prcout: PCA-based outlier detection
In 3inar/nowaclean: Methods for Standardized Data Cleaning in the Norwegian Women and Cancer Group
Description
Usage
Arguments
Value
Description
Takes a matrix of samples x measurements and looks for outliers in the two
first principal components of the data as defined by mahalanobis distance
to the center of the data in number of standard deviations
Usage
1
prcout(x, prob = 0.01)
Arguments
x
a numerical matrix with samples by row, measurements by column
prob
How unlikely should a data point at least be in order to not be
considered part of the "center mass" of the data. Translated to k in
Chebyshev's inequality P(|Z| >= k) =< 1/k^2 and applied to the two
first PCs.
Value
an object of class prcout
3inar/nowaclean documentation built on May 5, 2019, 10:44 a.m.
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Description Usage Arguments Value
Description
Takes a matrix of samples x measurements and looks for outliers in the two first principal components of the data as defined by mahalanobis distance to the center of the data in number of standard deviations
Usage
1 | prcout(x, prob = 0.01)
|
Arguments
x |
a numerical matrix with samples by row, measurements by column |
prob |
How unlikely should a data point at least be in order to not be considered part of the "center mass" of the data. Translated to k in Chebyshev's inequality P(|Z| >= k) =< 1/k^2 and applied to the two first PCs. |
Value
an object of class prcout
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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