bw.logG: Bandwidth estimation for strictly positive distributions.
In logKDE: Computing Log-Transformed Kernel Density Estimates for Positive Data
Description
Usage
Arguments
Value
References
Examples
Description
Computes bandwidth for log domain KDE using the Silverman rule.
Usage
1
bw.logG(x)
Arguments
x
numeric vector of the data. Must be strictly positive, will be log transformed during estimation.
Value
bw the optimal bandwidth.
References
Silverman, B. W. (1986). Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability. 26.
Wand, M. P., Marron, J. S., & Ruppert, D. (1991). Transformations in density estimation. Journal of the American Statistical Association, 86(414), 343-353.
Examples
1
logKDE documentation built on May 1, 2019, 8:40 p.m.
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Description Usage Arguments Value References Examples
Description
Computes bandwidth for log domain KDE using the Silverman rule.
Usage
1 | bw.logG(x)
|
Arguments
x |
numeric vector of the data. Must be strictly positive, will be log transformed during estimation. |
Value
bw the optimal bandwidth.
References
Silverman, B. W. (1986). Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability. 26.
Wand, M. P., Marron, J. S., & Ruppert, D. (1991). Transformations in density estimation. Journal of the American Statistical Association, 86(414), 343-353.
Examples
1 |
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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