OutlierSign1: Outlier identification in high dimensions using the SIGN1...
In rrcovHD: Robust Multivariate Methods for High Dimensional Data

Description Usage Arguments Details Value Author(s) References See Also Examples

Fast algorithm for identifying multivariate outliers in high-dimensional and/or large datasets, using spatial signs, see Filzmoser, Maronna, and Werner (CSDA, 2007). The computation of the distances is based on Mahalanobis distances.

    OutlierSign1(x, ...)
    ## Default S3 method:
OutlierSign1(x, grouping, qcrit = 0.975, trace=FALSE, ...)
    ## S3 method for class 'formula'
OutlierSign1(formula, data, ..., subset, na.action)

`formula`	a formula with no response variable, referring only to numeric variables.
`data`	an optional data frame (or similar: see `model.frame`) containing the variables in the formula `formula`.
`subset`	an optional vector used to select rows (observations) of the data matrix `x`.
`na.action`	a function which indicates what should happen when the data contain `NA`s. The default is set by the `na.action` setting of `options`, and is `na.fail` if that is unset. The default is `na.omit`.
`...`	arguments passed to or from other methods.
`x`	a matrix or data frame.
`grouping`	grouping variable: a factor specifying the class for each observation.
`qcrit`	a numeric value between 0 and 1 indicating the quantile to be used as critical value for outlier detection (default to 0.975).
`trace`	whether to print intermediate results. Default is `trace = FALSE`

Based on the robustly sphered and normed data, robust principal components are computed. These are used for computing the covariance matrix which is the basis for Mahalanobis distances. A critical value from the chi-square distribution is then used as outlier cutoff.

An S4 object of class OutlierSign1 which is a subclass of the virtual class Outlier.

Valentin Todorov valentin.todorov@chello.at

P. Filzmoser, R. Maronna and M. Werner (2008). Outlier identification in high dimensions, Computational Statistics & Data Analysis, Vol. 52 1694–1711.

Filzmoser P & Todorov V (2013). Robust tools for the imperfect world, Information Sciences 245, 4–20. doi: 10.1016/j.ins.2012.10.017.

OutlierSign1, OutlierSign2, Outlier

data(hemophilia)
obj <- OutlierSign1(gr~.,data=hemophilia)
obj

getDistance(obj)            # returns an array of distances
getClassLabels(obj, 1)      # returns an array of indices for a given class
getCutoff(obj)              # returns an array of cutoff values (for each class, usually equal)
getFlag(obj)                #  returns an 0/1 array of flags
plot(obj, class=2)          # standard plot function