
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RMSE<-function(x,y) {return(mean((x-y)^2)^0.5)}
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249
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MAE_fun<-function(x,y) {return(mean(abs(x-y)))}
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250
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#ME_fun<-function(x,y){return(mean(abs(y)))}
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251
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#FIT ASSESSMENT
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252
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sta_pred_data_s=lookup(tmax_predicted,data_s$lat,data_s$lon)
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253
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rmse_fit=RMSE(sta_pred_data_s,data_s$dailyTmax)
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254
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mae_fit=MAE_fun(sta_pred_data_s,data_s$dailyTmax)
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255
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256
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sta_pred=lookup(tmax_predicted,data_v$lat,data_v$lon)
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257
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#sta_pred=lookup(tmax_predicted,daily_sta_lola$lat,daily_sta_lola$lon)
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258
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#rmse=RMSE(sta_pred,dmoday$dailyTmax)
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259
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#pos<-match("value",names(data_v)) #Find column with name "value"
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260
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#names(data_v)[pos]<-c("dailyTmax")
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261
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tmax<-data_v$dailyTmax
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262
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#data_v$dailyTmax<-tmax
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263
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rmse=RMSE(sta_pred,tmax)
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264
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mae<-MAE_fun(sta_pred,tmax)
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265
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r2<-cor(sta_pred,tmax)^2 #R2, coef. of var
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266
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me<-mean(sta_pred-tmax)
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267
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268
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#plot(sta_pred~dmoday$dailyTmax,xlab=paste("Actual daily for",datelabel),ylab="Pred daily",main=paste("RMSE=",rmse))
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269
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270
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png(paste("Predicted_tmax_versus_observed_scatterplot_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",
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sampling_dat$run_samp[i],out_prefix,".png", sep=""))
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plot(sta_pred~tmax,xlab=paste("Actual daily for",datelabel),ylab="Pred daily",main=paste("RMSE=",rmse))
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273
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abline(0,1)
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#savePlot(paste("Predicted_tmax_versus_observed_scatterplot_",dates[i],out_prefix,".png", sep=""), type="png")
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dev.off()
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#resid=sta_pred-dmoday$dailyTmax
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resid=sta_pred-tmax
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278
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#quilt.plot(daily_sta_lola,resid)
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279
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280
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281
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###BEFORE GAM prediction the data object must be transformed to SDF
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282
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283
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coords<- data_v[,c('x_OR83M','y_OR83M')]
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284
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coordinates(data_v)<-coords
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285
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proj4string(data_v)<-CRS #Need to assign coordinates...
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286
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coords<- data_s[,c('x_OR83M','y_OR83M')]
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287
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coordinates(data_s)<-coords
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288
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proj4string(data_s)<-CRS #Need to assign coordinates..
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289
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coords<- modst[,c('x_OR83M','y_OR83M')]
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290
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coordinates(modst)<-coords
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291
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proj4string(modst)<-CRS #Need to assign coordinates..
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292
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293
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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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294
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nv<-nrow(data_v)
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295
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296
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###GAM PREDICTION
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297
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298
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#data_s$y_var<-data_s$dailyTmax #This shoudl be changed for any variable!!!
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299
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#data_v$y_var<-data_v$dailyTmax
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300
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#data_v$y_var<-data_v$daily_deltaclim
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301
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data_s$y_var<-data_s$daily_deltaclim
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302
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data_v$y_var<-data_v$daily_deltaclim
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303
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304
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if (climgam==1){ #This is an option to use covariates in the daily surface...
