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################## CLIMATE INTERPOLATION FUSION METHOD #######################################
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############################ Merging LST and station data ##########################################
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#This script interpolates tmax values using MODIS LST and GHCND station data #
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#interpolation area. It requires the text file of stations and a shape file of the study area. #
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#Note that the projection for both GHCND and study area is lonlat WGS84. #
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#AUTHOR: Brian McGill #
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#DATE: 07/11/2012 #
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#PROJECT: NCEAS INPLANT: Environment and Organisms --TASK#363-- #
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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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library(fields) # NCAR Spatial Interpolation methods such as kriging, splines
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library(raster) # Hijmans et al. package for raster processing
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library(rasterVis)
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library(parallel) # Urbanek S. and Ripley B., package for multi cores & parralel processing
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### Parameters and argument
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infile1<- "ghcn_or_tmax_covariates_06262012_OR83M.shp" #GHCN shapefile containing variables for modeling 2010
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#tinfile2<-"list_10_dates_04212012.txt" #List of 10 dates for the regression
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#infile2<-"list_2_dates_04212012.txt"
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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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#infile6<-"lst_climatology.txt"
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infile6<-"LST_files_monthly_climatology.txt"
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inlistf<-"list_files_05032012.txt" #Stack of images containing the Covariates
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#path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations"
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path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_07192012_GAM"
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#path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_GAM"
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#path<-"/home/parmentier/Data/IPLANT_project/data_Oregon_stations_07152012" #Jupiter LOCATION on Atlas for kriging"
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#path<-"M:/Data/IPLANT_project/data_Oregon_stations" #Locations on Atlas
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#Station location of the study area
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stat_loc<-read.table(paste(path,"/","location_study_area_OR_0602012.txt",sep=""),sep=",", header=TRUE)
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#GHCN Database for 1980-2010 for study area (OR)
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data3<-read.table(paste(path,"/","ghcn_data_TMAXy1980_2010_OR_0602012.txt",sep=""),sep=",", header=TRUE)
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nmodels<-8 #number of models running
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y_var_name<-"dailyTmax"
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predval<-1
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prop<-0.3 #Proportion of testing retained for validation
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#prop<-0.25
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seed_number<- 100 #Seed number for random sampling
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out_prefix<-"_07242012_365d_GAM_fusion5" #User defined output prefix
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setwd(path)
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bias_val<-0 #if value 1 then training data is used in the bias surface rather than the all monthly stations
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#source("fusion_function_07192012.R")
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source("GAM_fusion_function_07192012d.R")
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############ START OF THE SCRIPT ##################
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#
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#
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### Step 0/Step 6 in Brian's code...preparing year 2010 data for modeling
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#
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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 projection information (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*pi/180)) #Adding a variable to the dataframe
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ghcn <- transform(ghcn,Eastness = sin(ASPECT*pi/180)) #adding variable to the dataframe.
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ghcn <- transform(ghcn,Northness_w = sin(slope*pi/180)*cos(ASPECT*pi/180)) #Adding a variable to the dataframe
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ghcn <- transform(ghcn,Eastness_w = sin(slope*pi/180)*sin(ASPECT*pi/180)) #adding variable to the dataframe.
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#Remove NA for LC and CANHEIGHT
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ghcn$LC1[is.na(ghcn$LC1)]<-0
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ghcn$LC3[is.na(ghcn$LC3)]<-0
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ghcn$CANHEIGHT[is.na(ghcn$CANHEIGHT)]<-0
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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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##Extracting the variables values from the raster files
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lines<-read.table(paste(path,"/",inlistf,sep=""), sep=" ") #Column 1 contains the names of raster files
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inlistvar<-lines[,1]
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inlistvar<-paste(path,"/",as.character(inlistvar),sep="")
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covar_names<-as.character(lines[,2]) #Column two contains short names for covaraites
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s_raster<- stack(inlistvar) #Creating a stack of raster images from the list of variables.
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layerNames(s_raster)<-covar_names #Assigning names to the raster layers
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projection(s_raster)<-CRS
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#stat_val<- extract(s_raster, ghcn3) #Extracting values from the raster stack for every point location in coords data frame.
