1
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runGWR <- function(i) { # loop over dates
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2
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3
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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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#i=1
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date_proc<-dates[i]
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date_proc<-strptime(dates[i], "%Y%m%d") # interpolation date being processed
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mo<-as.integer(strftime(date_proc, "%m")) # current month of the date being processed
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day<-as.integer(strftime(date_proc, "%d"))
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year<-as.integer(strftime(date_proc, "%Y"))
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14
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#Adding layer LST to the raster stack and preparing terms
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pos<-match(LST_month,layerNames(s_raster)) #Find column with the current month for instance mm12
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r1<-raster(s_raster,layer=pos) #Select layer from stack
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layerNames(r1)<-"LST"
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s_raster_f<-s_raster #make a local copy for debugging
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s_raster_f<-addLayer(s_raster_f,r1) #Adding current month
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#s_sgdf<-as(s_raster,"SpatialGridDataFrame") #Conversion to spatial grid data frame
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23
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nel<-12
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#tab_range<-data.frame(varname=character(nel),varterm=character(nel),vmin=numeric(nel),vmax=numeric(nel))
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val_range<-vector("list",nel) #list of one row data.frame
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val_rst<-vector("list",nel) #list of one row data.frame
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val_range[[1]]<-data.frame(varname="lon",varterm="lon",vmin=-180,vmax=180)
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val_range[[2]]<-data.frame(varname="lat",varterm="lat",vmin=-90,vmax=90)
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val_range[[3]]<-data.frame(varname="ELEV_SRTM",varterm="ELEV_SRTM",vmin=0,vmax=6000)
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val_range[[4]]<-data.frame(varname="Eastness",varterm="Eastness",vmin=-1,vmax=1)
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val_range[[5]]<-data.frame(varname="Northness",varterm="Northness",vmin=-1,vmax=1)
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val_range[[6]]<-data.frame(varname="Northness_w",varterm="Northness_w",vmin=-1,vmax=1)
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val_range[[7]]<-data.frame(varname="Eastness_w",varterm="Eastness_w",vmin=-1,vmax=1)
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val_range[[8]]<-data.frame(varname="mm_01",varterm="LST",vmin=-258.16,vmax=313.16)
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val_range[[9]]<-data.frame(varname="DISTOC",varterm="DISTOC",vmin=-0,vmax=10000000)
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val_range[[10]]<-data.frame(varname="LC1",varterm="LC1",vmin=0,vmax=100)
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val_range[[11]]<-data.frame(varname="LC3",varterm="LC3",vmin=0,vmax=100)
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40
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val_range[[12]]<-data.frame(varname="slope",varterm="slope",vmin=0,vmax=90)
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41
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tab_range<-do.call(rbind,val_range)
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42
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43
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#pos<-match("ELEV_SRTM",layerNames(s_raster)) #Find column with the current month for instance mm12
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#ELEV_SRTM<-raster(s_raster,pos)
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45
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46
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for (k in 1:length(val_range)){
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47
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avl<-c(-Inf,tab_range$vmin[k],NA, tab_range$vmax[k],+Inf,NA) #This creates a input vector...val 1 are -9999, 2 neg, 3 positive
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48
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rclmat<-matrix(avl,ncol=3,byrow=TRUE)
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49
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s_raster_r<-raster(s_raster_f,match(tab_range$varterm[k],layerNames(s_raster_f)))
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50
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s_raster_r<-reclass(s_raster_r,rclmat) #Loss of layer names when using reclass
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51
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layerNames(s_raster_r)<-tab_range$varterm[k]
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52
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val_rst[[k]]<-s_raster_r
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53
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}
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54
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s_rst_m<-stack(val_rst) #This a raster stack with valid range of values
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55
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56
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###Regression part 1: Creating a validation dataset by creating training and testing datasets
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57
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58
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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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59
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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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60
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#n<-nrow(ghcn.subsets[[i]])
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61
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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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64
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ind.training<-sampling[[i]]
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65
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ind.testing <- setdiff(1:nrow(ghcn.subsets[[i]]), ind.training)
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66
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data_s <- ghcn.subsets[[i]][ind.training, ] #Training dataset currently used in the modeling
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67
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data_v <- ghcn.subsets[[i]][ind.testing, ] #Testing/validation dataset using input sampling
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68
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69
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ns<-nrow(data_s)
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70
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nv<-nrow(data_v)
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71
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72
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###BEFORE model prediction the data object must be transformed to SDF
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73
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74
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coords<- data_v[,c('x_OR83M','y_OR83M')]
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75
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coordinates(data_v)<-coords
|
76
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proj4string(data_v)<-CRS #Need to assign coordinates...