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305
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data_s$y_var<-data_s$TMax
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306
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data_v$y_var<-data_v$TMax
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307
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data_month<-modst
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308
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data_month$y_var<-modst$TMax
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309
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}
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310
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311
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#Model and response variable can be changed without affecting the script
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312
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313
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formula1 <- as.formula("y_var ~ s(lat) + s(lon) + s(ELEV_SRTM)", env=.GlobalEnv)
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314
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formula2 <- as.formula("y_var~ s(lat,lon)+ s(ELEV_SRTM)", env=.GlobalEnv)
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315
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formula3 <- as.formula("y_var~ s(lat) + s (lon) + s (ELEV_SRTM) + s (Northness)+ s (Eastness) + s(DISTOC)", env=.GlobalEnv)
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316
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formula4 <- as.formula("y_var~ s(lat) + s (lon) + s(ELEV_SRTM) + s(Northness) + s (Eastness) + s(DISTOC) + s(LST)", env=.GlobalEnv)
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317
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formula5 <- as.formula("y_var~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)", env=.GlobalEnv)
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318
|
formula6 <- as.formula("y_var~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)+s(LC1)", env=.GlobalEnv)
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319
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formula7 <- as.formula("y_var~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST)+s(LC3)", env=.GlobalEnv)
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320
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formula8 <- as.formula("y_var~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST) + s(LC1,LC3)", env=.GlobalEnv)
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321
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322
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#mod1<- try(gam(formula1, data=data_s))
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323
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#mod2<- try(gam(formula2, data=data_s)) #modified nesting....from 3 to 2
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324
|
#mod3<- try(gam(formula3, data=data_s))
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325
|
#mod4<- try(gam(formula4, data=data_s))
|
|
326
|
#mod5<- try(gam(formula5, data=data_s))
|
|
327
|
#mod6<- try(gam(formula6, data=data_s))
|
|
328
|
#mod7<- try(gam(formula7, data=data_s))
|
|
329
|
#mod8<- try(gam(formula8, data=data_s))
|
|
330
|
|
|
331
|
if (climgam==1){ #This will automatically use monthly station data in the second step
|
|
332
|
mod1<- try(gam(formula1, data=data_month))
|
|
333
|
mod2<- try(gam(formula2, data=data_month)) #modified nesting....from 3 to 2
|
|
334
|
mod3<- try(gam(formula3, data=data_month))
|
|
335
|
mod4<- try(gam(formula4, data=data_month))
|
|
336
|
mod5<- try(gam(formula5, data=data_month))
|
|
337
|
mod6<- try(gam(formula6, data=data_month))
|
|
338
|
mod7<- try(gam(formula7, data=data_month))
|
|
339
|
mod8<- try(gam(formula8, data=data_month))
|
|
340
|
|
|
341
|
} else if (climgam==0){ #This will use daily delta in the second step
|
|
342
|
|
|
343
|
mod1<- try(gam(formula1, data=data_s))
|
|
344
|
mod2<- try(gam(formula2, data=data_s)) #modified nesting....from 3 to 2
|
|
345
|
mod3<- try(gam(formula3, data=data_s))
|
|
346
|
mod4<- try(gam(formula4, data=data_s))
|
|
347
|
mod5<- try(gam(formula5, data=data_s))
|
|
348
|
mod6<- try(gam(formula6, data=data_s))
|
|
349
|
mod7<- try(gam(formula7, data=data_s))
|
|
350
|
mod8<- try(gam(formula8, data=data_s))
|
|
351
|
}
|
|
352
|
|
|
353
|
### Added by benoit
|
|
354
|
#Store results using TPS
|
|
355
|
j=nmodels+1
|
|
356
|
results_RMSE[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
357
|
results_RMSE[2]<- ns #number of stations used in the training stage
|
|
358
|
results_RMSE[3]<- "RMSE"
|
|
359
|
|
|
360
|
results_RMSE[j+3]<- rmse #Storing RMSE for the model j
|
|
361
|
|
|
362
|
results_RMSE_f[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
363
|
results_RMSE_f[2]<- ns #number of stations used in the training stage
|
|
364
|
results_RMSE_f[3]<- "RMSE_f"
|
|
365
|
results_RMSE_f[j+3]<- rmse_fit #Storing RMSE for the model j
|
|
366
|
|
|
367
|
results_MAE_f[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
368
|
results_MAE_f[2]<- ns #number of stations used in the training stage
|
|
369
|
results_MAE_f[3]<- "RMSE_f"
|
|
370
|
results_MAE_f[j+3]<- mae_fit #Storing RMSE for the model j
|
|
371
|
|
|
372
|
results_MAE[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
373
|
results_MAE[2]<- ns #number of stations used in the training stage
|
|
374
|
results_MAE[3]<- "MAE"
|
|
375
|
results_MAE[j+3]<- mae #Storing RMSE for the model j
|
|
376
|
|
|
377
|
results_ME[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
378
|
results_ME[2]<- ns #number of stations used in the training stage
|
|
379
|
results_ME[3]<- "ME"
|
|
380
|
results_ME[j+3]<- me #Storing RMSE for the model j
|
|
381
|
|
|
382
|
results_R2[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
383
|
results_R2[2]<- ns #number of stations used in the training stage
|
|
384
|
results_R2[3]<- "R2"
|
|
385
|
results_R2[j+3]<- r2 #Storing RMSE for the model j
|
|
386
|
|
|
387
|
pred_mod<-paste("pred_mod",j,sep="")
|
|
388
|
#Adding the results back into the original dataframes.
