Description Usage Arguments Details Author(s) Examples
Plots a gemstone to an interactive graphics device.
1 | gem(coords, hull, clr)
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coords |
Matrix with coordinates of the grid or of data
points that belong to the gemstone, calculated by either
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hull |
Matrix with indices of triangles that cover a convex hull arround the gemstone. Each row represents one triangle and the indices refer to the rows of coords. |
clr |
Specifies the color of the gemstone. |
Only applicable to 3-dimensional data sets. Transparent colors are recommended for outer gemstone of the gemplot. Further graphical parameters can be set using material3d() of the rgl-package.
Jochen Kruppa, Klaus Jung
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 | ### Two 3-dimensional example data sets D1 and D2
n <- 200
x1 <- rnorm(n, 0, 1)
y1 <- rnorm(n, 0, 1)
z1 <- rnorm(n, 0, 1)
D1 <- data.frame(cbind(x1, y1, z1))
x2 <- rnorm(n, 1, 1)
y2 <- rnorm(n, 1, 1)
z2 <- rnorm(n, 1, 1)
D2 <- data.frame(cbind(x2, y2, z2))
colnames(D1) <- c("x", "y", "z")
colnames(D2) <- c("x", "y", "z")
### Placing outliers in D1 and D2
D1[17,] = c(4, 5, 6)
D2[99,] = -c(3, 4, 5)
### Grid size and graphic parameters
grid.size <- 20
red <- rgb(200, 100, 100, alpha = 100, maxColorValue = 255)
blue <- rgb(100, 100, 200, alpha = 100, maxColorValue = 255)
yel <- rgb(255, 255, 102, alpha = 100, maxColorValue = 255)
white <- rgb(255, 255, 255, alpha = 100, maxColorValue = 255)
require(rgl)
material3d(color=c(red, blue, yel, white), alpha=c(0.5, 0.5, 0.5, 0.5), smooth=FALSE, specular="black")
### Calucation and visualization of gemplot for D1
G <- gridfun(D1, grid.size=20)
G$H <- hldepth(D1, G, verbose=TRUE)
dm <- depmed(G)
B <- bag(D1, G)
L <- loop(D1, B, dm)
rgl.open()
points3d(D1[L$outliers==0,1], D1[L$outliers==0,2], D1[L$outliers==0,3],
col=red, size = 1)
text3d(D1[L$outliers==1,1], D1[L$outliers==1,2], D1[L$outliers==1,3], as.character(which(L$outliers==1)), col=yel)
spheres3d(dm[1], dm[2], dm[3], col=white, radius=0.1)
gem(B$coords, B$hull, red)
gem(L$coords.loop, L$hull.loop, red)
axes3d(col="white")
### Calucation and visualization of gemplot for D2
G <- gridfun(D2, grid.size=20)
G$H <- hldepth(D2, G, verbose=TRUE)
dm <- depmed(G)
B <- bag(D2, G)
L <- loop(D2, B, dm)
points3d(D2[L$outliers==0,1], D2[L$outliers==0,2], D2[L$outliers==0,3], col=blue)
text3d(D2[L$outliers==1,1], D2[L$outliers==1,2], D2[L$outliers==1,3], as.character(which(L$outliers==1)), col=yel)
spheres3d(dm[1], dm[2], dm[3], col=white, radius=0.1)
gem(B$coords, B$hull, blue)
gem(L$coords.loop, L$hull.loop, blue)
### Example of outlier detection with four principal components.
### Attention: calculation is currently time-consuming.
set.seed(123)
n <- 200
x1 <- rnorm(n, 0, 1)
x2 <- rnorm(n, 0, 1)
x3 <- rnorm(n, 0, 1)
x4 <- rnorm(n, 0, 1)
D <- data.frame(cbind(x1, x2, x3, x4))
D[67,] = c(7, 0, 0, 0)
date()
G = gridfun(D, 20, 4)
G$H = hldepth(D, G, verbose=TRUE)
dm = depmed(G)
B = bag(D, G)
L = loop(D, B, dm=dm)
which(L$outliers==1)
date()
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