# 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

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 <wjiang2@fhcrc.org>

### See Also

`qaCheck`

,`qaReport`