|
|
389
|
data_s[[pred_mod]]<-sta_pred_data_s
|
|
390
|
data_v[[pred_mod]]<-sta_pred
|
|
391
|
|
|
392
|
#Model assessment: RMSE and then krig the residuals....!
|
|
393
|
|
|
394
|
res_mod_s<- data_s$dailyTmax - data_s[[pred_mod]] #Residuals from kriging training
|
|
395
|
res_mod_v<- data_v$dailyTmax - data_v[[pred_mod]] #Residuals from kriging validation
|
|
396
|
|
|
397
|
name2<-paste("res_mod",j,sep="")
|
|
398
|
data_v[[name2]]<-as.numeric(res_mod_v)
|
|
399
|
data_s[[name2]]<-as.numeric(res_mod_s)
|
|
400
|
|
|
401
|
#ns<-nrow(data_s) #This is added to because some loss of data might have happened because of the averaging...
|
|
402
|
#nv<-nrow(data_v)
|
|
403
|
#browser()
|
|
404
|
|
|
405
|
for (j in 1:nmodels){
|
|
406
|
|
|
407
|
##Model assessment: specific diagnostic/metrics for GAM
|
|
408
|
|
|
409
|
name<-paste("mod",j,sep="") #modj is the name of The "j" model (mod1 if j=1)
|
|
410
|
mod<-get(name) #accessing GAM model ojbect "j"
|
|
411
|
|
|
412
|
#If mod "j" is not a model object
|
|
413
|
if (inherits(mod,"try-error")) {
|
|
414
|
results_m1[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
415
|
results_m1[1,2]<- ns #number of stations used in the training stage
|
|
416
|
results_m1[1,3]<- "AIC"
|
|
417
|
results_m1[1,j+3]<- NA
|
|
418
|
|
|
419
|
results_m2[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
420
|
results_m2[1,2]<- ns #number of stations used in the training
|
|
421
|
results_m2[1,3]<- "GCV"
|
|
422
|
results_m2[1,j+3]<- NA
|
|
423
|
|
|
424
|
results_m3[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
425
|
results_m3[1,2]<- ns #number of stations used in the training stage
|
|
426
|
results_m3[1,3]<- "DEV"
|
|
427
|
results_m3[1,j+3]<- NA
|
|
428
|
|
|
429
|
results_RMSE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
430
|
results_RMSE_f[1,2]<- ns #number of stations used in the training stage
|
|
431
|
results_RMSE_f[1,3]<- "RSME_f"
|
|
432
|
results_RMSE_f[1,j+3]<- NA
|
|
433
|
|
|
434
|
results_MAE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
435
|
results_MAE_f[1,2]<- ns #number of stations used in the training stage
|
|
436
|
results_MAE_f[1,3]<- "MAE_f"
|
|
437
|
results_MAE_f[1,j+3]<-NA
|
|
438
|
|
|
439
|
results_R2_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
440
|
results_R2_f[1,2]<- ns #number of stations used in the training stage
|
|
441
|
results_R2_f[1,3]<- "R2_f"
|
|
442
|
results_R2_f[1,j+3]<- NA #Storing R2 for the model j
|
|
443
|
|
|
444
|
|
|
445
|
results_RMSE[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
446
|
results_RMSE[1,2]<- ns #number of stations used in the training stage
|
|
447
|
results_RMSE[1,3]<- "RMSE"
|
|
448
|
results_RMSE[1,j+3]<- NA #Storing RMSE for the model j
|
|
449
|
results_MAE[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
450
|
results_MAE[1,2]<- ns #number of stations used in the training stage
|
|
451
|
results_MAE[1,3]<- "MAE"
|
|
452
|
results_MAE[1,j+3]<- NA #Storing MAE for the model j
|
|
453
|
results_ME[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
454
|
results_ME[1,2]<- ns #number of stations used in the training stage
|
|
455
|
results_ME[1,3]<- "ME"
|
|
456
|
results_ME[1,j+3]<- NA #Storing ME for the model j
|
|
457
|
results_R2[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
458
|
results_R2[1,2]<- ns #number of stations used in the training stage
|
|
459
|
results_R2[1,3]<- "R2"
|
|
460
|
results_R2[1,j+3]<- NA #Storing R2 for the model j
|
