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Revision defe53e5

Added by Benoit Parmentier about 12 years ago

GWR assessment by visualizing residuals using kriging

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climate/research/oregon/interpolation/gwr_reg.R
8 8 
library(spdep)
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library(rgdal)
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library(spgwr)
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library(gpclib)


  		




  
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library(PBSmapping)


  		




  
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library(maptools)


  		




  
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library(gstat)


  		




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###Parameters and arguments


  		




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infile1<-"ghcn_or_tmax_b_03032012_OR83M.shp" 


  		




  ......




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#infile2<-"dates_interpolation_03012012.txt"  # list of 10 dates for the regression


  		




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infile2<-"dates_interpolation_03052012.txt"


  		




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prop<-0.3


  		




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out_prefix<-"_03132012_0"


  		




  
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out_prefix<-"_03272012_Res_fit"


  		




  
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###Reading the shapefile and raster image from the local directory


  		




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###Reading the shapefile from the local directory


  		




  
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mean_LST<- readGDAL("mean_day244_rescaled.rst")  #This reads the whole raster in memory and provide a grid for kriging


  		




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ghcn<-readOGR(".", "ghcn_or_tmax_b_03032012_OR83M") 


  		




  
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proj4string(ghcn) #This retrieves the coordinate system for the SDF


  		




  
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CRS_ghcn<-proj4string(ghcn) #this can be assigned to mean_LST!!!


  		




  
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proj4string(mean_LST)<-CRS_ghcn #Assigning coordinates information


  		




  
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# Creating state outline from county


  		




  
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orcnty<-readOGR(".", "orcnty24_OR83M")


  		




  
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proj4string(orcnty) #This retrieves the coordinate system for the SDF


  		




  
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lps <-getSpPPolygonsLabptSlots(orcnty)  #Getting centroids county labels


  		




  
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IDOneBin <- cut(lps[,1], range(lps[,1]), include.lowest=TRUE)  #Creating one bin var


  		




  
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gpclibPermit() #Set the gpclib to True to allow union


  		




  
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OR_state <- unionSpatialPolygons(orcnty ,IDOneBin) #Dissolve based on bin var


  		




  
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# Adding variables for the regression


  		




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ghcn$Northness<- cos(ghcn$ASPECT) #Adding a variable to the dataframe


  		




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ghcn$Eastness <- sin(ghcn$ASPECT)  #adding variable to the dataframe.


  		




  ......




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results <- matrix(1,length(dates),3)            #This is a matrix containing the diagnostic measures from the GAM models.


  		




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#Screening for bad values


  		




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#tmax~ lon + lat + ELEV_SRTM + Eastness + Northness + DISTOC


  		




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#tmax range: min max)


  		




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ghcn_test<-subset(ghcn,ghcn$tmax>-150 & ghcn$tmax<400)


  		




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ghcn_test2<-subset(ghcn_test,ghcn_test$ELEV_SRTM>0)


  		




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ghcn<-ghcn_test2


  		




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#lon range


  		




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#lat range


  		




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#ELEV_SRTM


  		




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#Eastness


  		




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#Northness


  		




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#ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==as.numeric(d)))#this creates a list of 10 subsets data


  		




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ghcn.subsets <-lapply(dates, function(d) subset(ghcn, ghcn$date==as.numeric(d)))


  		




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#ghcn.subsets <-subset(ghcn,ghcn$date=="20100101")


  		




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#summary(lm(y~x,data=df,weights=(df$wght1)^(3/4))


  		




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#ggwr.sel: find the bandwith from the data


  		




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


  		




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for(i in 1:length(dates)){            # start of the for loop #1


  		




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  ......




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


  		




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


  		




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  results[i,3]<- RMSE_f


  		




  
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  #Kriging residuals!!


  		




  
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  X11()


  		




  
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  hscat(residuals~1,data_s,(0:9)*20000) # 9 lag classes with 20,000m width


  		




  
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  v<-variogram(residuals~1, data_s)


  		




  
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  plot(v)


  		




  
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  tryCatch(v.fit<-fit.variogram(v,vgm(1,"Sph", 150000,1)),error=function()next)


  		




  
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  gwr_res_krige<-krige(residuals~1, data_s,mean_LST, v.fit)#mean_LST provides the data grid/raster image for the kriging locations.


  		




  
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  # GWR visualization of Residuals fit over space


  		




  
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  grays = gray.colors(5,0.45, 0.95)


  		




  
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  image(gwr_res_krige,col=grays) #needs to change to have a bipolar palette !!!


  		




  
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  #image(mean_LST, col=grays,breaks = c(185,245,255,275,315,325))


  		




  
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  plot(OR_state, axes = TRUE, add=TRUE)


  		




  
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  plot(data_s, pch=1, col="red", cex= abs(data_s$residuals)/10, add=TRUE) #Taking the absolute values because residuals are 


  		




  
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  LegVals<- c(0,20,40,80,110)


  		




  
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  legend("topleft", legend=LegVals,pch=1,col="red",pt.cex=LegVals/10,bty="n",title= "residuals")


  		




  
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  legend("left", legend=c("275-285","285-295","295-305", "305-315","315-325"),fill=grays, bty="n", title= "LST mean DOY=244")


  		




  
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  savePlot(paste(data_name,out_prefix,".png", sep=""), type="png")


  		




  
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  dev.off()


  		




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  }


  		




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## Plotting and saving diagnostic measures


  		




  ......




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write.csv(results_table, file= paste(path,"/","results_GWR_Assessment",out_prefix,".txt",sep=""))


  		




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# End of script##########


  		




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


  		












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