backfit               package:KernGPLM               R Documentation

_B_a_c_k_f_i_t_t_i_n_g _f_o_r _a_n _a_d_d_i_t_i_v_e _m_o_d_e_l

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

     Implements kernel-based backfitting in an additive model, optional
     with a partial linear term.

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

     backfit(t, y, h, x = NULL, grid = NULL, weights.conv = 1,
                offset = 0, method = "generic",
                max.iter = 50, eps.conv = 1e-04, m.start = NULL,
                kernel.p = 2, kernel.q = 2)

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

       y: n x 1 vector, responses

       t: n x q matrix, data for nonparametric part

       h: scalar or 1 x q, bandwidth(s)

       x: optional, n x p matrix, data for linear part

    grid: m x q matrix, where to calculate the nonparametric function
          (default = t)

weights.conv: weights for convergence criterion

  offset: offset

  method: one of '"generic"', '"linit"' or '"modified"'

max.iter: maximal number of iterations

eps.conv: convergence criterion

 m.start: n x q matrix, start values for m

kernel.p: integer or text, see 'kernel.function'

kernel.q: integer, see 'kernel.function'

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

     List with components: 

       c: constant

       b: p x 1 vector, linear coefficients

       m: n x q matrix, nonparametric marginal function estimates

  m.grid: m x q matrix, nonparametric marginal function estimates on
          grid

     rss: residual sum of squares

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

     Marlene Mueller

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

     'kernel.function', 'kreg'

