# outlierFunctions: outliers detection functions In QUALIFIER: Quality Control of Gated Flow Cytometry Experiments

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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 <[email protected]>

`qaCheck`,`qaReport`