Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
33 39 45 46 45 45 49 50 54 59 58 56 48 50 52 53 55 43 42 38 41 41 39 34 27 15 14 31 41 43 46 42 45 45 40 35 36 38 39 32 24 21 12 29 36 31 28 30 38 27 40 40 44 47 45 42 38 46 37 41 40 33 34 36 36 38 42 35 25 24 22 27 17 30 30 34 37 36 33 33 33 37 40 35 37 43 42 33 39 40 37 44 42 43 40 30 30 31 18 24 22 26 28 23 17 12 9 19 21 18 18 15 24 18 19 30 33 35 36 47 46
Data Y:
62 64 62 64 64 69 69 65 56 58 53 62 55 60 59 58 53 57 57 53 54 53 57 57 55 49 50 49 54 58 58 52 56 52 59 53 52 53 51 50 56 52 46 48 46 48 48 49 53 48 51 48 50 55 52 53 52 55 53 53 56 54 52 55 54 59 56 56 51 53 52 51 46 49 46 55 57 53 52 53 50 54 53 50 51 52 47 51 49 53 52 45 53 51 48 48 48 48 40 43 40 39 39 36 41 39 40 39 46 40 37 37 44 41 40 36 38 43 42 45 46
Data Z:
46 46 41 43 39 36 34 31 27 27 30 28 31 25 26 22 27 38 39 45 48 46 47 55 63 83 100 76 68 63 63 62 62 64 72 67 67 67 69 78 80 85 90 83 84 84 89 82 76 97 82 79 73 72 74 77 78 68 71 68 70 79 78 79 78 77 72 72 84 83 82 78 77 75 77 73 64 64 71 65 68 64 65 56 57 51 52 79 59 54 55 47 52 52 54 49 48 44 47 43 45 40 38 46 51 49 57 61 61 81 97 105 108 113 111 111 110 109 105 97 97
Sample Range:
(leave blank to include all observations)
From:
To:
gridsize on x-axis
(?)
gridsize on y-axis
(?)
plot contours
Y
Y
N
plot points
Y
Y
N
Name of dataset X
Name of dataset Y
Name of dataset Z
Chart options
R Code
x <- array(x,dim=c(length(x),1)) colnames(x) <- par5 y <- array(y,dim=c(length(y),1)) colnames(y) <- par6 z <- array(z,dim=c(length(z),1)) colnames(z) <- par7 d <- data.frame(cbind(z,y,x)) colnames(d) <- list(par7,par6,par5) par1 <- as.numeric(par1) par2 <- as.numeric(par2) if (par1>500) par1 <- 500 if (par2>500) par2 <- 500 if (par1<10) par1 <- 10 if (par2<10) par2 <- 10 library(GenKern) library(lattice) panel.hist <- function(x, ...) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='black', ...) } bitmap(file='cloud1.png') cloud(z~x*y, screen = list(x=-45, y=45, z=35),xlab=par5,ylab=par6,zlab=par7) dev.off() bitmap(file='cloud2.png') cloud(z~x*y, screen = list(x=35, y=45, z=25),xlab=par5,ylab=par6,zlab=par7) dev.off() bitmap(file='cloud3.png') cloud(z~x*y, screen = list(x=35, y=-25, z=90),xlab=par5,ylab=par6,zlab=par7) dev.off() bitmap(file='pairs.png') pairs(d,diag.panel=panel.hist) dev.off() x <- as.vector(x) y <- as.vector(y) z <- as.vector(z) bitmap(file='bidensity1.png') op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=cor(x,y), xbandwidth=dpik(x), ybandwidth=dpik(y)) image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,y)',xlab=par5,ylab=par6) if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE) if (par4=='Y') points(x,y) (r<-lm(y ~ x)) abline(r) box() dev.off() bitmap(file='bidensity2.png') op <- KernSur(y,z, xgridsize=par1, ygridsize=par2, correlation=cor(y,z), xbandwidth=dpik(y), ybandwidth=dpik(z)) op image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (y,z)',xlab=par6,ylab=par7) if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE) if (par4=='Y') points(y,z) (r<-lm(z ~ y)) abline(r) box() dev.off() bitmap(file='bidensity3.png') op <- KernSur(x,z, xgridsize=par1, ygridsize=par2, correlation=cor(x,z), xbandwidth=dpik(x), ybandwidth=dpik(z)) op image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,z)',xlab=par5,ylab=par7) if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE) if (par4=='Y') points(x,z) (r<-lm(z ~ x)) abline(r) box() dev.off()
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