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pos<-match("ASPECT",layerNames(s_raster)) #Find column with name "value"
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r1<-raster(s_raster,layer=pos) #Select layer from stack
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pos<-match("slope",layerNames(s_raster)) #Find column with name "value"
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r2<-raster(s_raster,layer=pos) #Select layer from stack
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N<-cos(r1*pi/180)
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E<-sin(r1*pi/180)
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Nw<-sin(r2*pi/180)*cos(r1*pi/180) #Adding a variable to the dataframe
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Ew<-sin(r2*pi/180)*sin(r1*pi/180) #Adding variable to the dataframe.
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pos<-match("LC1",layerNames(s_raster)) #Find column with name "value"
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LC1<-raster(s_raster,layer=pos) #Select layer from stack
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s_raster<-dropLayer(s_raster,pos)
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LC1[is.na(LC1)]<-0
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pos<-match("LC3",layerNames(s_raster)) #Find column with name "value"
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LC3<-raster(s_raster,layer=pos) #Select layer from stack
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s_raster<-dropLayer(s_raster,pos)
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LC3[is.na(LC3)]<-0
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xy<-coordinates(r1) #get x and y projected coordinates...
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xy_latlon<-project(xy, CRS, inv=TRUE) # find lat long for projected coordinats (or pixels...)
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lon<-raster(xy_latlon) #Transform a matrix into a raster object ncol=ncol(r1), nrow=nrow(r1))
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ncol(lon)<-ncol(r1)
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nrow(lon)<-nrow(r1)
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extent(lon)<-extent(r1)
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projection(lon)<-CRS #At this stage this is still an empty raster with 536 nrow and 745 ncell
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lat<-lon
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values(lon)<-xy_latlon[,1]
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values(lat)<-xy_latlon[,2]
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r<-stack(N,E,Nw,Ew,lon,lat,LC1,LC3)
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rnames<-c("Northness","Eastness","Northness_w","Eastness_w", "lon","lat","LC1","LC3")
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layerNames(r)<-rnames
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s_raster<-addLayer(s_raster, r)
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#s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame
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####### Preparing LST stack of climatology...
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#l=list.files(pattern="mean_month.*rescaled.rst")
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l <-readLines(paste(path,"/",infile6, sep=""))
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molst<-stack(l) #Creating a raster stack...
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#setwd(old)
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molst<-molst-273.16 #K->C #LST stack of monthly average...
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idx <- seq(as.Date('2010-01-15'), as.Date('2010-12-15'), 'month')
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molst <- setZ(molst, idx)
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layerNames(molst) <- month.abb
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###### Preparing tables for model assessment: specific diagnostic/metrics
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#Model assessment: specific diagnostics/metrics
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results_AIC<- matrix(1,1,nmodels+3)
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results_GCV<- matrix(1,1,nmodels+3)
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results_DEV<- matrix(1,1,nmodels+3)
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#results_RMSE_f<- matrix(1,length(models)+3)
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#Model assessment: general diagnostic/metrics
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results_RMSE <- matrix(1,1,nmodels+4)
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results_MAE <- matrix(1,1,nmodels+4)
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results_ME <- matrix(1,1,nmodels+4) #There are 8+1 models
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results_R2 <- matrix(1,1,nmodels+4) #Coef. of determination for the validation dataset
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results_RMSE_f<- matrix(1,1,nmodels+4) #RMSE fit, RMSE for the training dataset
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results_MAE_f <- matrix(1,1,nmodels+4)
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######## Preparing monthly averages from the ProstGres database
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# do this work outside of (before) this function
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# to avoid making a copy of the data frame inside the function call
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date1<-ISOdate(data3$year,data3$month,data3$day) #Creating a date object from 3 separate column
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date2<-as.POSIXlt(as.Date(date1))
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data3$date<-date2
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d<-subset(data3,year>=2000 & mflag=="0" ) #Selecting dataset 2000-2010 with good quality: 193 stations
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#May need some screeing??? i.e. range of temp and elevation...
