# tolEllipsePlot: Tolerance Ellipse Plot In robustbase: Basic Robust Statistics

## Description

Plots the 0.975 tolerance ellipse of the bivariate data set `x`. The ellipse is defined by those data points whose distance is equal to the squareroot of the 0.975 chisquare quantile with 2 degrees of freedom.

## Usage

 ```1 2 3 4 5 6 7``` ```tolEllipsePlot(x, m.cov = covMcd(x), cutoff = NULL, id.n = NULL, classic = FALSE, tol = 1e-07, xlab = "", ylab = "", main = "Tolerance ellipse (97.5%)", txt.leg = c("robust", "classical"), col.leg = c("red", "blue"), lty.leg = c("solid","dashed")) ```

## Arguments

 `x` a two dimensional matrix or data frame. `m.cov` an object similar to those of class `"mcd"`; however only its components `center` and `cov` will be used. If missing, the MCD will be computed (via `covMcd()`). `cutoff` numeric distance needed to flag data points outside the ellipse. `id.n` number of observations to be identified by a label. If not supplied, the number of observations with distance larger than `cutoff` is used. `classic` whether to plot the classical distances as well, `FALSE` by default. `tol` tolerance to be used for computing the inverse, see `solve`. Defaults to `1e-7`. `xlab, ylab, main` passed to `plot.default`. `txt.leg, col.leg, lty.leg` character vectors of length 2 for the legend, only used if `classic = TRUE`.

## Author(s)

Peter Filzmoser, Valentin Todorov and Martin Maechler

`covPlot` which calls `tolEllipsePlot()` when desired. `ellipsoidhull` and `predict.ellipsoid` from package cluster.
 ```1 2 3 4 5 6 7 8 9``` ```data(hbk) hbk.x <- data.matrix(hbk[, 1:3]) mcd <- covMcd(hbk.x) # compute mcd in advance ## must be a 2-dimensional data set: take the first two columns : tolEllipsePlot(hbk.x[,1:2]) ## an "impressive" example: data(telef) tolEllipsePlot(telef, classic=TRUE) ```