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77
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coords<- data_s[,c('x_OR83M','y_OR83M')]
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78
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coordinates(data_s)<-coords
|
79
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proj4string(data_s)<-CRS #Need to assign coordinates..
|
80
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81
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ns<-nrow(data_s) #This is added to because some loss of data might have happened because of the averaging...
|
82
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nv<-nrow(data_v)
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83
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|
84
|
### PREDICTION/ Interpolation
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85
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86
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pos<-match("value",names(data_s)) #Find column with name "value"
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87
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names(data_s)[pos]<-y_var_name
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88
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pos<-match("value",names(data_v)) #Find column with name "value"
|
89
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names(data_v)[pos]<-y_var_name
|
90
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|
91
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#if y_var_name=="dailyTmax"
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92
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data_v$y_var<-data_v[[y_var_name]]/10 #Note that values are divided by 10 because the var is temp
|
93
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data_s$y_var<-data_s[[y_var_name]]/10
|
94
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|
95
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#Model and response variable can be changed without affecting the script
|
96
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|
97
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formula1 <- as.formula("y_var~ lat + lon + ELEV_SRTM", env=.GlobalEnv)
|
98
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formula2 <- as.formula("y_var~ I(lat*lon) + ELEV_SRTM", env=.GlobalEnv)
|
99
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formula3 <- as.formula("y_var~ lat + lon + ELEV_SRTM + Northness + Eastness + DISTOC", env=.GlobalEnv)
|
100
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formula4 <- as.formula("y_var~ I(lat*lon) + ELEV_SRTM + I(Northness*Eastness) + DISTOC + LST", env=.GlobalEnv)
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101
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formula5 <- as.formula("y_var~ lat + lon + ELEV_SRTM + Northness_w + Eastness_w + DISTOC + LST", env=.GlobalEnv)
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102
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formula6 <- as.formula("y_var~ lat + lon + ELEV_SRTM + Northness_w + Eastness_w + DISTOC + LST + LC1", env=.GlobalEnv)
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103
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formula7 <- as.formula("y_var~ lat + lon + ELEV_SRTM + Northness_w + Eastness_w + DISTOC + LST + LC3", env=.GlobalEnv)
|
104
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formula8 <- as.formula("y_var~ lat + lon + ELEV_SRTM + Northness_w + Eastness_w + DISTOC + LST + I(LST*LC1)", env=.GlobalEnv)
|
105
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|
106
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# bwG <- gwr.sel(tmax~ lon + lat + ELEV_SRTM + Eastness + Northness + DISTOC,data=data_s,gweight=gwr.Gauss, verbose = FALSE)
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107
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# gwrG<- gwr(tmax~ lon + lat + ELEV_SRTM + Eastness + Northness + DISTOC, data=data_s, bandwidth=bwG, gweight=gwr.Gauss, hatmatrix=TRUE)
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108
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|
109
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bwGm1 <-try(gwr.sel(formula1,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
