dwt.fn: Empirical Bayes wavelet smoothing for flashier
In DongyueXie/smashrgen: Empirical Bayes smoothers
dwt.fn R Documentation
Empirical Bayes wavelet smoothing for flashier
Description
Smooth homogeneous Gaussian data.
Usage
dwt.fn(x, s, g_init, fix_g, output)
Arguments
x
data
s
known standard error
filter.number, family
wavelet family and filter number as in 'wavethresh' package
ebnm_params
a list of 'ebnm' parameters
W
the dwt matrix for calc posterior variance. Remove the first row which is all 1/sqrt(n).
Value
a list of
mu.est:
posterior mean
mu.var:
posterior variance
loglik:
log likelihood
dKL:
KL divergence between g(the prior) and q(the posterior)
DongyueXie/smashrgen documentation built on Jan. 14, 2024, 5:30 a.m.
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| dwt.fn | R Documentation |
Empirical Bayes wavelet smoothing for flashier
Description
Smooth homogeneous Gaussian data.
Usage
dwt.fn(x, s, g_init, fix_g, output)
Arguments
x |
data |
s |
known standard error |
filter.number, family |
wavelet family and filter number as in 'wavethresh' package |
ebnm_params |
a list of 'ebnm' parameters |
W |
the dwt matrix for calc posterior variance. Remove the first row which is all 1/sqrt(n). |
Value
a list of
mu.est: |
posterior mean |
mu.var: |
posterior variance |
loglik: |
log likelihood |
dKL: |
KL divergence between g(the prior) and q(the posterior) |
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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