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Data X:
3 3 4 4 7 9 10 11 12 12 13 13 14 14 14 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 16 16 17 17 17 17 18 18 18 18 18 18 18 18 18 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 22 22 22 22 22 22 22 22 22 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23 23
Data Y:
15.25 11.7 18.55 9.5 14.75 10.9 10.4 12.4 12.6 11.9 14.3 11.7 12.6 15.6 14.35 6.4 10.0 11.8 12.6 13.6 6.4 11.5 8.3 12.7 13.0 13.6 14.8 14.35 19.1 9.85 15.6 17.1 13.6 14.5 9.9 13.6 17.6 7.4 6.3 13.8 9.7 13.3 11.1 13.35 18.4 17.75 8.2 12.0 11.7 10.9 15.9 9.2 13.0 11.4 10.6 16.15 14.75 12.45 12.65 16.1 16.85 17.1 19.3 19.05 13.35 11.95 14.8 10.6 11.9 13.2 16.2 17.75 17.65 13.35 7.6 12.75 19.1 12.85 14.05 12.9 11.1 9.3 10.8 10.3 7.6 18.1 8.6 12.2 12.8 13.3 11.4 9.3 12.7 18.4 16.1 7.7 6.7 13.8 9.9 10.8 5.9 11.4 11.3 12.5 4.35 19.1 16.1 14.7 17.35 17.65 16.35 15.6 16.1 19.25 15.25 18.15 18.4 17.75 14.6
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
par8 <- 'terrain.colors' par7 <- 'Y' par6 <- 'Y' par5 <- '0' par4 <- '0' par3 <- '0' par2 <- '50' par1 <- '50' 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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