gam.fit2                package:mgcv                R Documentation

_P-_I_R_L_S _G_A_M _e_s_t_i_m_a_t_i_o_n _w_i_t_h _G_C_V & _U_B_R_E _d_e_r_i_v_a_t_i_v_e _c_a_l_c_u_l_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Estimation of GAM smoothing parameters is most stable if
     optimization of the UBRE or GCV score is outer to the penalized
     iteratively re-weighted least squares scheme used to estimate the
     model given smoothing  parameters.

     This routine estimates a GAM given log smoothing paramaters, and
     evaluates the first derivative of the GCV and UBRE scores of the
     model with respect to the log smoothing parameters. Calculation of
     exact derivatives is generally faster than approximating them by
     finite differencing, as well as generally improving the
     reliability of GCV/UBRE score minimization.

     Not normally called directly, but rather a service routine for
     'gam'.

_U_s_a_g_e:

     gam.fit2(x, y, sp, S=list(),rS=list(),off, H=NULL, 
              weights = rep(1, nobs), start = NULL, etastart = NULL, 
              mustart = NULL, offset = rep(0, nobs), family = gaussian(), 
              control = gam.control(), intercept = TRUE,deriv=TRUE,
              gamma=1,scale=1,pearson=FALSE)

_A_r_g_u_m_e_n_t_s:

       x: The model matrix for the GAM.

       y: The response variable.

      sp: The log smoothing parameters.

       S: A list of penalty matrices. Typically penalty matrices
          contain only a smallish square sub-matrix which is non-zero:
          this is what is actually stored. 'off[i]' indicates which
          parameter is the first one penalized by 'S[[i]]'.

      rS: List of square roots of penalty matrices, each having as few
          columns as possible, but as many rows as there are
          parameters.

     off: 'off[i]' indicates which parameter 'S[[i]][1,1]' relates to.

       H: The fixed penalty matrix for the model.

 weights: prior weights for fitting.

   start: optional starting parameter guesses.

etastart: optional starting values for the linear predictor.

 mustart: optional starting values for the mean.

  offset: the model offset

  family: the family - actually this routine would never be called with
          'gaussian()'

 control: control list as returned from 'glm.control'

intercept: does the model have and intercept, 'TRUE' or 'FALSE'

   deriv: Should derivatives of the GCV and UBRE scores be calculated?
          'TRUE' or 'FALSE'

   gamma: The weight given to each degree of freedom in the GCV and
          UBRE scores can be varied (usually increased) using this
          parameter.

   scale: The scale parameter - needed for the UBRE score.

 pearson: The GCV/UBRE score can be based either on the Pearson
          statistic or the deviance. The latter is generally to be
          preferred, as it is less prone to severe undersmoothing.

_D_e_t_a_i_l_s:

     This routine is basically 'glm.fit' with some modifications to
     allow (i) for quadratic penalties on the log likelihood; (ii)
     derivatives of the model coefficients with respect to log
     smoothing parameters to be obtained (by updating alongside the
     P-IRLS iteration) and (iii) derivatives of the GAM GCV and UBRE
     scores to be evaluated at convergence. 

     In addition the routine applies step halving to any step that
     increases the penalized deviance substantially.

     The most costly parts of the calculation are performed by calls to
     compiled C code (which in turn calls LAPACK routines) in place of
     the compiled code that would usually perform least squares
     estimation on the working model in the IRLS iteration. 

     Estimation of smoothing parameters by optimizing GCV scores
     obtained at convergence of the P-IRLS iteration was proposed by
     O'Sullivan et al. (1986), and is here termed `outer' iteration.

_A_u_t_h_o_r(_s):

     Simon N. Wood simon.wood@r-project.org

     The routine has been modified from 'glm.fit' in R 2.0.1, written
     by the R core (see 'glm.fit' for further credits).

_R_e_f_e_r_e_n_c_e_s:

     O 'Sullivan, Yandall & Raynor (1986) Automatic smoothing of
     regression functions in generalized linear models. J. Amer.
     Statist. Assoc. 81:96-103.

     <URL: http://www.stats.gla.ac.uk/~simon/>

_S_e_e _A_l_s_o:

     'gam.fit',  'gam', 'mgcv', 'magic'

