# rrest: Robust Regularized Estimator (RegMCD) for location and... In rrlda: Robust Regularized Linear Discriminant Analysis

## Description

Computes the Robust Regularized Estimator for location and inverse scatter.

## Usage

 `1` ```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

 ```1 2 3``` ``` 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.