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110
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bwGm2 <-try(gwr.sel(formula2,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
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111
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bwGm3 <-try(gwr.sel(formula3,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
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112
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bwGm4 <-try(gwr.sel(formula4,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
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113
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bwGm5 <-try(gwr.sel(formula5,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
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114
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bwGm6 <-try(gwr.sel(formula6,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
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115
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bwGm7 <-try(gwr.sel(formula7,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
|
116
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bwGm8 <-try(gwr.sel(formula8,data=data_s,gweight=gwr.Gauss, verbose = FALSE))
|
117
|
|
118
|
mod1<- try(gwr(formula1, data=data_s, bandwidth=bwGm1, gweight=gwr.Gauss, hatmatrix=TRUE))
|
119
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mod2<- try(gwr(formula2, data=data_s, bandwidth=bwGm2, gweight=gwr.Gauss, hatmatrix=TRUE))
|
120
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mod3<- try(gwr(formula3, data=data_s, bandwidth=bwGm3, gweight=gwr.Gauss, hatmatrix=TRUE))
|
121
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mod4<- try(gwr(formula4, data=data_s, bandwidth=bwGm4, gweight=gwr.Gauss, hatmatrix=TRUE))
|
122
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mod5<- try(gwr(formula5, data=data_s, bandwidth=bwGm5, gweight=gwr.Gauss, hatmatrix=TRUE))
|
123
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mod6<- try(gwr(formula6, data=data_s, bandwidth=bwGm6, gweight=gwr.Gauss, hatmatrix=TRUE))
|
124
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mod7<- try(gwr(formula7, data=data_s, bandwidth=bwGm7, gweight=gwr.Gauss, hatmatrix=TRUE))
|
125
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mod8<- try(gwr(formula8, data=data_s, bandwidth=bwGm8, gweight=gwr.Gauss, hatmatrix=TRUE))
|
126
|
|
127
|
#tmax_predicted=themolst+daily_delta_rast-bias_rast #Final surface?? but daily_rst
|
128
|
#pred1 <- gwr(formula1, data_s, bandwidth=bwGm1, fit.points =s_sgdf_m,predict=TRUE, se.fit=TRUE,fittedGWRobject=mod1)
|
129
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#pred2 <- gwr(formula1, data_s, bandwidth=bwGm2, fit.points =s_sgdf_m,predict=TRUE, se.fit=TRUE,fittedGWRobject=mod1)
|
130
|
|
131
|
pred_gwr<-vector("list",nmodels) #This will contain the nine gwr predictions...
|
132
|
mod_obj<-vector("list",nmodels) #This will contain the nine gwr fitting
|
133
|
|
134
|
for (j in 1:nmodels){
|
135
|
|
136
|
##Model assessment: specific diagnostic/metrics for GAM
|
137
|
|
138
|
name<-paste("mod",j,sep="") #modj is the name of The "j" model (mod1 if j=1)
|
139
|
mod<-get(name) #accessing GAM model ojbect "j"
|
140
|
|
141
|
mod_obj[[j]]<-mod #Storing the current mod in a list
|
142
|
formula_name<-paste("formula",j,sep="")
|
143
|
formula<-get(formula_name)
|
144
|
bwGm_name<-paste("bwGm",j,sep="")
|
145
|
bwGm<-get(bwGm_name)
|
146
|
t<-terms(formula) #extract information from formula
|
147
|
t_l<-labels(t) #extract labels from formula: this is a string (character), this can be used directly to find the variables in the stack!!
|
148
|
|
149
|
#inter<-grepl(pattern="I", t_l,ignore.case=FALSE)
|
150
|
inter<-grepl(pattern="I\\(", t_l,ignore.case=FALSE)
|
151
|
if (sum(inter)>0){
|
152
|
interaction<-vector("list",length(sum(inter)))
|
153
|
ks<-0
|
154
|
for(k in 1:length(inter)){
|
155
|
if (inter[k]==TRUE){
|
156
|
ks<-ks+1
|
157
|
postion<-regexpr("\\(.*\\)$",t_l[k])
|
158
|
x<-substring(t_l[k],first=postion+1,last=postion+attr(postion,"match.length")-2) #extract characters between brackets
|
159
|
t_inter <-as.character(unlist(strsplit(x, "[*]"))) #strsplit creates a list!!
