Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
11 11 11 11 10 14 9 11 11 12 10 10 10 14 12 10 9 10 12 10 13 8 12 11 11 12 12 12 11 14 11 11 11 10 12 12 10 14 12 12 11 11 11 15 11 11 10 0 10 11 12 10 12 9 10 7 9 12 11 12 13 11 11 12 9 13 11 10 8 11 12 11 8 11 8 11 7 10 9 11 11 9 11 11 13 10 14 11 11 9 10 13 9 12 11 14 12 12 7 11 11 12 12 11 11 11 15 12 12 11 12 10 11 11 9 14 11 8 11 11 13 11 11 14 11 11 11 8 10 11 11 14 12 12 12 10 13 3 10 13 7 11 12 11 11 11 12 8 14 14 12 12 14 12 10 11 11 7 13 10 11 9 13 11 10 11 12 11 11
Data Y:
10 13 14 8 8 13 13 9 9 14 14 12 12 11 12 14 8 0 11 9 13 8 13 8 9 8 12 12 13 13 10 12 13 9 10 14 8 10 10 14 8 14 10 13 12 12 9 12 10 3 14 10 9 8 11 10 8 14 12 8 14 13 13 13 12 10 14 11 10 13 8 8 7 7 9 12 13 11 10 14 9 7 9 13 9 15 13 14 8 13 11 8 14 9 14 8 12 13 14 13 4 9 12 10 8 9 8 9 8 12 8 7 8 9 14 12 13 9 13 11 12 11 8 12 9 12 13 9 8 8 8 12 13 7 8 8 13 3 12 15 14 7 11 12 10 14 10 15 11 8 6 12 13 12 9 8 14 7 8 14 12 15 11 8 8 7 12 7 11
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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation