Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
244.576 241.572 240.541 236.089 236.997 264.579 270.349 269.645 267.037 258.113 262.813 267.413 267.366 264.777 258.863 254.844 254.868 277.267 285.351 286.602 283.042 276.687 277.915 277.128 277.103 275.037 270.150 267.140 264.993 287.259 291.186 292.300 288.186 281.477 282.656 280.190 280.408 276.836 275.216 274.352 271.311 289.802 290.726 292.300 278.506 269.826 265.861 269.034 264.176 255.198 253.353 246.057 235.372 258.556 260.993 254.663 250.643 243.422 247.105 248.541 245.039 237.080 237.085 225.554 226.839 247.934 248.333 246.969 245.098 246.263 255.765 264.319 268.347 273.046 273.963 267.430 271.993 292.710 295.881
Data Y:
272.433 268.361 268.586 264.768 269.974 304.744 309.365 308.347 298.427 289.231 291.975 294.912 293.488 290.555 284.736 281.818 287.854 316.263 325.412 326.011 328.282 317.480 317.539 313.737 312.276 309.391 302.950 300.316 304.035 333.476 337.698 335.932 323.931 313.927 314.485 313.218 309.664 302.963 298.989 298.423 301.631 329.765 335.083 327.616 309.119 295.916 291.413 291.542 284.678 276.475 272.566 264.981 263.290 296.806 303.598 286.994 276.427 266.424 267.153 268.381 262.522 255.542 253.158 243.803 250.741 280.445 285.257 270.976 261.076 255.603 260.376 263.903 264.291 263.276 262.572 256.167 264.221 293.860 300.713
Data Z:
97 105.4 102.7 98.1 104.5 87.4 89.9 109.8 111.7 98.6 96.9 95.1 97 112.7 102.9 97.4 111.4 87.4 96.8 114.1 110.3 103.9 101.6 94.6 95.9 104.7 102.8 98.1 113.9 80.9 95.7 113.2 105.9 108.8 102.3 99 100.7 115.5 100.7 109.9 114.6 85.4 100.5 114.8 116.5 112.9 102 106 105.3 118.8 106.1 109.3 117.2 92.5 104.2 112.5 122.4 113.3 100 110.7 112.8 109.8 117.3 109.1 115.9 96 99.8 116.8 115.7 99.4 94.3 91 93.2 103.1 94.1 91.8 102.7 82.6 89.1
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