# rrest

From rrlda v1.1
0th

Percentile

##### Robust Regularized Estimator (RegMCD) for location and inverse scatter

Computes the Robust Regularized Estimator for location and inverse scatter.

Keywords
file
##### Usage
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

• rrest
##### Examples
	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

Documentation reproduced from package rrlda, version 1.1, License: GPL (>= 3)

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