|
160
|
interaction[[ks]]<-trim(t_inter)
|
161
|
}
|
162
|
}
|
163
|
mod_varn<-c(unlist(interaction),t_l[inter==FALSE]) #"trim" white space in string character
|
164
|
}
|
165
|
|
166
|
if (sum(inter)==0){
|
167
|
#interaction<-vector("list",length(sum(inter)))
|
168
|
mod_varn<-t_l
|
169
|
}
|
170
|
#browser()
|
171
|
mod_varn <-unique(mod_varn)
|
172
|
list_rst<-vector("list",length(mod_varn))
|
173
|
pos<-match(mod_varn,layerNames(s_rst_m)) #Find column with the current month for instance mm12
|
174
|
s_rst_mod<-subset(s_rst_m,pos)
|
175
|
|
176
|
#NOW CREATE THE MASK?? OR REMOVE NA AT THE data.frame stage...
|
177
|
### Model assessment
|
178
|
s_sgdf<-as(s_rst_mod,"SpatialGridDataFrame")
|
179
|
s_spdf<-as.data.frame(s_sgdf) #Note that this automatically removes all NA rows
|
180
|
s_spdf<-na.omit(s_spdf) #removes all rows that have na...
|
181
|
coords<- s_spdf[,c('s1','s2')]
|
182
|
coordinates(s_spdf)<-coords
|
183
|
proj4string(s_spdf)<-CRS #Need to assign coordinates...
|
184
|
|
185
|
#If mod "j" is not a model object
|
186
|
if (inherits(mod,"try-error")) {
|
187
|
|
188
|
results_m1[1,1]<- dates[i] #storing the interpolation dates in the first column
|
189
|
results_m1[1,2]<- ns #number of stations used in the training stage
|
190
|
results_m1[1,3]<- "SSERR"
|
191
|
results_m1[1,j+3]<- NA
|
192
|
|
193
|
results_m2[1,1]<- dates[i] #storing the interpolation dates in the first column
|
194
|
results_m2[1,2]<- ns #number of stations used in the training
|
195
|
results_m2[1,3]<- "GCV"
|
196
|
results_m2[1,j+3]<- NA
|
197
|
|
198
|
results_m3[1,1]<- dates[i] #storing the interpolation dates in the first column
|
199
|
results_m3[1,2]<- ns #number of stations used in the training stage
|
200
|
results_m3[1,3]<- "DEV"
|
201
|
results_m3[1,j+3]<- NA
|
202
|
|
203
|
results_RMSE_f[1,1]<- dates[i] #storing the interpolation dates in the first column
|
204
|
results_RMSE_f[1,2]<- ns #number of stations used in the training stage
|
205
|
results_RMSE_f[1,3]<- "RSME_f"
|
206
|
results_RMSE_f[1,j+3]<- NA
|
207
|
|
208
|
results_MAE_f[1,1]<- dates[i] #storing the interpolation dates in the first column
|
209
|
results_MAE_f[1,2]<- ns #number of stations used in the training stage
|
210
|
results_MAE_f[1,3]<- "MAE_f"
|
211
|
results_MAE_f[1,j+3]<-NA
|
212
|
|
213
|
results_RMSE[1,1]<- dates[i] #storing the interpolation dates in the first column
|
214
|
results_RMSE[1,2]<- ns #number of stations used in the training stage
|
215
|
results_RMSE[1,3]<- "RMSE"
|
216
|
results_RMSE[1,j+3]<- NA #Storing RMSE for the model j
|
217
|
results_MAE[1,1]<- dates[i] #storing the interpolation dates in the first column
|
218
|
results_MAE[1,2]<- ns #number of stations used in the training stage
|
219
|
results_MAE[1,3]<- "MAE"
|
220
|
results_MAE[1,j+3]<- NA #Storing MAE for the model j
|
221
|
results_ME[1,1]<- dates[i] #storing the interpolation dates in the first column
|
222
