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####################Interpolation of Tmax for 10 dates.#####################
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#This script interpolates station tmax values for the Oregon case study.It provides a mean to asssess the effect of random sampling and proportion
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# of validation hold out on the RMSE.This program loads the station data from a csv file
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#and perform one type of regression: general additive model (GAM) with different variables:
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# Lat, long, ELEV_SRTM, Eastness, Northness, DISTOC, mean_LST_monthly, Land Cover proportions.
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#Note that this program:
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#1)assumes that the csv file is in the current working
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#2)extract relevant variables from raster images before performing the regressions.
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#3)does not clear memory workspace at the start or end of script.
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#This scripts predicts tmax using GAM and LST derived from MOD11A1.
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#Interactions terms are also included and assessed using the RMSE from validation dataset.
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#There are 10 dates used for the GAM interpolation. The dates must be provided as a textfile.
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#Script created by Benoit Parmentier on April 25, 2012.
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###Loading r library and packages # loading the raster package
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library(gtools) # loading ...
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library(mgcv)
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library(sp)
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library(spdep)
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library(rgdal)
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###Parameters and arguments
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infile1<-"ghcn_or_tmax_b_04142012_OR83M.shp"
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path<-"/data/computer/parmentier/Data/IPLANT_project/data_Oregon_stations"
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#path<-"H:/Data/IPLANT_project/data_Oregon_stations"
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setwd(path)
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infile2<-"dates_interpolation_03052012.txt" #List of 10 dates for the regression
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prop<-0.3 #Proportion of testing retained for validation
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n_runs<- 30 #Number of runs
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out_prefix<-"_05142012_run03_LST"
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infile3<-"LST_dates_var_names.txt" #List of LST averages.
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infile4<-"models_interpolation_05142012.txt"
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#######START OF THE SCRIPT #############
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###Reading the station data and setting up for models' comparison
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filename<-sub(".shp","",infile1) #Removing the extension from file.
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ghcn<-readOGR(".", filename) #reading shapefile
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ghcn = transform(ghcn,Northness = cos(ASPECT)) #Adding a variable to the dataframe
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ghcn = transform(ghcn,Eastness = sin(ASPECT)) #adding variable to the dataframe.
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ghcn = transform(ghcn,Northness_w = sin(slope)*cos(ASPECT)) #Adding a variable to the dataframe
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ghcn = transform(ghcn,Eastness_w = sin(slope)*sin(ASPECT)) #adding variable to the dataframe.
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#Note that "transform" output is a data.frame not spatial object
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#set.seed(100) #modify this to a seed variable allowing different runs.
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dates <-readLines(paste(path,"/",infile2, sep=""))
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LST_dates <-readLines(paste(path,"/",infile3, sep=""))
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models <-readLines(paste(path,"/",infile4, sep=""))
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#results <- matrix(1,length(dates),14) #This is a matrix containing the diagnostic measures from the GAM models.
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results_AIC<- matrix(1,length(dates),length(models)+3)
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results_GCV<- matrix(1,length(dates),length(models)+3)
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results_DEV<- matrix(1,length(dates),length(models)+3)
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results_RMSE<- matrix(1,length(dates),length(models)+3)
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RMSE_run<-matrix(1,length(dates),1)
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cor_LST_LC1<-matrix(1,length(dates),1) #correlation LST-LC1
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cor_LST_LC3<-matrix(1,length(dates),1) #correlation LST-LC3
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cor_LST_tmax<-matrix(1,length(dates),1) #correlation LST-tmax
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#Screening for bad values
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#RMSE_run<-matrix(1,lenth)
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results_RMSE_all<- matrix(1,length(dates)*n_runs,length(models)+3)
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results_RMSE_all2<- matrix(1,0,length(models)+3)
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ghcn_all<-ghcn
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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$ELEV_SRTM>0)
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ghcn<-ghcn_test2
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#month_var<-c("mm_01","mm_02","mm_03","mm_04","mm_05","mm_06","mm_07","mm_08","mm_09", "mm_10", "mm_11", "mm_12")
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## looping through the dates...
