mvSmoothWt: Create casewise weights based on mahalanobis distances
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities
Description
Usage
Arguments
Value
Description
This uses a two-parameter generalization of Tukey's Bisquare weight
function to create non-binary weights for the rows of a multivariate data set
based on each row's mahalanobis distance.
Usage
1
mvSmoothWt(x, crit, k = 1.5)
Arguments
x
a vector of mahalanobis distances
crit
the critical value for declaring an outlier.
k
the second tuning parameter. recommended to set as 1 or 2. the default is a compromise of 1.5.
Value
a numeric vector of weights
abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.
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Description Usage Arguments Value
Description
This uses a two-parameter generalization of Tukey's Bisquare weight function to create non-binary weights for the rows of a multivariate data set based on each row's mahalanobis distance.
Usage
1 | mvSmoothWt(x, crit, k = 1.5)
|
Arguments
x |
a vector of mahalanobis distances |
crit |
the critical value for declaring an outlier. |
k |
the second tuning parameter. recommended to set as 1 or 2. the default is a compromise of 1.5. |
Value
a numeric vector of weights
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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