gemPlot: Calculation and visualization of gemplots (3-dimensional...

In jkruppa/gemPlot: Calculation and visualization of gemplots (3-dimensional extension of boxplot and bagplot).

Description

Gemplots are the 3-dimensional extension of the 1-dimensional boxplots and the 2-dimensional bagplot. The package calculates a 3-dimensional median (depmed), an inner bag (bag) that contains 50 percent of the data and an outer bag (loop). Data points outside the outer bag are flagged as outliers. The gemplot can be visualized in a 3-dimensional interactice device (gem). The calculations are based on the concept of halfspace location depths. The halfspace location depths need to be calculated in a user specified grid prior to the calculation of the gemplots components (gridfun, hldepth). Outlier detection for more than 3 dimensions is also implemented in the above workflow. An aditional parameter k needs then to be specified in the gridfun function.

References

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

• Kruppa, J. and Jung, K. (2017) Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots. BMC Bioinformatics, 18, 232

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$H <- hldepth(D1, G, verbose=TRUE)
dm <- depmed(G)
B <- bag(D1, G)
L <- loop(D1, B, inflation = 3, dm = dm)
rgl.open()
points3d(D1[L$outliers==0,1], D1[L$outliers==0,2], D1[L$outliers==0,3], col=red)
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)
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$H <- hldepth(D2, G, verbose=TRUE)
dm <- depmed(G)
B <- bag(D2, G)
L <- loop(D2, B, inflation = 3, dm = dm)
points3d(D2[L$outliers==0,1], D2[L$outliers==0,2], D2[L$outliers==0,3], col=red)
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)

jkruppa/gemPlot documentation built on May 19, 2019, 12:45 p.m.