rrest: Robust Regularized Estimator (RegMCD) for location and...

Description Usage Arguments Details Value Examples

View source: R/rrest.R

Description

Computes the Robust Regularized Estimator for location and inverse scatter.

Usage

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rrest(data, lambda=0.5, hp=0.75, thresh=0.0001, maxit=10, penalty="L2")

Arguments

data

Matrix or data.frame of observations

lambda

Penalty parameter which controls the sparseness of the resulting inverse scatter matrix. Default is 0.5

hp

Robustness parameter which specifies the amount of observations to be included in the computations. Default is 0.75

thresh

Threshold value controlling the convergence of the iterative algorithm. Default is 0.0001. In most cases this argument does not have to be supplied.

maxit

Maximum number of iterations of the algorithm. Default is 10.

penalty

Type of penalty to be applied. Possible values are "L1" and "L2".

Details

The Robust Regularized Estimator computes a sparse inverse covariance matrix of the given observations by maximization of a penalized likelihood function. The sparseness is controlled by a penalty parameter lambda. Possible outliers are dealt with by a robustness parameter alpha which specifies the amount of observations for which the likelihood function is maximized.

Value

mean

The resulting location estimate.

covi_nocons

The resulting inverse covariance estimate.

subset

An index vector specifying the data subset used (see robustness parameter alpha).

objective

The maximized objective value.

loglik

The maximized (log-)likelihood value.

niter

The number of iterations

Examples

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	x <- cbind(rnorm(100), rnorm(100), rnorm(100)) # use first group only
	rr <- rrest(x, lambda=0.2, hp=0.75)
	solve(rr$covi) ## estimated cov matrix

rrlda documentation built on May 29, 2017, 9:07 p.m.