# Calculates the fence and the loop Calculates the fence and the loop of a gemplot (i.e. the outer gemstone). ```r loop(D, B, inflation = 3, dm) ``` ## 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"). - `B`: List containing the information about the coordinates of the bag and the convex hull that forms the bag (determined by `bag`). - `inflation`: A numeric value > 0 that specifies the inflation factor of the bag relative to the median (default = 3). - `dm`: The coordinates of the depth median as produced by `depmed`. ## Returns A list containing the following elements: - ****coords.loop****: Coordinates of the data points that are inside the convex hull around the loop. - ****hull.loop****: A data matrix that contains the indices of the margin data points of the loop that cover the convex hull by triangles. Each row represnts one triangle. The indices correspond to the rows of coords.loop. - ****coords.fence****: Coordinates of the grid points that are inside the fence but outside the bag. - ****hull.fence****: A data matrix that contains the indices of the margin grid points of the fence that cover the convex hull around the fence by triangles. Each row represnts one triangle. The indices correspond to the rows of coords.fence. - ****outliers****: A vector of length equal to the sample size. Data points that are inside the fence are labelled by 0 and values outside the fence (i.e. outliers) are labelled by 1. ## Details The fence inflates the the bag relative to the depth median by the factor inflation. Data points outside the bag and inside the fence the loop or outer gemstone are flagged as outliers. Data points outside the fence are marked as outliers. In the case of a 3-dimensional data set, the loop can be visualized by an outer gemstone around the inner gemstone or bag. ## 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

loop function