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################## Interpolation of Tmax for 10 dates. #######################################
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########################### TWO-STAGE REGRESSION ###############################################
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#This script interpolates station values for the Oregon case study using a two-stage regression. #
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#For each input dates, it performs 1) Step 1: General Additive Model (GAM) #
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# 2) Step 2: Kriging on residuals from step 1 #
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# #
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#The script uses LST monthly averages as input variables and loads the station data #
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#from a shape file with projection information. #
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#Note that this program: #
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#1)assumes that the shape file is in the current working. #
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#2)relevant variables were extracted from raster images before performing the regressions #
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# and stored shapefile #
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#This scripts predicts tmax using ing GAM and LST derived from MOD11A1.Interactions terms are #
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#also included and assessed using the RMSE,MAE,ME and R2 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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#AUTHOR: Benoit Parmentier #
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#DATE: 05/31/212 #
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#364-- #
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##################################################################################################
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###Loading R library and packages
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library(gtools) # loading some useful tools
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library(mgcv) # GAM package by Simon Wood
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library(sp) # Spatial pacakge with class definition by Bivand et al.
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library(spdep) # Spatial pacakge with methods and spatial stat. by Bivand et al.
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library(rgdal) # GDAL wrapper for R, spatial utilities
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library(gstat) # Kriging and co-kriging by Pebesma et al.
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###Parameters and arguments
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infile1<- "ghcn_or_tmax_b_04142012_OR83M.shp" #GHCN shapefile containing variables
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infile2<-"list_10_dates_04212012.txt" #List of 10 dates for the regression
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#infile2<-"list_365_dates_04212012.txt"
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infile3<-"LST_dates_var_names.txt" #LST dates name
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infile4<-"models_interpolation_05142012.txt" #Interpolation model names
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infile5<-"mean_day244_rescaled.rst" #Raster or grid for the locations of predictions
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path<-"/data/computer/parmentier/Data/IPLANT_project/data_Oregon_stations" #Jupiter LOCATION on EOS
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#path<-"H:/Data/IPLANT_project/data_Oregon_stations" #Jupiter Location on XANDERS
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setwd(path)
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prop<-0.3 #Proportion of testing retained for validation
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seed_number<- 100 #Seed number for random sampling
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out_prefix<-"_05312012_2d_Kr_LST" #User defined output prefix
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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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CRS<-proj4string(ghcn) #Storing projectionminformation (ellipsoid, datum,etc.)
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mean_LST<- readGDAL(infile5) #Reading the whole raster in memory. This provides a grid for kriging
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proj4string(mean_LST)<-CRS #Assigning coordinate information to prediction grid.
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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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set.seed(seed_number) #Using a seed number allow results based on random number to be compared...
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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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#Model assessment: specific diagnostic/metrics for GAM
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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_f<- matrix(1,length(dates),length(models)+3)
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#Model assessment: general diagnostic/metrics
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results_RMSE <- matrix(1,length(dates),length(models)+3)
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results_RMSE_kr <- matrix(1,length(dates),length(models)+3)
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results_MAE <- matrix(1,length(dates),length(models)+3)
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results_MAE_kr <- matrix(1,length(dates),length(models)+3)
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results_ME <- matrix(1,length(dates),length(models)+3)
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results_ME_kr <- matrix(1,length(dates),length(models)+3) #ME corresponds to the bias
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results_R2 <- matrix(1,length(dates),length(models)+3) #Coef. of determination for the validation dataset
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results_R2_kr <- matrix(1,length(dates),length(models)+3)
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results_RMSE_f<- matrix(1,length(dates),length(models)+3) #RMSE fit, RMSE for the training dataset
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results_RMSE_f_kr<- matrix(1,length(dates),length(models)+3)
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#Tracking relationship between LST AND LC
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cor_LST_LC1<-matrix(1,10,1) #correlation LST-LC1
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cor_LST_LC3<-matrix(1,10,1) #correlation LST-LC3
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cor_LST_tmax<-matrix(1,10,1) #correlation LST-tmax
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#Screening for bad values
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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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#coords<- ghcn[,c('x_OR83M','y_OR83M')]
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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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ghcn.subsets <-lapply(dates, function(d) subset(ghcn, date==d)) #this creates a list of 10 or 365 subsets dataset based on dates
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## looping through the dates...this is the main part of the code
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#i=1 #for debugging
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#j=1 #for debugging
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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") # interpolation date being processed
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month<-strftime(date, "%m") # current month of the date being processed
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LST_month<-paste("mm_",month,sep="") # name of LST month to be matched
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###Regression part 1: Creating a validation dataset by creating training and testing datasets
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mod_LST <-ghcn.subsets[[i]][,match(LST_month, names(ghcn.subsets[[i]]))] #Match interpolation date and monthly LST average
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ghcn.subsets[[i]] = transform(ghcn.subsets[[i]],LST = mod_LST) #Add the variable LST to the subset dataset
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n<-nrow(ghcn.subsets[[i]])
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ns<-n-round(n*prop) #Create a sample from the data frame with 70% of the rows
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nv<-n-ns #create a sample for validation with prop of the rows
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ind.training <- sample(nrow(ghcn.subsets[[i]]), size=ns, replace=FALSE) #This selects the index position for 70% of the rows taken randomly
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ind.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
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####Regression part 2: GAM models (REGRESSION STEP1)
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#Model can be changed without affecting the script
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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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#listmod can be created and looped over. In this case we loop around the GAM objects in memory...
