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Data X:
9884.9 10174.5 11395.4 10760.2 10570.1 10536 9902.6 8889 10837.3 11624.1 10509 10984.9 10649.1 10855.7 11677.4 10760.2 10046.2 10772.8 9987.7 8638.7 11063.7 11855.7 10684.5 11337.4 10478 11123.9 12909.3 11339.9 10462.2 12733.5 10519.2 10414.9 12476.8 12384.6 12266.7 12919.9 11497.3 12142 13919.4 12656.8 12034.1 13199.7 10881.3 11301.2 13643.9 12517 13981.1 14275.7 13435 13565.7 16216.3 12970 14079.9 14235 12213.4 12581 14130.4 14210.8 14378.5 13142.8 13714.7 13621.9 15379.8 14441.8 15354.8 15537.8 14552.7
Data Y:
1962.3 2095.2 2161 2115.1 1929 2004.5 2009.9 1524.9 2061.1 2261.6 2103.6 2224.3 2173.8 2119.2 2226.4 2159.6 1918.3 2116.1 1948.3 1514.3 2180.5 2312.6 2019.8 2200.8 2028.9 2178.7 2433.7 2230.5 1884.2 2372.7 1918.6 1679.4 2327.3 2225.2 2211.7 2463.6 2029.5 2173.6 2387 2234 2179.9 2397 1960.2 1824.1 2479.3 2234.9 2345.9 2428.9 2179.4 2216.9 2642.3 2340.5 2474.6 2641.8 2165.1 1996.2 2562.9 2529.9 2549.6 2455.1 2472 2424.7 2820.1 2666 2654.6 2732.2 2546.9
Data Z:
89.9 89.3 105.7 100.3 108.2 105.3 97.9 83.7 101.5 97.9 96 173.9 115.8 109.2 112.2 109.7 102.4 110.2 98.3 88.1 120 121.9 104.6 112.5 107 107.8 137.8 128.8 124.6 133 130.9 156.1 181.6 150.4 140.4 130.1 114.5 108.5 132.3 133.9 109.1 294.6 103.6 101.5 125.7 130.2 130 145.4 124.8 132.2 155.7 131.5 129.5 142.9 131.5 130.4 140.4 152.6 155 136.1 143.9 145.7 165.2 240 152.7 160.3 128.6
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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
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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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