# Cross-Validation Wavelet Shrinkage after imputation This function performs level-dependent cross-validation wavelet shrinkage given the cross-validation scheme and imputation values. ```r cvwavelet.after.impute(y, ywd, yimpute, cv.index, cv.optlevel, cv.tol=0.1^3, cv.maxiter=100, filter.number=10, family="DaubLeAsymm", thresh.type="soft", ll=3) ``` ## Arguments - `y`: observation - `ywd`: DWT object - `yimpute`: imputed values according to cross-validation scheme - `cv.index`: test dataset index according to cross-validation scheme - `cv.optlevel`: thresholding levels - `cv.tol`: tolerance for cross-validation - `cv.maxiter`: maximum iteration for cross-validation - `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 Calculating the threshold values and reconstructing noisy data $y$, given the index of each testdata, imputed values according to cross-validation scheme and discrete wavelet transform of $y$. ## Returns Reconstruction and thresholding values by level-dependent cross-validation - **yc**: reconstruction - **cvthresh**: thresholding values by level-dependent cross-validation ## See Also `cvwavelet`, `cvtype`, `cvimpute.by.wavelet`. ## Examples ```r data(ipd) y <- as.numeric(ipd); n <- length(y); nlevel <- log2(n) set.seed(1) cv.index <- cvtype(n=n, cv.bsize=2, cv.kfold=4, cv.random=TRUE)$cv.index yimpute <- cvimpute.by.wavelet(y=y, impute.index=cv.index)$yimpute ywd <- wd(y) #out <- cvwavelet.after.impute(y=y, ywd=ywd, yimpute=yimpute, #cv.index=cv.index, cv.optlevel=c(3:(nlevel-1))) #ts.plot(ts(out$yc, start=1229.98, deltat=0.02, frequency=50), # main="Level-dependent Cross Validation", xlab = "Seconds", ylab="") ##### Specifying thresholding structure # cv.optlevel <- c(3) # Threshold (level 3 to finest level) at the same time. # cv.optlevel <- c(3, 5) # Threshold two groups of resolution levels, # (level 3, 4) and (level 5 to finest level). # cv.optlevel <- c(3,4,5,6,7,8) # Threshold each resolution level 3 to 8. ```

cvwavelet.after.impute function