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Data X:
107.2 107 119 110.4 101.7 102.4 98.8 105.6 104.4 106.3 107.2 108.5 106.9 114.2 125.9 110.6 110.5 106.7 104.7 107.4 109.8 103.4 114.8 114.3 109.6 118.3 127.3 112.3 114.9 108.2 105.4 122.1 113.5 110 125.3 114.3 115.6 127.1 123 122.2 126.4 112.7 105.8 120.9 116.3 115.7 127.9 108.3 121.1 128.6 123.1 127.7 126.6 118.4 110 129.6 115.8 125.9 128.4 114 125.6 128.5 136.6 133.1 124.6 123.5 117.2 135.5 124.8 127.8 133.1 125.7 128.4 131.9 146.3 140.6 129.5 132.4 125.9 126.9 135.8
Data Y:
80.6 104.1 108.2 93.4 71.9 94.1 94.9 96.4 91.1 84.4 86.4 88 75.1 109.7 103 82.1 68 96.4 94.3 90 88 76.1 82.5 81.4 66.5 97.2 94.1 80.7 70.5 87.8 89.5 99.6 84.2 75.1 92 80.8 73.1 99.8 90 83.1 72.4 78.8 87.3 91 80.1 73.6 86.4 74.5 71.2 92.4 81.5 85.3 69.9 84.2 90.7 100.3 79.4 84.8 92.9 81.6 76 98.7 89.1 88.7 67.1 93.6 97 100.8 80.1 80.7 89.4 81.5 73.6 90.9 97.3 84.3 65.6 87.3 90.5 82.4 80.4
Data Z:
88.6 105.4 119.9 107.2 84.1 101.4 105.1 118.7 113.8 113.8 118.9 98.5 91 120.7 127.9 112.4 93.1 107.5 107.3 114.8 120.8 112.2 123.3 100.6 86.7 123.6 125.3 111.1 98.4 102.3 105 128.2 124.7 116.1 131.2 97.7 88.8 132.8 113.9 112.6 104.3 107.5 106 117.3 123.1 114.3 132 92.3 93.7 121.3 113.6 116.3 98.3 111.9 109.3 133.2 118 131.6 134.1 96.7 99.8 128.3 134.9 130.7 107.3 121.6 120.6 140.5 124.8 129.9 159.4 111 110.1 132.7 135 118.6 94 117.9 114.7 113.6 130.6
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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()
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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