GetRSSWST             package:wavethresh             R Documentation

_C_o_m_p_u_t_e_s _e_s_t_i_m_a_t_e _o_f _e_r_r_o_r _f_o_r _f_u_n_c_t_i_o_n _e_s_t_i_m_a_t_e.

_D_e_s_c_r_i_p_t_i_o_n:

     Computes estimate of error for function estimate. Given noisy data
     and a threshold value this function uses Nason's 1996 two-fold
     cross-validation algorithm, but using packet ordered non-decimated
     wavelet transforms to compute two estimates of an underlying
     ``true'' function and uses them to compute an estimate of the
     error in estimating the truth.

_U_s_a_g_e:

     GetRSSWST(ndata, threshold, levels, family = "DaubLeAsymm", 
             filter.number = 10, type = "soft", norm = l2norm, verbose = 0, 
             InverseType = "average")

_A_r_g_u_m_e_n_t_s:

   ndata: the noisy data. This is a vector containing the signal plus
          noise. The length of this vector should be a power of two.

threshold: the value of the threshold that you wish to compute the
          error of the estimate at

  levels: the levels over which you wish the threshold value to be
          computed (the threshold that is used in computing the
          estimate and error in the estimate). See the explanation for
          this argument in the 'threshold.wst' function. 

  family: specifies the family of wavelets that you want to use. The
          options are "DaubExPhase" and "DaubLeAsymm".

filter.number: This selects the smoothness of wavelet that you want to
          use in the decomposition. By default this is 10, the
          Daubechies least-asymmetric orthonormal compactly supported
          wavelet with 10 vanishing moments.

    type: whether to use hard or soft thresholding. See the explanation
          for this argument in the 'threshold.wst' function.

    norm: which measure of distance to judge the dissimilarity between
          the estimates. The functions 'l2norm' and 'linfnorm' are
          suitable examples.

 verbose: If 'TRUE' then informative messages are printed during the
          progression of the function, otherwise they are not.

InverseType: The possible options are "average" or "minent". The former
          uses basis averaging to form estimates of the unknown
          function. The "minent" function selects a basis using the
          Coifman and Wickerhauser, 1992 algorithm to select a basis to
          invert.

_D_e_t_a_i_l_s:

     This function implements the component of the cross-validation
     method detailed by Nason, 1996 for computing an estimate of the
     error between an estimate and the ``truth''. The difference here
     is that it uses the packet ordered non-decimated wavelet transform
     rather than the standard Mallat 'wd' discrete wavelet transform.
     As such it is an example of the translation-invariant denoising of
     Coifman and Donoho, 1995 but uses cross-validation to choose the
     threshold rather than SUREshrink. 

     Note that the procedure outlined above can use 'AvBasis' basis
     averaging or basis selection and inversion using the Coifman and
     Wickerhauser, 1992 best-basis algorithm

_V_a_l_u_e:

     A real number which is estimate of the error between estimate and
     truth at the given threshold.

_R_E_L_E_A_S_E:

     Version 3.6 Copyright Guy Nason 1995

_S_e_e _A_l_s_o:

     'linfnorm', 'linfnorm', 'wstCV', 'wstCVl'.

_E_x_a_m_p_l_e_s:

     #
     # This function performs the error estimation step for the
     # this-is-escaped-codenormal-bracket52bracket-normal function and so is not intended for
     # user use. 
     #