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for (j in 1:length(models)){
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##Model assessment: specific diagnostic/metrics for GAM
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name<-paste("mod",j,sep="") #modj is the name of The "j" model (mod1 if j=1)
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mod<-get(name) #accessing GAM model ojbect "j"
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results_AIC[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_AIC[i,2]<- ns #number of stations used in the training stage
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results_AIC[i,3]<- "AIC"
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results_AIC[i,j+3]<- AIC (mod)
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results_GCV[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_GCV[i,2]<- ns #number of stations used in the training
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results_GCV[i,3]<- "GCV"
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results_GCV[i,j+3]<- mod$gcv.ubre
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results_DEV[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_DEV[i,2]<- ns #number of stations used in the training stage
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results_DEV[i,3]<- "DEV"
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results_DEV[i,j+3]<- mod$deviance
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results_RMSE_f[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_RMSE_f[i,2]<- ns #number of stations used in the training stage
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results_RMSE_f[i,3]<- "RSME"
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results_RMSE_f[i,j+3]<- sqrt(sum((mod$residuals)^2)/nv)
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##Model assessment: general diagnostic/metrics
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##validation: using the testing data
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#Automate this using a data frame of size??
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y_mod<- predict(mod, newdata=data_v, se.fit = TRUE) #Using the coeff to predict new values.
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res_mod<- data_v$tmax - y_mod$fit #Residuals for the model
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RMSE_mod <- sqrt(sum(res_mod^2)/nv) #RMSE FOR REGRESSION STEP 1: GAM
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MAE_mod<- sum(abs(res_mod))/nv #MAE, Mean abs. Error FOR REGRESSION STEP 1: GAM
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ME_mod<- sum(res_mod)/nv #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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R2_mod<- cor(data_v$tmax,y_mod$fit)^2 #R2, coef. of var FOR REGRESSION STEP 1: GAM
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results_RMSE[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_RMSE[i,2]<- ns #number of stations used in the training stage
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results_RMSE[i,3]<- "RMSE"
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results_RMSE[i,j+3]<- RMSE_mod #Storing RMSE for the model j
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results_MAE[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_MAE[i,2]<- ns #number of stations used in the training stage
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results_MAE[i,3]<- "MAE"
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results_MAE[i,j+3]<- MAE_mod #Storing MAE for the model j
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results_ME[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_ME[i,2]<- ns #number of stations used in the training stage
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results_ME[i,3]<- "ME"
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results_ME[i,j+3]<- ME_mod #Storing ME for the model j
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results_R2[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_R2[i,2]<- ns #number of stations used in the training stage
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results_R2[i,3]<- "R2"
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results_R2[i,j+3]<- R2_mod #Storing R2 for the model j
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#Saving residuals and prediction in the dataframes: tmax predicted from GAM
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pred<-paste("pred_mod",j,sep="")
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data_v[[pred]]<-as.numeric(y_mod$fit)
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data_s[[pred]]<-as.numeric(mod$fit) #Storing model fit values (predicted on training sample)
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name2<-paste("res_mod",j,sep="")
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data_v[[name2]]<-as.numeric(res_mod)
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data_s[[name2]]<-as.numeric(mod$residuals)
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#end of loop calculating RMSE
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}
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###BEFORE Kringing the data object must be transformed to SDF
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coords<- data_v[,c('x_OR83M','y_OR83M')]
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coordinates(data_v)<-coords
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proj4string(data_v)<-CRS #Need to assign coordinates...
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coords<- data_s[,c('x_OR83M','y_OR83M')]
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coordinates(data_s)<-coords
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proj4string(data_s)<-CRS #Need to assign coordinates..
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#KRIGING ON GAM RESIDUALS: REGRESSION STEP2
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for (j in 1:length(models)){
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name<-paste("res_mod",j,sep="")
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data_s$residuals<-data_s[[name]]
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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) # This plot may be saved at a later stage...
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dev.off()
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v.fit<-fit.variogram(v,vgm(1,"Sph", 150000,1))
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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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res_krig1_s <- overlay(res_krige,data_s) #This overlays the kriged surface tmax and the location of weather stations
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res_krig1_v <- overlay(res_krige,data_v) #This overlays the kriged surface tmax and the location of weather stations
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name2<-paste("pred_kr_mod",j,sep="")
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#Adding the results back into the original dataframes.
