Denoising function

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Description

Denoise algorithm for thresholding methods supplied with wavethresh.

Usage

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hfdenoise.wav(x, binsize, transform = "binhf", meth = "u", van = 1, fam = "DaubExPhase", 
min.level = 3,coarse=FALSE)

Arguments

x

vector of observed values, of length a power of two.

binsize

the binomial size of the observed values x.

transform

A Gaussianizing transform. Possible values are "binhf" or "ansc".

meth

A wavelet thresholding method. Possible values are "u" for universal thresholding, or "c" for cross-validation.

van

the number of vanishing moments of the wavelet used in the wavelet denoiser.

fam

the wavelet family used in the wavelet denoiser. Possible values are "DaubLeAsymm" and "DaubExPhase".

min.level

the primary resolution level for the wavelet transform denoiser.

coarse

Boolean variable indicating whether a "coarsening" modification should be applied. For use with the chromosome datasets.

Details

The function pre and post-processes the observed data with either Anscombe's transform or the binomial Haar-Fisz transform, using a wavelet denoiser to smooth the data, specified by the inputs min.level, van and fam combined with the thresholding rule meth.If coarse is set to true, the first finest 11 coefficient levels are set to zero, corresponding to coefficients produced from 2^11= 2048 nucleotide bases.

Value

fhat

vector corresponding to x of the estimated binomial proportion.

Note

This function requires the package wavethresh.

Author(s)

Matt Nunes (m.nunes@ucl.ac.uk)

See Also

hfdenoise

Examples

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library(wavethresh)

#create a sample intensity vector:

int<-sinlog(seq(0,1,length=256))
x<-NULL
for(i in 1:256){
x[i]<-rbinom(1,1,int[i])
}


est<-hfdenoise.wav(x,1,transform="ansc","u",6,"DaubLeAsymm",3,FALSE)