bag {RepeatedHighDim}

Calculates the bag

Description

Calculates the bag of a gemplot (i.e. the inner gemstone).

Usage

bag(D, G)

Arguments

D

Data set with rows representing the individuals and columns representing the features. In the case of three dimensions, the colnames of D must be c("x", "y", "z").

G

List containing the grid information produced by gridfun and the halfspace location depths calculated by hldepth.

Details

Determines those grid points that belong to the bag, i.e. a convex hull that contains 50 percent of the data. In the case of a 3-dimensional data set, the bag can be visualized by an inner gemstone that can be accompanied by an outer gemstone (loop).

Value

A list containg the following elements:

coords

Coordinates of the grid points that belong to the bag. Each row represents a grid point and each column represents one dimension.

hull

A data matrix that contains the indices of the margin grid points of the bag that cover the convex hull by triangles. Each row represents one triangle. The indices correspond to the rows of coords.

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)

## End(Not run)