# Calculates the bag Calculates the bag of a gemplot (i.e. the inner gemstone). ```r 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`. ## Returns 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. ## 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`). ## Examples ```r ## 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) ``` ## References Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999). The bagplot: a bivariate boxplot. **The American Statistician**, 53(4) , 382-387. 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](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/](https://software.klausjung-lab.de/). ## Author(s) Jochen Kruppa, Klaus Jung

bag function