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data_s[[name2]]<-res_krig1_s$var1.pred
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data_v[[name2]]<-res_krig1_v$var1.pred
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#NEED TO ADD IT BACK TO THE PREDICTION FROM GAM
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gam_kr<-paste("pred_gam_kr",j,sep="")
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pred_gam<-paste("pred_mod",j,sep="")
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data_s[[gam_kr]]<-data_s[[pred_gam]]+ data_s[[name2]]
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data_v[[gam_kr]]<-data_v[[pred_gam]]+ data_v[[name2]]
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#Model assessment: RMSE and then krig the residuals....!
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res_mod_kr_s<- data_s$tmax - data_s[[gam_kr]] #Residuals from kriging training
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res_mod_kr_v<- data_v$tmax - data_v[[gam_kr]] #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$tmax,data_s[[gam_kr]],use="complete.obs")^2 #R2, coef. of determination FOR REGRESSION STEP 1: GAM
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R2_mod_kr_v<- cor(data_v$tmax,data_v[[gam_kr]],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_kr[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_RMSE_kr[i,2]<- ns #number of stations used in the training stage
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results_RMSE_kr[i,3]<- "RMSE"
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results_RMSE_kr[i,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_kr[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_MAE_kr[i,2]<- ns #number of stations used in the training stage
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results_MAE_kr[i,3]<- "MAE"
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results_MAE_kr[i,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_kr[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_ME_kr[i,2]<- ns #number of stations used in the training stage
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results_ME_kr[i,3]<- "ME"
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results_ME_kr[i,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_kr[i,1]<- dates[i] #storing the interpolation dates in the first column
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results_R2_kr[i,2]<- ns #number of stations used in the training stage
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results_R2_kr[i,3]<- "R2"
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results_R2_kr[i,j+3]<- R2_mod_kr_v
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#results_RMSE_kr[i,3]<- res_mod_kr_v
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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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}
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###SAVING THE DATA FRAME IN SHAPEFILES AND TEXTFILES
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data_name<-paste("ghcn_v_",out_prefix,"_",dates[[i]],sep="")
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assign(data_name,data_v)
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#write.table(data_v, file= paste(path,"/",data_name,".txt",sep=""), sep=" ")
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#write out a new shapefile (including .prj component)
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#outfile<-sub(".shp","",data_name) #Removing extension if it is present
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#writeOGR(data_v,".", outfile, driver ="ESRI Shapefile")
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data_name<-paste("ghcn_s_",out_prefix,"_",dates[[i]],sep="")
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assign(data_name,data_s)
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#write.table(data_s, file= paste(path,"/",data_name,".txt",sep=""), sep=" ")
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#outfile<-sub(".shp","",data_name) #Removing extension if it is present
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#writeOGR(data_s,".", outfile, driver ="ESRI Shapefile")
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# end of the for loop1
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}
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## Plotting and saving diagnostic measures
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#Specific diagnostic measures related to the testing datasets
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results_table_AIC<-as.data.frame(results_AIC)
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results_table_GCV<-as.data.frame(results_GCV)
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results_table_DEV<-as.data.frame(results_DEV)
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results_table_RMSE_f<-as.data.frame(results_RMSE_f)
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results_table_RMSE<-as.data.frame(results_RMSE)
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results_table_MAE<-as.data.frame(results_MAE)
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results_table_ME<-as.data.frame(results_ME)
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results_table_R2<-as.data.frame(results_R2)
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320
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cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8")
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colnames(results_table_RMSE)<-cname
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colnames(results_table_MAE)<-cname
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colnames(results_table_ME)<-cname
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colnames(results_table_R2)<-cname
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#Specific diagnostic measures
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colnames(results_table_AIC)<-cname
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colnames(results_table_GCV)<-cname
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328