|
results_ME[1,2]<- ns #number of stations used in the training stage
|
223
|
results_ME[1,3]<- "ME"
|
224
|
results_ME[1,j+3]<- NA #Storing ME for the model j
|
225
|
results_R2[1,1]<- dates[i] #storing the interpolation dates in the first column
|
226
|
results_R2[1,2]<- ns #number of stations used in the training stage
|
227
|
results_R2[1,3]<- "R2"
|
228
|
results_R2[1,j+3]<- NA #Storing R2 for the model j
|
229
|
|
230
|
}
|
231
|
|
232
|
#If mod is a modelobject
|
233
|
|
234
|
#If mod "j" is not a model object
|
235
|
if (inherits(mod,"gwr")) {
|
236
|
|
237
|
pred <- gwr(formula, data_s, bandwidth=bwGm, fit.points =s_spdf,predict=TRUE, se.fit=TRUE,fittedGWRobject=mod)
|
238
|
#pred <- try(gwr(formula, data_s, bandwidth=bwGm, fit.points =s_spdf,predict=TRUE, se.fit=TRUE,fittedGWRobject=mod))
|
239
|
|
240
|
pred_gwr[[j]]<-pred #prediction stored in a list
|
241
|
|
242
|
raster_pred<-rasterize(pred$SDF,r1,"pred",fun=mean)
|
243
|
|
244
|
layerNames(raster_pred)<-"y_pred"
|
245
|
|
246
|
data_name<-paste("predicted_mod",j,"_",dates[[i]],sep="")
|
247
|
raster_name<-paste("GWR_",data_name,out_prefix,".rst", sep="")
|
248
|
writeRaster(raster_pred, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
|
249
|
#writeRaster(r2, filename=raster_name,overwrite=TRUE) #Writing the data in a raster file format...(IDRISI)
|
250
|
|
251
|
#Save png plot here...
|
252
|
data_name<-paste("predicted_mod",j,"_",dates[[i]],sep="")
|
253
|
png_name<-paste("GWR_plot_",data_name,out_prefix,".png", sep="")
|
254
|
png(png_name) #Create file to write a plot
|
255
|
#datelabel2=format(ISOdate(year,mo,day),"%B ") #Plot label
|
256
|
plot(raster_pred) #Plot to file the autokrige object
|
257
|
#savePlot(paste("Bias_surface_LST_TMax_",dates[i],out_prefix,".png", sep=""), type="png")
|
258
|
dev.off() #Release the hold to the file
|
259
|
|
260
|
|
261
|
#rpred<-as #Extracting the SptialGriDataFrame from the autokrige object
|
262
|
|
263
|
#rpred<- predict(mod, newdata=s_sgdf, se.fit = TRUE) #Using the coeff to predict new values.
|
264
|
#y_pred<-rpred$var1.pred #is the order the same?
|
265
|
#y_prederr<-rpred$var1.var
|
266
|
|
267
|
pred_sgdf<-as(raster_pred,"SpatialGridDataFrame" ) #Conversion to spatial grid data frame
|
268
|
#rpred_val_s <- overlay(raster_pred,data_s) #This overlays the kriged surface tmax and the location of weather stations
|
269
|
|
270
|
rpred_val_s <- overlay(pred_sgdf,data_s) #This overlays the kriged surface tmax and the location of weather stations
|
271
|
rpred_val_v <- overlay(pred_sgdf,data_v) #This overlays the kriged surface tmax and the location of weather stations
|
272
|
|
273
|
pred_mod<-paste("pred_mod",j,sep="")
|
274
|
#Adding the results back into the original dataframes.
|
275
|
data_s[[pred_mod]]<-rpred_val_s$y_pred
|
276
|
data_v[[pred_mod]]<-rpred_val_v$y_pred
|
277
|
|
278
|
#Model assessment: RMSE and then krig the residuals....!
