cvwavelet {CVThresh}

Wavelet reconstruction by level-dependent Cross-Validation

Description

This function reconstructs the noise data by level-dependent cross-validation wavelet shrinkage.

Usage

cvwavelet(y=y, ywd=ywd, cv.optlevel, cv.bsize=1, cv.kfold, 
    cv.random=TRUE, cv.tol=0.1^3, cv.maxiter=100,
    impute.vscale="independent", impute.tol=0.1^3, impute.maxiter=100,
    filter.number=10, family="DaubLeAsymm", thresh.type ="soft", ll=3)

Arguments

y

observation

ywd

DWT object

cv.optlevel

thresholding levels

cv.bsize

block size of cross-validation

cv.kfold

the number of fold of cross-validation

cv.random

whether or not random cross-validation scheme should be used. Set cv.random=TRUE for random cross-validation scheme

cv.tol

tolerance for cross-validation

cv.maxiter

maximum iteration for cross-validation

impute.vscale

specifies whether variance is adjusted level-by-level or not. “level" or “independent"

impute.tol

tolerance for imputation

impute.maxiter

maximum iteration for imputation

filter.number

specifies the smoothness of wavelet in the decomposition (argument of WaveThresh)

family

specifies the family of wavelets “DaubExPhase" or “DaubLeAsymm" (argument of WaveThresh)

thresh.type

specifies the type of thresholding “hard" or “soft" (argument of WaveThresh)

ll

specifies the lowest level to be thresholded

Details

This function performs level-dependent cross-validation wavelet shrinkage.

Value

y

observations

yimpute

imputed values by provided cross-validation scheme

yc

reconstruction by level-dependent cross-validation wavelet shrinkage

cvthresh

threshold values by level-dependent cross-validation

See Also

cvtype, cvimpute.by.wavelet, cvwavelet.after.impute.

Examples

data(ipd)
y <- as.numeric(ipd); n <- length(y); nlevel <- log2(n)
ywd <- wd(y)
#out <- cvwavelet(y=y, ywd=ywd, cv.optlevel=c(3:(nlevel-1)), 
#                     cv.bsize=2, cv.kfold=4)

#ts.plot(ts(out$yc, start=1229.98, deltat=0.02, frequency=50),
#   main="Level-dependent Cross Validation", xlab = "Seconds", ylab="")