gem: Plots a gemstone to an interactive graphics device

In RepeatedHighDim: Methods for High-Dimensional Repeated Measures Data

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. \Sexpr[results=rd]{tools:::Rd_expr_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

Examples

## Attention: calculation is currently time-consuming.
## Remove #-Symbols to run examples

## 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()

RepeatedHighDim documentation built on July 9, 2023, 6:33 p.m.