betashrink: Wavelet shrinkage under beta prior.
In Alexestat/bayesShrink: Bayesian wavelet shrinkage
Description
Usage
Arguments
Value
Examples
Description
Performs bayesian shrinkage under beta prior on empirical wavelet coefficients.
Usage
1
betashrink(d, alpha, a, b, m, s)
Arguments
d
The empirical wavelet coefficients vector.
alpha
The weight of the point mass at zero function of the prior.
a
The shape parameter of the beta prior.
b
The shape parameter of the beta prior.
m
The upper value of the beta prior support.
s
The standard deviation of the normal random noise.
Value
The shrunk wavelet coefficients vector.
Examples
1
betashrink(c(0.5,1,2),0.9,2,3,10,1)
Alexestat/bayesShrink documentation built on Oct. 6, 2020, 12:42 a.m.
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Description Usage Arguments Value Examples
Description
Performs bayesian shrinkage under beta prior on empirical wavelet coefficients.
Usage
1 | betashrink(d, alpha, a, b, m, s)
|
Arguments
d |
The empirical wavelet coefficients vector. |
alpha |
The weight of the point mass at zero function of the prior. |
a |
The shape parameter of the beta prior. |
b |
The shape parameter of the beta prior. |
m |
The upper value of the beta prior support. |
s |
The standard deviation of the normal random noise. |
Value
The shrunk wavelet coefficients vector.
Examples
1 | betashrink(c(0.5,1,2),0.9,2,3,10,1)
|
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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