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colnames(results_table_DEV)<-cname
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colnames(results_table_RMSE_f)<-cname
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330
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331
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#General diagnostic measures
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333
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results_table_RMSE_kr<-as.data.frame(results_RMSE_kr)
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results_table_MAE_kr<-as.data.frame(results_MAE_kr)
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335
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results_table_ME_kr<-as.data.frame(results_ME_kr)
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results_table_R2_kr<-as.data.frame(results_R2_kr)
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337
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338
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cname<-c("dates","ns","metric","mod1k", "mod2k","mod3k", "mod4k", "mod5k", "mod6k", "mod7k","mod8k")
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339
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colnames(results_table_RMSE_kr)<-cname
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colnames(results_table_MAE_kr)<-cname
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colnames(results_table_ME_kr)<-cname
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colnames(results_table_R2_kr)<-cname
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343
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#Summary of diagnostic measures are stored in a data frame
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345
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tb_diagnostic1<-rbind(results_table_RMSE,results_table_MAE, results_table_ME, results_table_R2) #
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346
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tb_diagnostic1_kr<-rbind(results_table_RMSE_kr,results_table_MAE_kr, results_table_ME_kr, results_table_R2_kr)
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347
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tb_diagnostic2<-rbind(results_table_AIC,results_table_GCV, results_table_DEV,results_table_RMSE_f)
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348
|
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349
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write.table(tb_diagnostic1, file= paste(path,"/","results_GAM_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
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350
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write.table(tb_diagnostic1_kr, file= paste(path,"/","results_GAM_Assessment_measure1_kr_",out_prefix,".txt",sep=""), sep=",")
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351
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write.table(tb_diagnostic2, file= paste(path,"/","results_GAM_Assessment_measure2_",out_prefix,".txt",sep=""), sep=",")
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352
|
|
353
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# ##Visualization of results##
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354
|
#
|
355
|
# for(i in 1:length(dates)){
|
356
|
# X11()
|
357
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# RMSE_kr<-results_table_RMSE_kr[i,]
|
358
|
# RMSE_ga<-results_table_RMSE[i,]
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359
|
#
|
360
|
# RMSE_kr<-RMSE_kr[,1:length(models)+2]
|
361
|
# RMSE_ga<-RMSE_ga[,1:length(models)+2]
|
362
|
# colnames(RMSE_kr)<-names(RMSE_ga)
|
363
|
# height<-rbind(RMSE_ga,RMSE_kr)
|
364
|
# rownames(height)<-c("GAM","GAM_KR")
|
365
|
# height<-as.matrix(height)
|
366
|
# barplot(height,ylim=c(14,36),ylab="RMSE in tenth deg C",beside=TRUE,
|
367
|
# legend.text=rownames(height),
|
368
|
# args.legend=list(x="topright"),
|
369
|
# main=paste("RMSE for date ",dates[i], sep=""))
|
370
|
# savePlot(paste("Barplot_results_RMSE_GAM_KR_",dates[i],out_prefix,".png", sep=""), type="png")
|
371
|
# dev.off()
|
372
|
# }
|
373
|
#
|
374
|
# r1<-(results_table_RMSE[,3:10]) #selecting only the columns related to models and method 1
|
375
|
# r2<-(results_table_RMSE_kr[,3:10]) #selecting only the columns related to models and method 1
|
376
|
# mean_r1<-mean(r1)
|
377
|
# mean_r2<-mean(r2)
|
378
|
# median_r1<-sapply(r1, median) #Calulcating the mean for every model (median of columns)
|
379
|
# median_r2<-sapply(r2, median)
|
380
|
# sd_r1<-sapply(r1, sd)
|
381
|
# sd_r2<-sapply(r2, sd)
|
382
|
#
|
383
|
# barplot(mean_r1,ylim=c(23,26),ylab="RMSE in tenth deg C")
|
384
|
# barplot(mean_r2,ylim=c(23,26),ylab="RMSE in tenth deg C")
|
385
|
# barplot(median_r1,ylim=c(23,26),ylab="RMSE in tenth deg C",add=TRUE,inside=FALSE,beside=TRUE) # put both on the same plot
|
386
|
# barplot(median_r2,ylim=c(23,26),ylab="RMSE in tenth deg C",add=TRUE,inside=FALSE,beside=TRUE) # put both on the same plot
|
387
|
#
|
388
|
# barplot(sd_r1,ylim=c(6,8),ylab="RMSE in tenth deg C") # put both on the same plot
|
389
|
# barplot(sd_r2,ylim=c(6,8),ylab="RMSE in tenth deg C") # put both on the same plot
|
390
|
#
|
391
|
# height<-rbind(mean_r1,mean_r2)
|
392
|
# barplot(height,ylim=c(20,26),ylab="RMSE in tenth deg C",beside=TRUE,legend=rownames(height))
|
393
|
# 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
|
394
|
#
|
395
|
# height<-rbind(median_r1,median_r2)
|
396
|
# barplot(height,ylim=c(20,26),ylab="RMSE in tenth deg C",beside=TRUE,legend=rownames(height))
|
397
|
# 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
|
398
|
#
|
399
|
# height<-rbind(mean_r2,median_r2)
|
400
|
# barplot(height,ylim=c(20,26),ylab="RMSE in tenth deg C",beside=TRUE,legend=rownames(height))
|
401
|
# 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
|
402
|
|
403
|
#barplot2(mean_r,median_r,ylim=c(23,26),ylab="RMSE in tenth deg C") # put both on the same plot
|
404
|
#Collect var explained and p values for each var...
|
405
|
|
406
|
|
407
|
##### END OF SCRIPT ##########
|
408
|
|
409
|
|
410
|
|