CovLP: CovLp
In zzawadz/DepthProc: Statistical Depth Functions for Multivariate Analysis

Description Usage Arguments Details Value Author(s) See Also Examples

Weighted by L ^ p depth (outlyingness) multivariate location and scatter estimators.

1	CovLP(x, pdim = 2, la = 1, lb = 1)

`x`	The data as a matrix or data frame. If it is a matrix or data frame, then each row is viewed as one multivariate observation.
`pdim`	The parameter of the weighted {L} ^ {p} dim depth
`la`	parameter of a simple weight function w = ax + b
`lb`	parameter of a simple weight function w = ax + b

Using depth function one can define a depth-weighted location and scatter estimators. In case of location estimator we have

L(F) = {\int {{x}{{w}_{1}}(D({x}, F))dF({x})}} / {{{w}_{1}}(D({x}, F))dF({x})}

Subsequently, a depth-weighted scatter estimator is defined as

S(F) = \frac{ \int {({x} - L(F)){{({x} - L(F))} ^ {T}}{{w}_{2}}(D({x}, F))dF({x})} }{ \int {{{w}_{2}}(D({x}, F))dF({x})}},

where {{w}_{2}}(\cdot) is a suitable weight function that can be different from {{w}_{1}}(\cdot) .

The DepthProc package offers these estimators for weighted {L} ^ {p} depth. Note that L(\cdot) and S(\cdot) include multivariate versions of trimmed means and covariance matrices. Their sample counterparts take the form

{{T}_{WD}}({{{X}} ^ {n}}) = {∑\limits_{i = 1} ^ {n} {{{d}_{i}}{{X}_{i}}}} / {∑\limits_{i = 1} ^ {n} {{{d}_{i}}}},

DIS({{{X}}^{n}}) = \frac{ ∑\limits_{i = 1} ^ {n} {{{d}_{i}}≤ft( {{{X}}_{i}} - {{T}_{WD}}({{{X}} ^ {n}}) \right){{≤ft( {{{X}}_{i}} - {{T}_{WD}}({{{X}} ^ {n}}) \right)} ^ {T}}} }{ ∑\limits_{i = 1} ^ {n} {{{d}_{i}}}},

where {{d}_{i}} are sample depth weights, {{w}_{1}}(x) = {{w}_{2}}(x) = x .

loc: Robust Estimate of Location:

cov: Robust Estimate of Covariance:

Returns depth weighted covariance matrix.

Daniel Kosiorowski and Zygmunt Zawadzki from Cracow University of Economics.

depthContour and depthPersp for depth graphics.

# EXAMPLE 1
x <- mvrnorm(n = 100, mu = c(0, 0), Sigma = 3 * diag(2))
cov_x <- CovLP(x, 2, 1, 1)

# EXAMPLE 2
data(under5.mort, inf.mort, maesles.imm)
data1990 <- na.omit(cbind(under5.mort[, 1], inf.mort[, 1], maesles.imm[, 1]))
CovLP(data1990)