depth.spatial: Calculate Spatial Depth
In ddalpha: Depth-Based Classification and Calculation of Data Depth

depth.spatial

R Documentation

Calculate Spatial Depth

Description

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

Usage

depth.spatial(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 Spatial depth is calculated
`mah.parMcd`	is the value of the argument `alpha` for the function `covMcd`; is used when `mah.estimate =` `"MCD"`.

Details

Calculates spatial depth. Spatial depth (also L1-depth) is a distance-based depth exploiting the idea of spatial quantiles of Chaudhuri (1996) and Koltchinskii (1997), formulated by Vardi & Zhang (2000) and Serfling (2002).

Value

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

References

Chaudhuri, P. (1996). On a geometric notion of quantiles for multivariate data. Journal of the Americal Statistical Association 91 862–872.

Koltchinskii, V.I. (1997). M-estimation, convexity and quantiles. The Annals of Statistics 25 435–477.

Serfling, R. (2006). Depth functions in nonparametric multivariate inference. In: Liu, R., Serfling, R., Souvaine, D. (eds.), Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications, American Mathematical Society, 1–16.

Vardi, Y. and Zhang, C.H. (2000). The multivariate L1-median and associated data depth. Proceedings of the National Academy of Sciences, U.S.A. 97 1423–1426.

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 Oct. 1, 2024, 1:07 a.m.