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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:
10 15 14 14 8 19 17 18 10 15 16 12 13 10 14 15 20 9 12 13 16 12 14 15 19 16 16 14 14 14 13 18 15 15 15 13 14 15 14 19 16 16 12 10 11 13 14 11 11 16 9 16 19 13 15 14 15 11 14 15 17 16 13 15 14 15 14 12 12 15 17 13 5 7 10 15 9 9 15 14 11 18 20 20 16 15 14 13 18 14 12 9 19 13 12 14 6 14 11 11 14 12 19 13 14 17 12 16 15 15 15 16 15 12 13 14 17 14 14 14 15 11 11 16 12 12 19 18 16 16 13 11 10 14 14 14 16 10 16 7 16 15 17 11 11 10 13 14 13 13 12 10 15 6 15 15 11 14 14 16 12 15 20 12 9 13 15 19 11 11 17 15 14 15 11 12 15 16 16
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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R Server
Big Analytics Cloud Computing Center
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