scale                 package:waved                 R Documentation

_E_s_t_i_m_a_t_e_s _s_t_a_n_d_a_r_d _d_e_v_i_a_t_i_o_n _o_f _n_o_i_s_e

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

     Estimates standard deviation of noise in the nonparametric 
     signal+(Gaussian noise) regression  model. Input vector must be of
     dyadic length and assumes a regular grid.

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

     scale(yobs, L=3, deg=3)

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

    yobs: a vector of dyadic length representing signal+(Gaussian
          noise)

       L: lowest resolution level

     deg: degree of Meyer wavelet

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

     Returns a positive  estimate  of the standard deviation of noise
     in the nonparametric  regression  model.

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

     Marc Raimondo

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

     Raimondo, M. and Stewart, M. (2006), `The WaveD Transform in R',
     preprint, School and Mathematics and Statistics, University of
     Sydney.

_S_e_e _A_l_s_o:

     'WaveD'

_E_x_a_m_p_l_e_s:

     library(waved)
     data=waved.example(TRUE,FALSE)
     scale(data$lidar.noisy)