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#Changed this section into a nested loop, looping through the number of models
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for(j in 1:n_runs){
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ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==d)) #this creates a list of 10 subset data
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for(i in 1:length(dates)){ # start of the for loop #1
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date<-strptime(dates[i], "%Y%m%d")
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month<-strftime(date, "%m")
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LST_month<-paste("mm_",month,sep="")
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###Regression part 1: Creating a validation dataset by creating training and testing datasets
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mod <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))]
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ghcn.subsets[[i]] = transform(ghcn.subsets[[i]],LST = mod)
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#Screening LST values
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#ghcn.subsets[[i]]<-subset(ghcn.subsets[[i]],ghcn.subsets[[i]]$LST> 258 & ghcn.subsets[[i]]$LST<313)
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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.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)
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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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####Regression part 2: GAM models
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mod1<- gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM), data=data_s)
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mod2<- gam(tmax~ s(lat,lon,ELEV_SRTM), data=data_s)
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mod3<- gam(tmax~ s(lat) + s (lon) + s (ELEV_SRTM) + s (Northness)+ s (Eastness) + s(DISTOC), data=data_s)
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mod4<- gam(tmax~ s(lat) + s (lon) + s(ELEV_SRTM) + s(Northness) + s (Eastness) + s(DISTOC) + s(LST), data=data_s)
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mod5<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST), data=data_s)
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mod6<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC1), data=data_s)
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mod7<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST,LC3), data=data_s)
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mod8<- gam(tmax~ s(lat,lon) +s(ELEV_SRTM) + s(Northness,Eastness) + s(DISTOC) + s(LST) + s(LC1), data=data_s)
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####Regression part 3: Calculating and storing diagnostic measures
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results_AIC[i,1]<- j
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results_AIC[i,2]<- dates[i] #storing the interpolation dates in the first column
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results_AIC[i,3]<- ns #number of stations used in the training stage
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results_AIC[i,4]<- AIC (mod1)
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results_AIC[i,5]<- AIC (mod2)
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results_AIC[i,6]<- AIC (mod3)
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results_AIC[i,7]<- AIC (mod4)
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results_AIC[i,8]<- AIC (mod5)
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results_AIC[i,9]<- AIC (mod6)
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results_AIC[i,10]<- AIC (mod7)
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results_GCV[i,1]<- j # "j" is the counter index for the number of runs.
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results_GCV[i,2]<- dates[i] #storing the interpolation dates in the first column
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results_GCV[i,3]<- ns #number of stations used in the training stage
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results_GCV[i,4]<- mod1$gcv.ubre
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results_GCV[i,5]<- mod2$gcv.ubre
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results_GCV[i,6]<- mod3$gcv.ubre
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results_GCV[i,7]<- mod4$gcv.ubre
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results_GCV[i,8]<- mod5$gcv.ubre
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results_GCV[i,9]<- mod6$gcv.ubre
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results_GCV[i,10]<- mod7$gcv.ubre
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results_DEV[i,1]<- j # "j" is the counter index for the number of runs.
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results_DEV[i,2]<- dates[i] #storing the interpolation dates in the first column
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results_DEV[i,3]<- ns #number of stations used in the training stage
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results_DEV[i,4]<- mod1$deviance
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results_DEV[i,5]<- mod2$deviance
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results_DEV[i,6]<- mod3$deviance
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results_DEV[i,7]<- mod4$deviance
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results_DEV[i,8]<- mod5$deviance
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results_DEV[i,9]<- mod6$deviance
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results_DEV[i,10]<- mod7$deviance
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#####VALIDATION: Prediction checking the results using the testing data########
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y_mod1<- predict(mod1, newdata=data_v, se.fit = TRUE) #Using the coeff to predict new values.
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y_mod2<- predict(mod2, newdata=data_v, se.fit = TRUE)
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y_mod3<- predict(mod3, newdata=data_v, se.fit = TRUE)
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y_mod4<- predict(mod4, newdata=data_v, se.fit = TRUE)
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y_mod5<- predict(mod5, newdata=data_v, se.fit = TRUE)
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y_mod6<- predict(mod6, newdata=data_v, se.fit = TRUE)
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y_mod7<- predict(mod7, newdata=data_v, se.fit = TRUE)
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res_mod1<- data_v$tmax - y_mod1$fit #Residuals for GMA model that resembles the ANUSPLIN interpolation
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res_mod2<- data_v$tmax - y_mod2$fit #Residuals for GAM model that resembles the PRISM interpolation
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res_mod3<- data_v$tmax - y_mod3$fit
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res_mod4<- data_v$tmax - y_mod4$fit
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res_mod5<- data_v$tmax - y_mod5$fit
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res_mod6<- data_v$tmax - y_mod6$fit
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res_mod7<- data_v$tmax - y_mod7$fit
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RMSE_mod1 <- sqrt(sum(res_mod1^2)/nv)
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RMSE_mod2 <- sqrt(sum(res_mod2^2)/nv)
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RMSE_mod3 <- sqrt(sum(res_mod3^2)/nv)
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RMSE_mod4 <- sqrt(sum(res_mod4^2)/nv)
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RMSE_mod5 <- sqrt(sum(res_mod5^2)/nv)
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RMSE_mod6 <- sqrt(sum(res_mod6^2)/nv)
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RMSE_mod7 <- sqrt(sum(res_mod7^2)/nv)
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results_RMSE[i,1]<- j
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results_RMSE[i,2]<- dates[i] #storing the interpolation dates in the first column
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results_RMSE[i,3]<- ns #number of stations used in the training stage
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results_RMSE[i,4]<- RMSE_mod1
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results_RMSE[i,5]<- RMSE_mod2
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results_RMSE[i,6]<- RMSE_mod3
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results_RMSE[i,7]<- RMSE_mod4
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results_RMSE[i,8]<- RMSE_mod5
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results_RMSE[i,9]<- RMSE_mod6
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results_RMSE[i,10]<- RMSE_mod7
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#Saving dataset in dataframes
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data_name<-paste("ghcn_v_",dates[[i]],sep="")
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assign(data_name,data_v)
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data_name<-paste("ghcn_s_",dates[[i]],sep="")
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assign(data_name,data_s)
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#ghcn_v<-ls(pattern="ghcn_v_")
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# end of the for loop #2 (nested in loop #1)
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}
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results_RMSEnum <-results_RMSE
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results_AICnum <-results_AIC
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mode(results_RMSEnum)<- "numeric"
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mode(results_AICnum)<- "numeric"
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# Make it numeric first
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# Now turn it into a data.frame...
