bkfsmooth                package:pgam                R Documentation

_S_m_o_o_t_h_i_n_g _o_f _n_o_n_p_a_r_a_m_e_t_r_i_c _t_e_r_m_s

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

     Interface for smoothing functions

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

     bkfsmooth(y, x, df, smoother = "spline", w = rep(1, length(y)))

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

       y: dependent variable for fitting. In semiparametric models,
          this is the partial residuals of parametric fit

       x: independent variable. Univariate fit only

      df: equivalent degrees of freedom. If 'NULL' the smoothing
          parameter is selected by cross-validation

smoother: string with the name of the smoother to be used

       w: vector with the diagonal elements of the weight matrix.
          Default is a vector of 1 with the same length of y

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

     Although several smoothers can be used in semiparametric
     regression models, only natural cubic splines is intended to be
     used in Poisson-Gamma Additive Models due to its interesting
     mathematical properties.

     Nowadays, this function interfaces the 'smooth.spline' in 'stats'
     library. It will become not dependent soon enough.

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

  fitted: smoothed values

     lev: diagonal of the influence matrix

      df: degrees of freedom

_N_o_t_e:

     This function is not intended to be called directly.

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

     Washington Leite Junger wjunger@ims.uerj.br and Antonio Ponce de
     Leon ponce@ims.uerj.br

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

     Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and
     Generalized Linear Models: a roughness penalty approach. Chapman
     and Hall, London

     Hastie, T. J., Tibshirani, R. J.(1990) Generalized Additive
     Models. Chapman and Hall, London

     Junger, W. L. (2004) Modelo Poisson-Gama Semi-Paramétrico: Uma
     Abordagem de Penalização por Rugosidade. MSc Thesis. Rio de
     Janeiro, PUC-Rio, Departamento de Engenharia Elétrica

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

     'pgam', 'predict.pgam'

