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Data X:
182 110 162 155 141 165 101 152 127 128 121 112 142 164 156 161 176 152 155 85 164 155 88 145 122 161 150 132 171 118 96 114 140 139 184 121 139 140 146 148 112 146 126 169 75 83 163 180 181 168 94 114 107 152 186 118 133 90 127 87 121 103 50 134 89 84 163 50 98 96 123 104 122 124 128 123 76 85 121 88 116 137 66 136 159 102 110 104 107 158 126 83 48 97 63 131 93 97 105 88 89 95 70 81 107 84 129 77 134 84 58 69 93 166 40 102 97 75 67 81 146 103 48 63 147 70 64 77 146 103 63 126 41 65 30 35 56 37 30 20 49 8 22 21 23 12 13 16 18 1 12 8 4 4 0 7 0 0 0 0 0 0 0 0
Data Y:
198 153 150 148 147 138 137 136 136 135 130 129 127 126 126 122 121 121 121 120 118 118 116 116 115 112 112 112 111 111 110 108 107 107 107 106 106 104 104 104 103 103 103 102 102 101 101 100 99 99 97 97 96 95 95 94 94 91 91 90 90 90 89 89 89 87 87 87 87 86 86 86 86 86 85 85 84 84 84 83 82 82 82 80 79 79 79 79 79 78 77 76 75 74 74 73 72 72 72 72 71 71 71 70 69 69 69 68 67 66 66 65 65 65 65 64 63 63 63 61 60 60 60 58 58 56 56 55 55 54 51 50 50 42 41 40 38 36 28 25 22 20 20 18 17 13 12 11 9 9 7 7 6 6 5 2 0 0 0 0 0 0 0 0
Sample Range:
(leave blank to include all observations)
From:
To:
xgridsize
ygridsize
xbandwidth
(zero to use default)
ybandwidth
(zero to use default)
correlation
(zero to use actual correlation)
display contours
(Y/N)
Y
Y
N
display data points
(Y/N)
Y
Y
N
colors
terrain.colors
terrain.colors
rainbow
heat.colors
topo.colors
cm.colors
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
par1 <- as(par1,'numeric') par2 <- as(par2,'numeric') par3 <- as(par3,'numeric') par4 <- as(par4,'numeric') par5 <- as(par5,'numeric') library('GenKern') x <- x[!is.na(y)] y <- y[!is.na(y)] y <- y[!is.na(x)] x <- x[!is.na(x)] if (par3==0) par3 <- dpik(x) if (par4==0) par4 <- dpik(y) if (par5==0) par5 <- cor(x,y) if (par1 > 500) par1 <- 500 if (par2 > 500) par2 <- 500 if (par8 == 'terrain.colors') mycol <- terrain.colors(100) if (par8 == 'rainbow') mycol <- rainbow(100) if (par8 == 'heat.colors') mycol <- heat.colors(100) if (par8 == 'topo.colors') mycol <- topo.colors(100) if (par8 == 'cm.colors') mycol <- cm.colors(100) bitmap(file='bidensity.png') op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=par5, xbandwidth=par3, ybandwidth=par4) image(op$xords, op$yords, op$zden, col=mycol, axes=TRUE,main=main,xlab=xlab,ylab=ylab) if (par6=='Y') contour(op$xords, op$yords, op$zden, add=TRUE) if (par7=='Y') points(x,y) (r<-lm(y ~ x)) abline(r) box() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Bandwidth',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'x axis',header=TRUE) a<-table.element(a,par3) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'y axis',header=TRUE) a<-table.element(a,par4) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'correlation used in KDE',header=TRUE) a<-table.element(a,par5) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'correlation(x,y)',header=TRUE) a<-table.element(a,cor(x,y)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Raw Output
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Computing time
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R Server
Big Analytics Cloud Computing Center
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