Thresholding            package:waveslim            R Documentation

_W_a_v_e_l_e_t _S_h_r_i_n_k_a_g_e _v_i_a _T_h_r_e_s_h_o_l_d_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Perform wavelet shrinkage using data-analytic, hybrid SURE,
     manual, SURE, or universal thresholding.

_U_s_a_g_e:

     da.thresh(wc, alpha = .05, max.level = 4, verbose = FALSE, return.thresh = FALSE)
     hybrid.thresh(wc, max.level = 4, verbose = FALSE, seed = 0)
     manual.thresh(wc, max.level = 4, value, hard = TRUE)
     sure.thresh(wc, max.level = 4, hard = TRUE)
     universal.thresh(wc, max.level = 4, hard = TRUE)
     universal.thresh.modwt(wc, max.level = 4, hard = TRUE)

_A_r_g_u_m_e_n_t_s:

      wc: wavelet coefficients

   alpha: level of the hypothesis tests

max.level: maximum level of coefficients to be affected by threshold

 verbose: if 'verbose=TRUE' then information is printed to the screen

   value: threshold value (only utilized in 'manual.thresh')

    hard: Boolean value, if 'hard=F' then soft thresholding is used

    seed: sets random seed (only utilized in 'hybrid.thresh')

return.thresh: if 'return.thresh=TRUE' then the vector of threshold
          values is returned, otherwise the surviving wavelet
          coefficients are returned

_D_e_t_a_i_l_s:

     An extensive amount of literature has been written on wavelet
     shrinkage.  The functions here represent the most basic approaches
     to the problem of nonparametric function estimation.  See the
     references for further information.

_V_a_l_u_e:

     The default output is a list structure, the same length as was
     input, containing only those wavelet coefficients surviving the
     threshold.

_A_u_t_h_o_r(_s):

     B. Whitcher (some code taken from R. Todd Ogden)

_R_e_f_e_r_e_n_c_e_s:

     Gencay, R., F. Selcuk and B. Whitcher (2001) _An Introduction to
     Wavelets and Other Filtering Methods in Finance and Economics_,
     Academic Press.

     Ogden, R. T. (1996) _Essential Wavelets for Statistical
     Applications and Data Analysis_, Birkhauser.

     Percival, D. B. and A. T. Walden (2000) _Wavelet Methods for Time
     Series Analysis_, Cambridge University Press.

     Vidakovic, B. (1999) _Statistical Modeling by Wavelets_, John
     Wiley & Sons.

