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Data X:
106.7 110.2 125.9 100.1 106.4 114.8 81.3 87 104.2 108 105 94.5 92 95.9 108.8 103.4 102.1 110.1 83.2 82.7 106.8 113.7 102.5 96.6 92.1 95.6 102.3 98.6 98.2 104.5 84 73.8 103.9 106 97.2 102.6 89 93.8 116.7 106.8 98.5 118.7 90 91.9 113.3 113.1 104.1 108.7 96.7 101 116.9 105.8 99 129.4 83 88.9 115.9 104.2 113.4 112.2 100.8 107.3 126.6 102.9 117.9 128.8 87.5 93.8 122.7 126.2 124.6 116.7 115.2 111.1 129.9 113.3 118.5 133.5 102.1 102.4
Data Y:
97.3 101 113.2 101 105.7 113.9 86.4 96.5 103.3 114.9 105.8 94.2 98.4 99.4 108.8 112.6 104.4 112.2 81.1 97.1 112.6 113.8 107.8 103.2 103.3 101.2 107.7 110.4 101.9 115.9 89.9 88.6 117.2 123.9 100 103.6 94.1 98.7 119.5 112.7 104.4 124.7 89.1 97 121.6 118.8 114 111.5 97.2 102.5 113.4 109.8 104.9 126.1 80 96.8 117.2 112.3 117.3 111.1 102.2 104.3 122.9 107.6 121.3 131.5 89 104.4 128.9 135.9 133.3 121.3 120.5 120.4 137.9 126.1 133.2 146.6 103.4 117.2
Data Z:
124.9 132 151.4 108.9 121.3 123.4 90.3 79.3 117.2 116.9 120.8 96.1 100.8 105.3 116.1 112.8 114.5 117.2 77.1 80.1 120.3 133.4 109.4 93.2 91.2 99.2 108.2 101.5 106.9 104.4 77.9 60 99.5 95 105.6 102.5 93.3 97.3 127 111.7 96.4 133 72.2 95.8 124.1 127.6 110.7 104.6 112.7 115.3 139.4 119 97.4 154 81.5 88.8 127.7 105.1 114.9 106.4 104.5 121.6 141.4 99 126.7 134.1 81.3 88.6 132.7 132.9 134.4 103.7 119.7 115 132.9 108.5 113.9 142.9 95.2 93
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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
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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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Big Analytics Cloud Computing Center
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