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results_table_RMSE<-as.data.frame(results_RMSEnum)
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results_table_AIC<-as.data.frame(results_AICnum)
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colnames(results_table_RMSE)<-c("run","dates","ns","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7")
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colnames(results_table_AIC)<-c("run","dates","ns","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7")
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write.table(results_table_RMSE, file= paste(path,"/","results_GAM_Assessment",out_prefix,".txt",sep=""), sep=",", col.names=FALSE, append=TRUE)
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#write.table(results_table_AIC, file= paste(path,"/","results_GAM_Assessment",out_prefix,".txt",sep=""),sep=",", append=TRUE)
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#results_RMSE_all Need to put all the results in one data.frame...
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results_RMSE_all2<-rbind(results_RMSE_all2,results_table_RMSE)
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#Print(j) #This is the run umber printed to the console.
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} #end of loop 1
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write.table(results_RMSE_all2, file= paste(path,"/","results_GAM_Assessment",out_prefix,"all.txt",sep=""), sep=",", col.names=TRUE)
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###Analysing the results from the 365 days run: Summarize by month
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#
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# for(i in 1:nrow(results_table_RMSE)){
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# date<-results_table_RMSE$dates[i]
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# date<-strptime(date, "%Y%m%d")
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# results_table_RMSE$month[i]<-as.integer(strftime(date, "%m"))
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# }
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#
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# average<-aggregate(cbind(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)~month,data=results_table_RMSE,mean, na.rm=TRUE)
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# average<-aggregate(cbind(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)~month,data=results_table_RMSE, FUN=mean)
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# #average on all the data.frame
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# averaget<-aggregate(results_table_RMSE, by=list(results_table_RMSE$month),FUN=mean, na.rm=TRUE)
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# #mediant<-aggregate(results_table_RMSE, by=list(results_table_RMSE$month),FUN=median, na.rm=TRUE)
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# #average_lowt<-aggregate(results_table_RMSE, by=list(results_table_RMSE$month), FUN=function(v) t.test(v)$conf.int[1])
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# #average_up<-aggregate(cbind(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)~month,data=results_table_RMSE, function(v) t.test(v)$conf.int[2])
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#
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# median<-aggregate(cbind(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)~month,data=results_table_RMSE, median, na.rm=TRUE)
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# average_low<-aggregate(cbind(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)~month,data=results_table_RMSE, function(v) t.test(v)$conf.int[1])
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# average_up<-aggregate(cbind(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)~month,data=results_table_RMSE, function(v) t.test(v)$conf.int[2])
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#
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# mod<-names(averaget)
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# mod<-mod[4:11]
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# #Saving graphic plots
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# for(i in 1:length(mod)){
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# X11(width=14,height=10)
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# name<-mod[i]
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# barplot2(average[[name]],plot.ci=TRUE, ci.l=average_low[[name]], ci.u=average_up[[name]],main="Mean RMSE per month", names.arg=c("Jan", "Feb", "Mar", "Apr", "May", "Jun","Jul", "Aug", "Sep","Oct", "Nov", "Dec"),ylim=c(20,30),ylab="RMSE in tenth deg C",xlab=name)
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# #title(paste("Sampling RMSE for mod",i,sep=""))
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# savePlot(paste("barplot_results_RMSE_month_",name,out_prefix,".png", sep=""), type="png")
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# dev.off()
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# }
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#
