swr                package:SpherWave                R Documentation

_S_p_h_e_r_i_c_a_l _W_a_v_e_l_e_t _R_e_c_o_n_s_t_r_u_c_t_i_o_n _o_f '_s_w_d' _O_b_j_e_c_t

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

     This function performs spherical wavelet reconstruction.

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

     swr(swd) 

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

     swd: an object of class `swd'

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

     This function performs spherical wavelet reconstruction.

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

   recon: the spherical wavelet reconstruction

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

     Oh, H-S. and Li, T-H. (2004) Estimation of global temperature
     fields from scattered observations by  a spherical-wavelet-based
     spatially adaptive method. _Journal of the Royal Statistical
     Society Ser._ B, *66*, 221-238.

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

     'sbf', 'swd', 'swthresh'

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

     ### Observations of year 1967
     #data(temperature)
     #names(temperature)

     # Temperatures on 939 weather stations of year 1967    
     #temp67 <- temperature$obs[temperature$year == 1967] 
     # Locations of 939 weather stations    
     #latlon <- temperature$latlon[temperature$year == 1967, ]

     ### Network design by BUD
     #data(netlab)

     ### Bandwidth for Poisson kernel
     #eta <- c(0.961, 0.923, 0.852, 0.723, 0.506)

     ### SBF representation of the observations by pls
     #out.pls <- sbf(obs=temp67, latlon=latlon, netlab=netlab, eta=eta, 
     #    method="pls", grid.size=c(50, 100), lambda=0.89)

     ### Decomposition
     #out.dpls <- swd(out.pls)

     ### Thresholding
     #out.univ <- swthresh(out.dpls, policy="universal", by.level=TRUE, 
     #    type="hard", nthresh=4)  

     ### Reconstruction
     #out.rec <- swr(out.univ)
     #sw.plot(z=out.rec, type="recon", xlab="", ylab="")

