Amatdual               package:adlift               R Documentation

_A_m_a_t_d_u_a_l

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

     Combines filter matrices to produce a refinement matrix A for a
     wavelet transform.

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

     Amatdual(steps, pointsin, removelist, nbrs, weights, alpha)

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

   steps: a value indicating which refinement matrix to construct. It
          refers to the number of points already removed during the
          transform.

pointsin: The indices of gridpoints still to be removed. 

removelist: a vector of indices into env{X} of the lifted coefficients
          during the transform (in the order of removal). 

    nbrs: indices of the neighbours used in the last step of the
          decomposition.

 weights: the prediction weights obtained from the regression in the
          prediction step of the transform.

   alpha: the update weights used to update 'lengths' and 'coeff'.

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

     The function uses the prediction and update weights to construct
     the filter matrices 'Hdual' and 'Gdual'. Combining these two
     matrices results in the refinement matrix 'Adual'.

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

   Adual: the refinement matrix for the particular step of the
          transform. 

   Hdual: the high-pass filter matrix for the current step of the
          transform.

   Gdual: the low-pass filter matrix for the current step of the
          transform.

       o: the indices of 'nbrs' into the vector of 'pointsin' and the
          'steps' removed points of the transform.

   alpha: the update weights used to update 'lengths' and 'coeff'.

 weights: the prediction weights obtained from the regression in the
          prediction step of the transform.

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

     Matt Nunes (matt.nunes@bristol.ac.uk), Marina Popa
     (Marina.Popa@bristol.ac.uk)

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

     'transmatdual'

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

     #
     x<-runif(256)
     y<-make.signal2("doppler",x=x)
     a<-fwtnp(x,y,LocalPred=AdaptNeigh,neighbours=2)
     #
     A<-Amatdual(90,a$pointsin,a$removelist,a$neighbrs[[90]],a$gamlist[[90]],a$alphalist[[90]])
     #
     A$Adual
     #
     #the 90th refinement matrix for the transform above.
     #

