help mregress 
-------------------------------------------------------------------------------

Title

    mcd --        Minimum Covariance Determinant estimator of location and
                    scatter


Syntax
        mcd varlist [if] [in] [, options]

    options                 Description
    -------------------------------------------------------------------------
    Model
      e(#)                  maximal expected proportion of outliers
      proba(#)              probability of selecting at least one clean
                              sumple in the p-subset algorithm
      trim(#)               percentage of trimming
      outlier               return robust Mahalanobis distances and flag
                              outliers
      bestsampl             flag oservations used for estimating the trimmed
                              covariance matrix
      raw                   return the raw robust covariance matrix
      setseed(#)            set the seed


Description

    mcd finds the Minimum Covariance Determinant estimator of location and
    scatter.  By default, the one step reweighted MCD robust covariance
    matrix is saved in matrix covRMCD and the one step reweighted MCD robust
    location vector is saved in matrix locationRMCD.

Options

        +-------+
    ----+ Model +------------------------------------------------------------

    e(#) sets the expected percentage of outliers existing in the dataset.
        Setting it high, slows down the algorithm. It is set by default to
        0.2 but can take any value ranging from 0 to 0.5.

    proba(#) sets the probability of having at least one non-corrupt sample
        among all those considered. It is set by default to 0.99 but can take
        any value ranging from 0 to 0.9999.

    trim(#) sets the trimming. It is set by default to 0.5 but can take any
        value ranging from 0 to 0.5.

    outlier creates a dummy identifying multivariate outliers and returns
        robust distances.

    bestsample flags the subsample used for estimating the MCD location
        vector and scatter matrix.

    raw returns the genuine MCD location vector (locationMCD) and covariance
        matrix (covMCD) rather than the one step reweighted (the default).
        The reweighted location vector and covariance matrix are computed
        using classical estimators on the dataset cleaned of identified
        outliers.

    setseed(#) allows the user to set a seed. Setting the seed allows to
        replicate the results.

Examples

    Setup
        . webuse auto
    Estimate robust Mahalanobis distances
        . mcd mpg headroom trunk weight length turn displacement gear_ratio,
            outlier
    Display the robust reweighted covariance matrix and location vector
        . matrix list covRMCD
        . matrix list locationRMCD
    Same as above bust using the raw data
        . mcd mpg headroom trunk weight length turn displacement gear_ratio,
            outlier raw
    Display the robust raw covariance matrix and location vector
        . matrix list covMCD
        . matrix list locationMCD

References

    Rousseeuw, P.J. and Van Driessen, K. (1999). "A fast algorithm for the
    minimum covariance determinant estimator". Technometrics, 41, 212--223.


Also see

    Online:  [R] qreg, [R] regress;
             [R] rreg, mmregress, sregress, msregress, mregress