adjOutlyingness {robustbase} | R Documentation

## Compute (Skewness-adjusted) Multivariate Outlyingness

### Description

For an n * p data matrix (or data frame) `x`, compute the “outlyingness” of all n observations. Outlyingness here is a generalization of the Donoho-Stahel outlyingness measure, where skewness is taken into account via the medcouple, `mc()`.

### Usage

    adjOutlyingness(x, ndir = 250, clower = 4, cupper = 3,
                    alpha.cutoff = 0.75, coef = 1.5,
                    qr.tol = 1e-12, keep.tol = 1e-12,
                    only.outlyingness = FALSE, maxit.mult = max(100, p))

### Arguments

`x` | a numeric `matrix` or `data.frame`.
`ndir` | positive integer specifying the number of directions that should be searched.
`clower, cupper` | the constant to be used for the lower and upper tails, in order to transform the data towards symmetry. You can set `clower = 0, cupper = 0` to get the non-adjusted, i.e., classical (“central” or “symmetric”) outlyingness. In that case, `mc()` is not used.
`alpha.cutoff` | number in (0,1) specifying the quantiles (α, 1-α) which determine the “outlier” cutoff. The default, using quartiles, corresponds to the definition of the medcouple (`mc`), but there is no stringent reason for using the same alpha for the outlier cutoff.
`coef` | positive number specifying the factor with which the interquartile range (`IQR`) is multiplied to determine ‘boxplot hinges’-like upper and lower bounds.
`qr.tol` | positive tolerance to be used for `qr` and `solve.qr` for determining the `ndir` directions, each determined by a random sample of p (out of n) observations.
`keep.tol` | positive tolerance to determine which of the sample direction should be kept, namely only those for which ||x|| * ||B|| is larger than `keep.tol`.
`only.outlyingness` | logical indicating if the final outlier determination should be skipped. In that case, a vector is returned, see ‘Value:’ below.
`maxit.mult` | integer factor; `maxit <- maxit.mult * ndir` will determine the maximal number of direction searching iterations. May need to be increased for higher dimensional data.

### Details

FIXME: Details in the comment of the Matlab code; also in the reference(s).

The method as described can be useful as preprocessing in FASTICA (http://www.cis.hut.fi/projects/ica/fastica/; see also the R package fastICA.

### Value

If `only.outlyingness` is true, a vector `adjout`, otherwise, as by default, a list with components

`adjout` | numeric of `length(n)` giving the adjusted outlyingness of each observation.
`cutoff` | cutoff for “outlier” with respect to the adjusted outlyingnesses, and depending on `alpha.cutoff`.
`nonOut` | logical of `length(n)`, `TRUE` when the corresponding observation is non-outlying with respect to the cutoff and the adjusted outlyingnesses.

### Note

The result is random as it depends on the sample of `ndir` directions chosen.

Till Aug/Oct. 2014, the default values for `clower` and `cupper` were accidentally reversed, and the signs inside `exp(.)` where swapped in the (now corrected) two expressions

     tup <- Q3 + coef * IQR * exp(.... + clower * tmc * (tmc < 0))
     tlo <- Q1 - coef * IQR * exp(.... - cupper * tmc * (tmc < 0))

already in the code from Antwerpen (‘mcrsoft/adjoutlingness.R’), contrary to the published reference.

Further, the original algorithm had not been scale-equivariant in the direction construction, which has been amended in 2014-10 as well.

The results, including diagnosed outliers, therefore have changed, typically slightly, since robustbase version 0.92-0.

### Author(s)

Guy Brys; help page and improvements by Martin Maechler

### References

Brys, G., Hubert, M., and Rousseeuw, P.J. (2005) A Robustification of Independent Component Analysis; Journal of Chemometrics, 19, 1–12.

For the up-to-date reference, please consult http://wis.kuleuven.be/stat/robust

### See Also

the adjusted boxplot, `adjbox` and the medcouple, `mc`.

### Examples

    ## An Example with bad condition number and "border case" outliers

    dim(longley)
    set.seed(1) ## result is random!
    ao1 <- adjOutlyingness(longley)
    ## which are outlying ?
    which(!ao1$nonOut) ## one: "1948" - for this seed! (often: none)
    stopifnot(all(ao1$nonOut[-2]))

    ## An Example with outliers :

    dim(hbk)
    set.seed(1)
    ao.hbk <- adjOutlyingness(hbk)
    str(ao.hbk)
    hist(ao.hbk $adjout)## really two groups
    table(ao.hbk$nonOut)## 14 outliers, 61 non-outliers:
    ## outliers are :
    which(! ao.hbk$nonOut) # 1 .. 14   --- but not for all random seeds!

    ## here, they are the same as found by (much faster) MCD:
    cc <- covMcd(hbk)
    stopifnot(all(cc$mcd.wt == ao.hbk$nonOut))

    ## This is revealing (about 1--2 cases, where outliers are *not* == 1:14
    ##  but needs almost 1 [sec] per call:
    if(interactive()) {
      for(i in 1:30) {
        print(system.time(ao.hbk <- adjOutlyingness(hbk)))
        if(!identical(iout <- which(!ao.hbk$nonOut), 1:14)) {
    	 cat("Outliers:\n"); print(iout)
        }
      }
    }

    ## "Central" outlyingness: *not* calling mc()  anymore, since 2014-12-11:
    trace(mc)
    out <- capture.output(
      oo <- adjOutlyingness(hbk, clower=0, cupper=0)
    )
    untrace(mc)
    stopifnot(length(out) == 0)

[Package robustbase version 0.92-7 Index]