|
279
|
|
280
|
res_mod_s<- data_s$y_var - data_s[[pred_mod]] #Residuals from kriging training
|
281
|
res_mod_v<- data_v$y_var - data_v[[pred_mod]] #Residuals from kriging validation
|
282
|
|
283
|
####ADDED ON JULY 20th
|
284
|
res_mod<-res_mod_v
|
285
|
|
286
|
#RMSE_mod <- sqrt(sum(res_mod^2)/nv) #RMSE FOR REGRESSION STEP 1: GAM
|
287
|
RMSE_mod<- sqrt(mean(res_mod^2,na.rm=TRUE))
|
288
|
#MAE_mod<- sum(abs(res_mod),na.rm=TRUE)/(nv-sum(is.na(res_mod))) #MAE from kriged surface validation
|
289
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MAE_mod<- mean(abs(res_mod), na.rm=TRUE)
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290
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#ME_mod<- sum(res_mod,na.rm=TRUE)/(nv-sum(is.na(res_mod))) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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291
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ME_mod<- mean(res_mod,na.rm=TRUE) #ME, Mean Error or bias FOR REGRESSION STEP 1: GAM
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292
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#R2_mod<- cor(data_v$y_var,data_v[[pred_mod]])^2 #R2, coef. of var FOR REGRESSION STEP 1: GAM
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293
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R2_mod<- cor(data_v$y_var,data_v[[pred_mod]], use="complete")^2
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294
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295
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R2_mod_f<- cor(data_s$y_var,data_s[[pred_mod]], use="complete")^2
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296
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RMSE_mod_f<- sqrt(mean(res_mod_s^2,na.rm=TRUE))
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297
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#MAE_mod<- sum(abs(res_mod),na.rm=TRUE)/(nv-sum(is.na(res_mod))) #MAE from kriged surface validation
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298
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MAE_mod_f<- mean(abs(res_mod_s), na.rm=TRUE)
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299
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300
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results_m1[1,1]<- dates[i] #storing the interpolation dates in the first column
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301
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results_m1[1,2]<- ns #number of stations used in the training stage
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302
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results_m1[1,3]<- "AICb"
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303
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results_m1[1,j+3]<- mod$results$AICb
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304
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305
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results_m2[1,1]<- dates[i] #storing the interpolation dates in the first column
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306
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results_m2[1,2]<- ns #number of stations used in the training
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307
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results_m2[1,3]<- "RSS"
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308
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results_m2[1,j+3]<- mod$results$rss
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309
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310
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results_m3[1,1]<- dates[i] #storing the interpolation dates in the first column
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311
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results_m3[1,2]<- ns #number of stations used in the training stage
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312
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results_m3[1,3]<- "AICc"
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313
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results_m3[1,j+3]<- mod$results$AICc
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314
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315
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results_RMSE_f[1,1]<- dates[i] #storing the interpolation dates in the first column
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316
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results_RMSE_f[1,2]<- ns #number of stations used in the training stage
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317
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results_RMSE_f[1,3]<- "RSME_f"
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318
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results_RMSE_f[1,j+3]<-RMSE_mod_f
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319
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320
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results_MAE_f[1,1]<- dates[i] #storing the interpolation dates in the first column
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321
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results_MAE_f[1,2]<- ns #number of stations used in the training stage
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322
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results_MAE_f[1,3]<- "MAE_f"
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323
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results_MAE_f[1,j+3]<-MAE_mod_f
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324
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325
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results_R2_f[1,1]<- dates[i] #storing the interpolation dates in the first column
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326
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results_R2_f[1,2]<- ns #number of stations used in the training stage
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327
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results_R2_f[1,3]<- "R2_f"
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328