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d1<-aggregate(value~station+month, data=d, mean) #Calculate monthly mean for every station in OR
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id<-as.data.frame(unique(d1$station)) #Unique station in OR for year 2000-2010: 193 but 7 loss of monthly avg
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dst<-merge(d1, stat_loc, by.x="station", by.y="STAT_ID") #Inner join all columns are retained
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#This allows to change only one name of the data.frame
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pos<-match("value",names(dst)) #Find column with name "value"
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names(dst)[pos]<-c("TMax")
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dst$TMax<-dst$TMax/10 #TMax is the average max temp for monthy data
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#dstjan=dst[dst$month==9,] #dst contains the monthly averages for tmax for every station over 2000-2010
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######### Preparing daily values for training and testing
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#Screening for bad values: value is tmax in this case
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#ghcn$value<-as.numeric(ghcn$value)
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ghcn_all<-ghcn
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ghcn_test<-subset(ghcn,ghcn$value>-150 & ghcn$value<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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##Sampling: training and testing sites...
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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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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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sampling<-vector("list",length(dates))
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for(i in 1:length(dates)){
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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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sampling[[i]]<-ind.training
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}
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######## Prediction for the range of dates
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#Start loop here...
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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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#i=1
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#mclapply(1:length(dates), runFusion, mc.cores = 8)#This is the end bracket from mclapply(...) statement
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#source("GAM_fusion_function_07192012d.R")
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gam_fus_mod<-mclapply(1:length(dates), runGAMFusion,mc.preschedule=FALSE,mc.cores = 8) #This is the end bracket from mclapply(...) statement
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#fusion_mod357<-mclapply(357:365,runFusion, mc.cores=8)# for debugging
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#test<-runFusion(362) #date 362 has problems with GAM
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#test<-mclapply(357,runFusion, mc.cores=1)# for debugging
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## Plotting and saving diagnostic measures
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accuracy_tab_fun<-function(i,f_list){
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tb<-f_list[[i]][[3]]
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return(tb)
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}
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tb<-gam_fus_mod[[1]][[3]][0,] #empty data frame with metric table structure that can be used in rbinding...
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tb_tmp<-gam_fus_mod #copy
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for (i in 1:length(tb_tmp)){
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tmp<-tb_tmp[[i]][[3]]
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tb<-rbind(tb,tmp)
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}
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rm(tb_tmp)
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for(i in 4:12){ # start of the for loop #1
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tb[,i]<-as.numeric(as.character(tb[,i]))
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}
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tb_RMSE<-subset(tb, metric=="RMSE")
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tb_MAE<-subset(tb,metric=="MAE")
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tb_ME<-subset(tb,metric=="ME")
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tb_R2<-subset(tb,metric=="R2")
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tb_RMSE_f<-subset(tb, metric=="RMSE_f")
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tb_MAE_f<-subset(tb,metric=="MAE_f")
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tb_diagnostic1<-rbind(tb_RMSE,tb_MAE,tb_ME,tb_R2)
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#tb_diagnostic2<-rbind(tb_,tb_MAE,tb_ME,tb_R2)
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mean_RMSE<-sapply(tb_RMSE[,4:12],mean)
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mean_MAE<-sapply(tb_MAE[,4:12],mean)
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#tb<-sapply(fusion_mod,accuracy_tab_fun)
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write.table(tb_diagnostic1, file= paste(path,"/","results2_fusion_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
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write.table(tb, file= paste(path,"/","results2_fusion_Assessment_measure_all",out_prefix,".txt",sep=""), sep=",")
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save(gam_fus_mod,file= paste(path,"/","results2_fusion_Assessment_measure_all",out_prefix,".RData",sep=""))
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#tb<-as.data.frame(tb_diagnostic1)
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#write.table(tb_1, file= paste(path,"/","results2_fusion_Assessment_measure1",out_prefix,".txt",sep=""), sep=",")
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#write.table(tb_diagnostic2, file= paste(path,"/","results_fusion_Assessment_measure2",out_prefix,".txt",sep=""), sep=",")
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#### END OF SCRIPT
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