outs.robdist: Determine the robust distance outliers
In muschellij2/clever: Calculate Leverage of Component Models
Description
Usage
Arguments
Value
Description
Identify outliers based on leverage values above a multiple of the median.
Usage
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outs.robdist(
rbd,
Fparam_1,
Fparam_2,
inMCD,
outMCD_scale,
quantile_cutoff = 0.9999
)
Arguments
rbd
The robust distance values
Fparam_1, Fparam_2
the F distribution degrees of freedom
inMCD
Logical vector indicating in-MCD observations
outMCD_scale
The scale for out-of-MCD observations
quantile_cutoff
The F-distribution quantile cutoff. Default:
.9999
Value
List with entries "cut" (the robust distance cutoff value for
scaled out-of-MCD observations) and "flag" (logical vector
indicating the outliers)
muschellij2/clever documentation built on Sept. 26, 2020, 3:54 p.m.
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Description Usage Arguments Value
Description
Identify outliers based on leverage values above a multiple of the median.
Usage
1 2 3 4 5 6 7 8 | outs.robdist(
rbd,
Fparam_1,
Fparam_2,
inMCD,
outMCD_scale,
quantile_cutoff = 0.9999
)
|
Arguments
rbd |
The robust distance values |
Fparam_1, Fparam_2 |
the F distribution degrees of freedom |
inMCD |
Logical vector indicating in-MCD observations |
outMCD_scale |
The scale for out-of-MCD observations |
quantile_cutoff |
The F-distribution quantile cutoff. Default:
|
Value
List with entries "cut" (the robust distance cutoff value for
scaled out-of-MCD observations) and "flag" (logical vector
indicating the outliers)
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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