proportion.outliers.robust {QUALIFIER} | R Documentation |
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.
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)
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 |
These different outlier detection functions are used together with qaCheck method to perform outlier checks.
a logical vector with the same length of input vector,indicating whether each entry of the input is a outlier.
Mike Jiang,Greg Finak
Maintainer: Mike Jiang <wjiang2@fhcrc.org>