|
461
|
|
|
462
|
}
|
|
463
|
|
|
464
|
#If mod is a modelobject
|
|
465
|
|
|
466
|
#If mod "j" is not a model object
|
|
467
|
if (inherits(mod,"gam")) {
|
|
468
|
|
|
469
|
# model specific metrics
|
|
470
|
results_m1[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
471
|
results_m1[1,2]<- ns #number of stations used in the training stage
|
|
472
|
results_m1[1,3]<- "AIC"
|
|
473
|
results_m1[1,j+3]<- AIC (mod)
|
|
474
|
|
|
475
|
results_m2[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
476
|
results_m2[1,2]<- ns #number of stations used in the training
|
|
477
|
results_m2[1,3]<- "GCV"
|
|
478
|
results_m2[1,j+3]<- mod$gcv.ubre
|
|
479
|
|
|
480
|
results_m3[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
481
|
results_m3[1,2]<- ns #number of stations used in the training stage
|
|
482
|
results_m3[1,3]<- "DEV"
|
|
483
|
results_m3[1,j+3]<- mod$deviance
|
|
484
|
|
|
485
|
##Model assessment: general diagnostic/metrics
|
|
486
|
##validation: using the testing data
|
|
487
|
if (predval==1) {
|
|
488
|
|
|
489
|
##Model assessment: specific diagnostic/metrics for GAM
|
|
490
|
|
|
491
|
name<-paste("mod",j,sep="") #modj is the name of The "j" model (mod1 if j=1)
|
|
492
|
mod<-get(name) #accessing GAM model ojbect "j"
|
|
493
|
|
|
494
|
s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame
|
|
495
|
|
|
496
|
rpred<- predict(mod, newdata=s_sgdf, se.fit = TRUE) #Using the coeff to predict new values.
|
|
497
|
y_pred<-rpred$fit
|
|
498
|
raster_pred<-r1
|
|
499
|
layerNames(raster_pred)<-"y_pred"
|
|
500
|
values(raster_pred)<-as.numeric(y_pred)
|
|
501
|
data_name<-paste("predicted_mod",j,"_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],sep="")
|
|
502
|
raster_name<-paste("GAMCAI_",data_name,out_prefix,".rst", sep="")
|
|
503
|
writeRaster(raster_pred, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
|
|
504
|
#writeRaster(r2, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
|
|
505
|
|
|
506
|
tmax_predicted_CAI<-raster_pred + clim_rast #Final surface as a raster layer...taht is if daily prediction with GAM
|
|
507
|
if (climgam==1){
|
|
508
|
tmax_predicted_CAI<-raster_pred + daily_deltaclim_rast #Final surface as a raster layer...
|
|
509
|
}
|
|
510
|
|
|
511
|
layerNames(tmax_predicted_CAI)<-"y_pred"
|
|
512
|
data_name<-paste("predicted_mod",j,"_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],sep="")
|
|
513
|
raster_name<-paste("GAMCAI_tmax_predicted_",data_name,out_prefix,".rst", sep="")
|
|
514
|
writeRaster(tmax_predicted_CAI, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
|
|
515
|
#writeRaster(r2, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
|
|
516
|
|
|
517
|
pred_sgdf<-as(tmax_predicted_CAI,"SpatialGridDataFrame") #Conversion to spatial grid data frame
|
|
518
|
#rpred_val_s <- overlay(raster_pred,data_s) #This overlays the kriged surface tmax and the location of weather stations
|
|
519
|
|
|
520
|
rpred_val_s <- overlay(pred_sgdf,data_s) #This overlays the kriged surface tmax and the location of weather stations
|
|
521
|
rpred_val_v <- overlay(pred_sgdf,data_v) #This overlays the kriged surface tmax and the location of weather stations
|
|
522
|
|
|
523
|
pred_mod<-paste("pred_mod",j,sep="")
|
|
524
|
#Adding the results back into the original dataframes.
|
|
525
|
data_s[[pred_mod]]<-rpred_val_s$y_pred
|
|
526
|
data_v[[pred_mod]]<-rpred_val_v$y_pred
|
|
527
|
|
|
528
|
#Model assessment: RMSE and then krig the residuals....!