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#
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# for(i in 1:length(mod)){
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# X11(width=14,height=10)
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# name<-mod[i]
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# barplot2(average[[name]],plot.ci=TRUE, ci.l=average_low[[name]], ci.u=average_up[[name]],main=paste(" Mean RMSE per month ",name, sep=""), names.arg=c("Jan", "Feb", "Mar", "Apr", "May", "Jun","Jul", "Aug", "Sep","Oct", "Nov", "Dec"),ylim=c(20,30),ylab="RMSE in tenth deg C",xlab=name)
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# #title(paste("Sampling RMSE for mod",i,sep=""))
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# savePlot(paste("barplot_results_RMSE_month_",name,out_prefix,".png", sep=""), type="png")
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# dev.off()
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#
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# X11(width=14,height=10)
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# name<-mod[i]
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# hist(results_table_RMSE[[name]],breaks=15, main=paste(" Histogram RMSE_",name, sep=""),xlab=paste("RMSE ",name, sep=""))
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# savePlot(paste("Hist_results_RMSE_365_",name,out_prefix,".png", sep=""), type="png")
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# dev.off()
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258
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#
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259
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# }
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260
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#
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261
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# for(i in 1:length(mod)){
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262
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# X11(width=14,height=10)
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263
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# name<-mod[i]
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264
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# hist(results_table_RMSE[[name]],breaks=15, main=paste(" Histogram RMSE_",name, sep=""),xlab=paste("RMSE ",name, sep=""))
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265
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# savePlot(paste("Hist_results_RMSE_365_",name,out_prefix,".png", sep=""), type="png")
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266
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# dev.off()
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267
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# }
|
|
268
|
#
|
|
269
|
# r<-(results_RMSE_all2[,4:10]) #selecting only the columns related to models...
|
|
270
|
#
|
|
271
|
# mean_r<-sapply(r, mean)
|
|
272
|
# median_r<-sapply(r, median)
|
|
273
|
# sd_r<-sapply(r, sd)
|
|
274
|
#
|
|
275
|
# barplot(mean_r,ylim=c(20,26),ylab="RMSE in tenth deg C")
|
|
276
|
# barplot(median_r,ylim=c(20,26),ylab="RMSE in tenth deg C",add=TRUE,inside=FALSE,beside=TRUE) # put both on the same plot
|
|
277
|
# barplot(sd_r,ylim=c(6,8),ylab="RMSE in tenth deg C") # put both on the same plot
|
|
278
|
#
|
|
279
|
# height<-rbind(mean_r,median_r)
|
|
280
|
# barplot(height,ylim=c(20,26),ylab="RMSE in tenth deg C",beside=TRUE,legend=rownames(height))
|
|
281
|
# barplot(height,ylim=c(20,26),ylab="RMSE in tenth deg C",beside=TRUE, col=c("darkblue","red"),legend=rownames(height)) # put both on the same plot
|
|
282
|
# PNG(paste("Barplot_results_RMSE_sampling_",out_prefix,".png", sep=""))
|
|
283
|
#
|
|
284
|
# barplot2(mean_r,median_r,ylim=c(23,26),ylab="RMSE in tenth deg C") # put both on the same plot
|
|
285
|
# #Collect var explained and p values for each var...
|
|
286
|
#
|
|
287
|
# mod<-names(mean_r)
|
|
288
|
# average<-mean_r
|
|
289
|
# average_low<-mean_r-sd_r
|
|
290
|
# average_up<-mean_r+sd_r
|
|
291
|
#
|
|
292
|
# for(i in 1:length(mod)){
|
|
293
|
# #X11(width=14,height=10)
|
|
294
|
# name<-mod[i]
|
|
295
|
# barplot2(average[[name]],plot.ci=TRUE, ci.l=average_low[[name]], ci.u=average_up[[name]],main=paste(" Mean RMSE per month ",name, sep=""), names.arg=c("mod1", "mod2", "mod3", "mod4", "mod5", "mod6","mod7"),ylim=c(20,30),ylab="RMSE in tenth deg C",xlab=name)
|
|
296
|
# #title(paste("Sampling RMSE for mod",i,sep=""))
|
|
297
|
# #savePlot(paste("barplot_results_RMSE_month_",name,out_prefix,".png", sep=""), type="png")
|
|
298
|
# #dev.off()
|
|
299
|
# }
|
|
300
|
|
|
301
|
# End of script##########
|
|
302
|
|
|
303
|
#Selecting dates and files based on names
|
|
304
|
#cor_LST_LC<-matrix(1,10,1)
|
|
305
|
# for(i in 1:length(dates)){
|
|
306
|
# cor_LST_LC1[i]<-cor(ghcn.subsets[[i]]$LST,ghcn.subsets[[i]]$LC1)
|
|
307
|
# }
|
|
308
|
# for(i in 1:length(dates)){
|
|
309
|
# cor_LST_LC3[i]<-cor(ghcn.subsets[[i]]$LST,ghcn.subsets[[i]]$LC3)
|
|
310
|
# }
|
|
311
|
|
