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CSV
Data X:
91 137 92 148 131 59 128 90 83 116 42 155 96 49 104 76 66 74 99 96 108 97 116 106 80 74 114 87 140 127 74 91 98 126 98 95 133 110 70 95 86 130 96 99 68 121 131 71 102 68 89 87 49 96 100 141 102 110 100 146 147 94 52 61 98 60 118 109 68 109 115 78 118 73 162 122 65 100 82 52 115 90 121 101 104 42 96 110 108 113 57 86 88 115 85 111 102 86 114 94 64 77 105 49 95 89 78 110 117 63 131 102 63 117 57 73 77 105 31 112 49 56 48 63 162 81 110 104 88 99 76 109 120 91 108 119
Data Y:
11.3 9.6 13.4 16.1 12.7 12.3 5.7 7.9 12.3 11.6 6.7 12.1 13.3 8 12.2 8.8 9.1 9.9 14.6 10.5 12.6 4.3 13.4 11.8 11.2 12.6 5.6 10.9 9.9 10.3 11.4 8.6 7.7 7.3 11.4 13.6 13.2 7.9 10.7 8.8 8.3 9.6 14.2 11.1 12.6 9 18.9 11.6 10.3 14.6 13.85 15.9 10.95 15.1 15.95 14.6 8.5 17.6 13.5 15.35 12.9 4.9 6.4 12.6 9.6 10.35 11.6 15.4 9.6 16.6 14.85 11.75 18.45 14.85 19.9 18.45 13.6 15 11.35 12.65 18.1 13.4 13.9 16.6 15.25 11.2 15.85 16.1 17.35 13.15 11.15 15.6 13.1 12.15 12.4 18.2 14.9 11.2 14.6 14.75 7.85 13.6 7.85 10.95 9.95 14.1 14.9 16.25 15.65 14.6 19.2 14.9 13.4 16.85 10.95 12.2 13.2 15.2 8.1 15.65 7.65 15.2 11.85 11.4 19.9 15.15 16.85 12.6 12.35 16.65 13.95 15.7 15.35 15.1 17.75 16.65
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
N
display data points
(Y/N)
Y
N
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
0 seconds
R Server
Big Analytics Cloud Computing Center
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