|
|
529
|
|
|
530
|
res_mod_s<- data_s$dailyTmax - data_s[[pred_mod]] #Residuals from kriging training
|
|
531
|
res_mod_v<- data_v$dailyTmax - data_v[[pred_mod]] #Residuals from kriging validation
|
|
532
|
|
|
533
|
}
|
|
534
|
|
|
535
|
if (predval==0) {
|
|
536
|
|
|
537
|
y_mod<- predict(mod, newdata=data_v, se.fit = TRUE) #Using the coeff to predict new values.
|
|
538
|
|
|
539
|
pred_mod<-paste("pred_mod",j,sep="")
|
|
540
|
#Adding the results back into the original dataframes.
|
|
541
|
data_s[[pred_mod]]<-as.numeric(mod$fit)
|
|
542
|
data_v[[pred_mod]]<-as.numeric(y_mod$fit)
|
|
543
|
|
|
544
|
#Model assessment: RMSE and then krig the residuals....!
|
|
545
|
#y_var_name<-"dailyTmax"
|
|
546
|
res_mod_s<- data_s$dailyTmax - data_s[[pred_mod]] #Residuals from kriging training
|
|
547
|
res_mod_v<- data_v$dailyTmax - data_v[[pred_mod]] #Residuals from kriging validation
|
|
548
|
}
|
|
549
|
|
|
550
|
#y_var_fit= mod$fit #move it
|
|
551
|
#Use res_mod_s so the R2 is based on daily station training
|
|
552
|
R2_mod_f<- cor(data_s$dailyTmax,res_mod_s, use="complete")^2
|
|
553
|
RMSE_mod_f<- sqrt(mean(res_mod_s^2,na.rm=TRUE))
|
|
554
|
|
|
555
|
results_RMSE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
556
|
results_RMSE_f[1,2]<- ns #number of stations used in the training stage
|
|
557
|
results_RMSE_f[1,3]<- "RSME_f"
|
|
558
|
#results_RMSE_f[1,j+3]<-sqrt(mean(mod$residuals^2,na.rm=TRUE))
|
|
559
|
results_RMSE_f[1,j+3]<-sqrt(mean(res_mod_s^2,na.rm=TRUE))
|
|
560
|
|
|
561
|
results_MAE_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
562
|
results_MAE_f[1,2]<- ns #number of stations used in the training stage
|
|
563
|
results_MAE_f[1,3]<- "MAE_f"
|
|
564
|
#results_MAE_f[j+3]<-sum(abs(y_var_fit-data_s$y_var))/ns
|
|
565
|
results_MAE_f[1,j+3]<-mean(abs(res_mod_s),na.rm=TRUE)
|
|
566
|
|
|
567
|
results_R2_f[1,1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
568
|
results_R2_f[1,2]<- ns #number of stations used in the training stage
|
|
569
|
results_R2_f[1,3]<- "R2_f"
|
|
570
|
results_R2_f[1,j+3]<- R2_mod_f #Storing R2 for the model j
|
|
571
|
|
|
572
|
#### Now calculate validation metrics
|
|
573
|
res_mod<-res_mod_v
|
|
574
|
|
|
575
|
#RMSE_mod <- sqrt(sum(res_mod^2)/nv) #RMSE FOR REGRESSION STEP 1: GAM
|
|
576
|
RMSE_mod<- sqrt(mean(res_mod^2,na.rm=TRUE))
|
|
577
|
#MAE_mod<- sum(abs(res_mod),na.rm=TRUE)/(nv-sum(is.na(res_mod))) #MAE from kriged surface validation
|
|
578
|
MAE_mod<- mean(abs(res_mod), na.rm=TRUE)
|
|
579
|
#ME_mod<- sum(res_mod,na.rm=TRUE)/(nv-sum(is.na(res_mod))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
|
|
580
|
ME_mod<- mean(res_mod,na.rm=TRUE) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
|
|
581
|
#R2_mod<- cor(data_v$y_var,data_v[[pred_mod]])^2 #R2, coef. of var FOR REGRESSION STEP 1: GAM
|
|
582
|
pred_mod<-paste("pred_mod",j,sep="")
|
|
583
|
R2_mod<- cor(data_v$dailyTmax,data_v[[pred_mod]], use="complete")^2
|
|
584
|
results_RMSE[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
585
|
results_RMSE[2]<- ns #number of stations used in the training stage
|
|
586
|
results_RMSE[3]<- "RMSE"
|
|
587
|
results_RMSE[j+3]<- RMSE_mod #Storing RMSE for the model j
|
|
588
|
results_MAE[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
589
|
results_MAE[2]<- ns #number of stations used in the training stage
|
|
590
|
results_MAE[3]<- "MAE"
|
|
591
|
results_MAE[j+3]<- MAE_mod #Storing MAE for the model j
|
|
592
|
