# depth.L2: Calculate L2-Depth In ddalpha: Depth-Based Classification and Calculation of Data Depth

 depth.L2 R Documentation

## Calculate L2-Depth

### Description

Calculates the L2-depth of points w.r.t. a multivariate data set.

### Usage

``````depth.L2(x, data, mah.estimate = "moment", mah.parMcd = 0.75)
``````

### Arguments

 `x` Matrix of objects (numerical vector as one object) whose depth is to be calculated; each row contains a `d`-variate point. Should have the same dimension as `data`. `data` Matrix of data where each row contains a `d`-variate point, w.r.t. which the depth is to be calculated. `mah.estimate` is a character string specifying which estimates to use when calculating sample covariance matrix; can be `"none"`, `"moment"` or `"MCD"`, determining whether traditional moment or Minimum Covariance Determinant (MCD) (see `covMcd`) estimates for mean and covariance are used. By default `"moment"` is used. With `"none"` the non-affine invariant version of the L2-depth is calculated `mah.parMcd` is the value of the argument `alpha` for the function `covMcd`; is used when `mah.estimate =` `"MCD"`.

### Details

Calculates L2-depth (Mosler, 2013). L2-depth is based on the oultyingness distance calculated as the average L2-distance from (a row of) `x` to each point in `data`.

### Value

Numerical vector of depths, one for each row in `x`; or one depth value if `x` is a numerical vector.

### References

Mosler, K. (2013). Depth statistics. In: Becker, C., Fried, R. and Kuhnt, S. (eds), Robustness and Complex Data Structures: Festschrift in Honour of Ursula Gather, Springer-Verlag (Berlin, Heidelberg), 17–34.

`depth.halfspace` for calculation of the Tukey depth.

`depth.Mahalanobis` for calculation of Mahalanobis depth.

`depth.projection` for calculation of projection depth.

`depth.qhpeeling` for calculation of convex hull peeling depth.

`depth.simplicial` for calculation of simplicial depth.

`depth.simplicialVolume` for calculation of simplicial volume depth.

`depth.spatial` for calculation of spatial depth.

`depth.potential` for calculation of data potential.

`depth.zonoid` for calculation of zonoid depth.

### Examples

``````# 5-dimensional normal distribution
data <- mvrnorm(1000, rep(0, 5),
matrix(c(1, 0, 0, 0, 0,
0, 2, 0, 0, 0,
0, 0, 3, 0, 0,
0, 0, 0, 2, 0,
0, 0, 0, 0, 1),
nrow = 5))
x <- mvrnorm(10, rep(1, 5),
matrix(c(1, 0, 0, 0, 0,
0, 1, 0, 0, 0,
0, 0, 1, 0, 0,
0, 0, 0, 1, 0,
0, 0, 0, 0, 1),
nrow = 5))

depths <- depth.spatial(x, data)
cat("Depths:", depths, "\n")
``````

ddalpha documentation built on May 29, 2024, 1:12 a.m.