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results_R2_f[1,j+3]<- R2_mod_f #Storing R2 for the model j
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329
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330
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results_RMSE[1,1]<- dates[i] #storing the interpolation dates in the first column
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331
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results_RMSE[1,2]<- ns #number of stations used in the training stage
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332
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results_RMSE[1,3]<- "RMSE"
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333
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results_RMSE[1,j+3]<- RMSE_mod #Storing RMSE for the model j
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334
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results_MAE[1,1]<- dates[i] #storing the interpolation dates in the first column
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335
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results_MAE[1,2]<- ns #number of stations used in the training stage
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336
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results_MAE[1,3]<- "MAE"
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337
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results_MAE[1,j+3]<- MAE_mod #Storing MAE for the model j
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338
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results_ME[1,1]<- dates[i] #storing the interpolation dates in the first column
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339
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results_ME[1,2]<- ns #number of stations used in the training stage
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340
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results_ME[1,3]<- "ME"
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341
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results_ME[1,j+3]<- ME_mod #Storing ME for the model j
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342
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results_R2[1,1]<- dates[i] #storing the interpolation dates in the first column
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343
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results_R2[1,2]<- ns #number of stations used in the training stage
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344
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results_R2[1,3]<- "R2"
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345
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results_R2[1,j+3]<- R2_mod #Storing R2 for the model j
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346
|
|
347
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#Saving residuals and prediction in the dataframes: tmax predicted from GAM
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348
|
|
349
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name2<-paste("res_mod",j,sep="")
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350
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data_v[[name2]]<-as.numeric(res_mod_v)
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351
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data_s[[name2]]<-as.numeric(res_mod_s)
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352
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#end of loop calculating RMSE
|
353
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}
|
354
|
}
|
355
|
|
356
|
#if (i==length(dates)){
|
357
|
|
358
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#Specific diagnostic measures related to the testing datasets
|
359
|
|
360
|
results_table_RMSE<-as.data.frame(results_RMSE)
|
361
|
results_table_MAE<-as.data.frame(results_MAE)
|
362
|
results_table_ME<-as.data.frame(results_ME)
|
363
|
results_table_R2<-as.data.frame(results_R2)
|
364
|
results_table_RMSE_f<-as.data.frame(results_RMSE_f)
|
365
|
results_table_MAE_f<-as.data.frame(results_MAE_f)
|
366
|
results_table_R2_f<-as.data.frame(results_R2_f)
|
367
|
|
368
|
results_table_m1<-as.data.frame(results_m1)
|
369
|
results_table_m2<-as.data.frame(results_m2)
|
370
|
results_table_m3<-as.data.frame(results_m3)
|
371
|
|
372
|
#Preparing labels
|
373
|
mod_labels<-rep("mod",nmodels)
|
374
|
index<-as.character(1:nmodels)
|
375
|
mod_labels<-paste(mod_labels,index,sep="")
|
376
|
|
377
|
tb_metrics1<-rbind(results_table_RMSE,results_table_MAE, results_table_ME,
|
378
|
results_table_R2,results_table_RMSE_f,results_table_MAE_f,results_table_R2_f) #
|
379
|
tb_metrics2<-rbind(results_table_m1,results_table_m2, results_table_m3)
|
380
|
cname<-c("dates","ns","metric", mod_labels)
|
381
|
#cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8")
|
382
|
colnames(tb_metrics1)<-cname
|
383
|
#cname<-c("dates","ns","metric","mod1", "mod2","mod3", "mod4", "mod5", "mod6", "mod7","mod8")
|
384
|
colnames(tb_metrics2)<-cname
|
385
|
|
386
|
#}
|
387
|
print(paste(dates[i],"processed"))
|
388
|
# Kriging object may need to be modified...because it contains the full image of prediction!!
|
389
|
##loop through model objects data frame and set field to zero...
|
390
|
|
391
|
#mod_obj<-list(mod1,mod2,mod3,mod4,mod5,mod6,mod7,mod8)
|
392
|
#names(mod_obj)<-c("mod1","mod2","mod3","mod4","mod5","mod6","mod7","mod8") #generate names automatically??
|
393
|
names(mod_obj)<-mod_labels
|
394
|
#results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2)
|
395
|
save(mod_obj,file= paste(path,"/","results_list_mod_objects_",dates[i],out_prefix,".RData",sep=""))
|
396
|
|
397
|
names(pred_gwr)<-mod_labels
|
398
|
save(pred_gwr,file= paste(path,"/","results_list_pred_mod_objects_",dates[i],out_prefix,".RData",sep=""))
|
399
|
|
400
|
results_list<-list(data_s,data_v,tb_metrics1,tb_metrics2,mod_obj)
|
401
|
names(results_list)<-c("data_s","data_v","tb_metrics1","tb_metrics2","mod_obj")
|
402
|
save(results_list,file= paste(path,"/","results_list_metrics_objects_",dates[i],out_prefix,".RData",sep=""))
|
403
|
return(results_list)
|
404
|
#return(tb_diagnostic1)
|
405
|
}
|