results_ME[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
593
|
results_ME[2]<- ns #number of stations used in the training stage
|
|
594
|
results_ME[3]<- "ME"
|
|
595
|
results_ME[j+3]<- ME_mod #Storing ME for the model j
|
|
596
|
results_R2[1]<- sampling_dat$date[i] #storing the interpolation dates in the first column
|
|
597
|
results_R2[2]<- ns #number of stations used in the training stage
|
|
598
|
results_R2[3]<- "R2"
|
|
599
|
results_R2[j+3]<- R2_mod #Storing R2 for the model j
|
|
600
|
|
|
601
|
#Saving residuals and prediction in the dataframes: tmax predicted from GAM
|
|
602
|
|
|
603
|
name2<-paste("res_mod",j,sep="")
|
|
604
|
data_v[[name2]]<-as.numeric(res_mod_v)
|
|
605
|
data_s[[name2]]<-as.numeric(res_mod_s)
|
|
606
|
#end of loop calculating RMSE
|
|
607
|
}
|
|
608
|
}
|
|
609
|
|
|
610
|
#if (i==length(dates)){
|
|
611
|
|
|
612
|
|
|
613
|
#Specific diagnostic measures related to the testing datasets
|
|
614
|
|
|
615
|
results_table_RMSE<-as.data.frame(results_RMSE)
|
|
616
|
results_table_MAE<-as.data.frame(results_MAE)
|
|
617
|
results_table_ME<-as.data.frame(results_ME)
|
|
618
|
results_table_R2<-as.data.frame(results_R2)
|
|
619
|
results_table_RMSE_f<-as.data.frame(results_RMSE_f)
|
|
620
|
results_table_MAE_f<-as.data.frame(results_MAE_f)
|
|
621
|
results_table_R2_f<-as.data.frame(results_R2_f)
|
|
622
|
|
|
623
|
results_table_m1<-as.data.frame(results_m1)
|
|
624
|
results_table_m2<-as.data.frame(results_m2)
|
|
625
|
results_table_m3<-as.data.frame(results_m3)
|
|
626
|
|
|
627
|
tb_metrics1<-rbind(results_table_RMSE,results_table_MAE, results_table_ME,
|
|
628
|
results_table_R2,results_table_RMSE_f,results_table_MAE_f,results_table_R2_f) #
|
|
629
|
tb_metrics2<-rbind(results_table_m1,results_table_m2, results_table_m3)
|
|
630
|
cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8","mod9")
|
|
631
|
colnames(tb_metrics1)<-cname
|
|
632
|
cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8")
|
|
633
|
colnames(tb_metrics2)<-cname
|
|
634
|
#colnames(results_table_RMSE)<-cname
|
|
635
|
#colnames(results_table_RMSE_f)<-cname
|
|
636
|
#tb_diagnostic1<-results_table_RMSE #measures of validation
|
|
637
|
#tb_diagnostic2<-results_table_RMSE_f #measures of fit
|
|
638
|
|
|
639
|
#write.table(tb_diagnostic1, file= paste(path,"/","results_fusion_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
|
|
640
|
|
|
641
|
#}
|
|
642
|
print(paste(sampling_dat$date[i],"processed"))
|
|
643
|
# Kriging object may need to be modified...because it contains the full image of prediction!!
|
|
644
|
##loop through model objects data frame and set field to zero...
|
|
645
|
|
|
646
|
mod_obj<-list(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8,mod9a,mod9b)
|
|
647
|
names(mod_obj)<-c("mod1","mod2","mod3","mod4","mod5","mod6","mod7","mod8","mod9a","mod9b") #generate names automatically??
|
|
648
|
#results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2)
|
|
649
|
#results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2,mod_obj)
|
|
650
|
results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2,mod_obj,data_month)
|
|
651
|
names(results_list)<-c("data_s","data_v","tb_metrics1","tb_metrics2","mod_obj","data_month")
|
|
652
|
save(results_list,file= paste(path,"/","results_list_metrics_objects_",sampling_dat$date[i],"_",sampling_dat$prop[i],"_",sampling_dat$run_samp[i],
|
|
653
|
out_prefix,".RData",sep=""))
|
|
654
|
return(results_list)
|
|
655
|
#return(tb_diagnostic1)
|
|
656
|
}
|
