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Data X:
10 9 12 14 6 13 12 13 6 12 10 9 12 7 10 11 15 10 12 10 12 11 11 12 15 12 11 9 11 11 9 15 12 9 12 12 9 9 11 12 12 12 12 6 11 12 9 11 9 10 10 9 12 11 9 9 12 6 10 12 11 14 8 9 10 10 10 11 10 12 14 10 8 8 7 11 6 9 12 12 12 9 15 15 13 9 12 9 15 11 11 6 14 11 8 10 10 9 8 9 10 11 14 12 9 13 8 12 14 9 10 12 12 9 9 12 15 12 11 8 11 11 10 12 9 11 15 14 6 9 9 8 7 10 6 9 9 7 11 9 12 9 10 11 7 12 8 13 11 11 12 11 12 3 10 13 10 6 11 12 9 10 15 9 6 9 15 15 9 11 9 11 10 9 6 12 13 12 12
Data Y:
21 22 17 21 19 23 21 22 11 20 18 16 18 13 17 20 20 15 18 15 19 19 19 20 20 16 18 17 18 13 20 21 17 19 20 15 15 19 18 22 20 18 14 15 17 16 17 15 17 18 16 18 22 16 16 20 18 16 16 20 21 18 15 18 18 20 18 16 19 20 22 18 8 13 13 18 12 16 21 20 18 22 23 23 21 16 14 18 22 20 18 12 17 15 18 18 15 16 15 16 19 19 23 20 18 21 19 18 19 17 21 19 24 12 15 18 19 22 19 16 19 18 18 19 21 19 22 23 17 18 19 15 14 18 17 19 16 14 20 16 18 16 21 16 14 16 19 19 19 18 16 14 19 11 18 18 16 20 18 20 16 18 19 19 15 17 21 24 16 13 21 16 17 17 18 18 23 20 20
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 Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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