Smult

Smult computes S estimators in multivariate analysis

Syntax

Description

example

out =Smult(Y) Smult with all default options.

example

out =Smult(Y, Name, Value) Smult with optional arguments.

example

[out , varargout] =Smult(___) Smult with exctracted subsamples.

Examples

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  • Smult with all default options.
  •     n=200;
        v=3;
        randn('state', 123456);
        Y=randn(n,v);
        % Contaminated data
        Ycont=Y;
        Ycont(1:5,:)=Ycont(1:5,:)+3;
        [out]=Smult(Ycont);
    

  • Smult with optional arguments.
  •     n=200;
        v=3;
        randn('state', 123456);
        Y=randn(n,v);
        % Contaminated data
        Ycont=Y;
        Ycont(1:5,:)=Ycont(1:5,:)+3;
        [out]=Smult(Ycont,'plots',1);
    
    Total estimated time to complete S estimator:  2.09 seconds 
    

  • Smult with exctracted subsamples.
  •     n=200;
        v=3;
        randn('state', 123456);
        Y=randn(n,v);
        % Contaminated data
        Ycont=Y;
        Ycont(1:5,:)=Ycont(1:5,:)+3;
        [out,C]=Smult(Ycont);
    

    Input Arguments

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    Y — Input data. Matrix.

    n x v data matrix; n observations and v variables. Rows of Y represent observations, and columns represent variables.

    Missing values (NaN's) and infinite values (Inf's) are allowed, since observations (rows) with missing or infinite values will automatically be excluded from the computations.

    Data Types: single|double

    Name-Value Pair Arguments

    Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

    Example: 'bdp',0.4 , 'nsamp',1000 , 'refsteps',0 , 'reftol',1e-8 , 'refstepsbestr',10 , 'reftolbestr',1e-10 , 'minsctol',1e-7 , 'bestr',10 , 'conflev',0.99 , 'nocheck',1 , 'plots',0 , 'msg',0 , 'ysave',1

    bdp —breakdown point.scalar.

    It measures the fraction of outliers the algorithm should resist. In this case any value greater than 0 but smaller or equal than 0.5 will do fine (default=0.5).

    Note that given bdp nominal efficiency is automatically determined.

    Example: 'bdp',0.4

    Data Types: double

    nsamp —Number of subsamples which will be extracted to find the robust estimator.scalar.

    If nsamp=0 all subsets will be extracted.

    They will be (n choose p).

    If the number of all possible subset is <1000 the default is to extract all subsets otherwise just 1000.

    Example: 'nsamp',1000

    Data Types: single | double

    refsteps —Number of refining iterations.scalar.

    Number of refining iterationsin each subsample (default = 3).

    refsteps = 0 means "raw-subsampling" without iterations.

    Example: 'refsteps',0

    Data Types: single | double

    reftol —scalar.default value of tolerance for the refining steps.

    The default value is 1e-6;

    Example: 'reftol',1e-8

    Data Types: single | double

    refstepsbestr —number of refining iterations for each best subset.scalar.

    Scalar defining number of refining iterations for each best subset (default = 50).

    Example: 'refstepsbestr',10

    Data Types: single | double

    reftolbestr —Tolerance for the refining steps.scalar.

    Tolerance for the refining steps for each of the best subsets The default value is 1e-8;

    Example: 'reftolbestr',1e-10

    Data Types: single | double

    minsctol —tolerance for the iterative procedure for finding the minimum value of the scale.scalar.

    Value of tolerance for the iterative procedure for finding the minimum value of the scale for each subset and each of the best subsets (It is used by subroutine minscale.m) The default value is 1e-7;

    Example: 'minsctol',1e-7

    Data Types: single | double

    bestr —number of "best betas" to remember.scalar.

    Scalar defining number of "best betas" to remember from the subsamples. These will be later iterated until convergence (default=5)

    Example: 'bestr',10

    Data Types: single | double

    conflev —Confidence level which is used to declare units as outliers.scalar.

    Usually conflev=0.95, 0.975 0.99 (individual alpha) or 1-0.05/n, 1-0.025/n, 1-0.01/n (simultaneous alpha).

    Default value is 0.975

    Example: 'conflev',0.99

    Data Types: double

    nocheck —Check input arguments.scalar.

    If nocheck is equal to 1 no check is performed on matrix Y. As default nocheck=0.

    Example: 'nocheck',1

    Data Types: double

    plots —Plot on the screen.scalar | structure.

    If plots is a structure or scalar equal to 1, generates:

    (1) a plot of Mahalanobis distances against index number. The confidence level used to draw the confidence bands for the MD is given by the input option conflev. If conflev is not specified a nominal 0.975 confidence interval will be used.

    (2) a scatter plot matrix with the outliers highlighted.

    If plots is a structure it may contain the following fields

    Value Description
    labeladd

    if this option is '1', the outliers in the spm are labelled with their unit row index. The default value is labeladd='', i.e. no label is added.

    nameY

    cell array of strings containing the labels of the variables. As default value, the labels which are added are Y1, ...Yv.

    Example: 'plots',0

    Data Types: single | double

    msg —Level of output to display.scalar.

    If msg==1 (default) messages are displayed on the screen about estimated time to compute the final estimator else no message is displayed on the screen

    Example: 'msg',0

    Data Types: single | double

    ysave —save input matrix Y.scalar.

    Scalar that is set to 1 to request that the data matrix Y is saved into the output structure out. This feature is meant at simplifying the use of function malindexplot.

    Default is 0, i.e. no saving is done.

    Example: 'ysave',1

    Data Types: double

    Output Arguments

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    out — description Structure

    A structure containing the following fields

    Value Description
    loc

    1 x v vector containing S estimate of location

    shape

    v x v matrix containing robust estimate of the shape matrix. Remark det|shape|=1

    scale

    scalar, robust estimate of the scale

    cov

    out.scale^2 * out.shape = robust estimate of covariance matrix

    bs

    (v+1) x 1 vector containing the units forming best subset associated with S estimate of location.

    md

    n x 1 vector containing the estimates of the robust Mahalanobis distances (in squared units)

    outliers

    A vector containing the list of the units declared as outliers using confidence level specified in input scalar conflev

    conflev

    Confidence level that was used to declare outliers

    singsub

    Number of subsets without full rank. Notice that out.singsub > 0.1*(number of subsamples) produces a warning

    weights

    n x 1 vector containing the estimates of the weights

    Y

    Data matrix Y. The field is present if option ysave is set to 1.

    class

    'Smult'

    References

    Maronna, R.A., Martin D. and Yohai V.J. (2006), Robust Statistics, Theory and Methods, Wiley, New York.

    Acknowledgements

    This function follows the lines of MATLAB/R code developed during the years by many authors.

    For more details seehttp://www.econ.kuleuven.be/public/NDBAE06/programs/ and the R library robustbasehttp://robustbase.r-forge.r-project.org// The core of these routines, e.g. the resampling approach, however, has been completely redesigned, with considerable increase of the computational performance.

    See Also

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