proportion.outliers.robust {QUALIFIER} | R Documentation

## outliers detection functions

### Description

Distribution based outlier detection functions.

qoutlier is IQR based outlier detection.

outlier.norm is based on normal distribution using Huber M-estimator of location with MAD scale

outlier.t is based on t-distribution.

outlier.cutoff is a simple cutoff-based outlier detection.

### Usage

    proportion.outliers.robust(x, alpha = 0.01, isUpper = TRUE,
      isLower = TRUE)

    proportion.outliers.mle(x, alpha = 0.01, isUpper = TRUE, isLower = TRUE)

    qoutlier(x, alpha = 1.5, isUpper = TRUE, isLower = TRUE, plot = FALSE,
      ...)

    outlier.norm(x, alpha = 0.01, z.cutoff = NULL, isUpper = TRUE,
      isLower = TRUE, plot = FALSE)

    outlier.t(x, alpha = 0.01, z.cutoff = NULL, isUpper = TRUE,
      isLower = TRUE, plot = FALSE)

    outlier.cutoff(x, lBound = NULL, uBound = NULL)

### Arguments

`x` | An integer/numeric vector used as the input
`alpha,z.cutoff` | alpha is the percentage of the standard deviation from the center of the data. z.cutoff is the standardized z-score value. They are used as the distribution based thresholds.
`isUpper,isLower` | logical scalars indicating whether the outliers are checked at upper or lower side of the distribution.
`plot` | logical scalar indicating whether to visualize the outlier detection results.
`...` | other arguments to be passed to qoutlier function,currently it is ignored.
`lBound,uBound` | Numeric scalars used as cutoff threshold for either lower limit or upper limit

### Details

These different outlier detection functions are used together with qaCheck method to perform outlier checks.

### Value

a logical vector with the same length of input vector,indicating whether each entry of the input is a outlier.

### Author(s)

Mike Jiang,Greg Finak

Maintainer: Mike Jiang <wjiang2@fhcrc.org>

### See Also

`qaCheck`,`qaReport`

[Package QUALIFIER version 1.28.0 Index]