gem {RepeatedHighDim}

Plots a gemstone to an interactive graphics device

Description

Plots a gemstone to an interactive graphics device.

Usage

gem(coords, hull, clr)

Arguments

coords

Matrix with coordinates of the grid or of data points that belong to the gemstone, calculated by either bag or loop. Each row represents a grid point and each column represents one dimension.

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.

Details

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.

Author(s)

Jochen Kruppa, Klaus Jung

References

Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999). The bagplot: a bivariate boxplot. The American Statistician, 53(4), 382-387. doi:10.1080/00031305.1999.10474494

Kruppa, J., & Jung, K. (2017). Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots. BMC bioinformatics, 18(1), 1-10. https://link.springer.com/article/10.1186/s12859-017-1645-5

See Also

For more information, please refer to the package's documentation and the tutorial: https://software.klausjung-lab.de/.

Examples

## Attention: calculation is currently time-consuming.
## Not run: 

# 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$H <- hldepth(D1, G, verbose=TRUE)
dm <- depmed(G)
B <- bag(D1, G)
L <- loop(D1, B, dm=dm)
bg3d(color = "gray39" )
points3d(D1[L$outliers==0,1], D1[L$outliers==0,2], D1[L$outliers==0,3], col="green")
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=yel, radius=0.1)
material3d(1,alpha=0.4)
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$H <- hldepth(D2, G, verbose=TRUE)
dm <- depmed(G)
B <- bag(D2, G)
L <- loop(D2, B, dm=dm)
points3d(D2[L$outliers==0,1], D2[L$outliers==0,2], D2[L$outliers==0,3], col="green")
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=yel, 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()

## End(Not run)