gam.selection              package:mgcv              R Documentation

_G_e_n_e_r_a_l_i_z_e_d _A_d_d_i_t_i_v_e _M_o_d_e_l _S_e_l_e_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This page is intended to provide some more information on how to
     select GAMs. Given a model structure specified by a gam model
     formula, 'gam()' attempts to find the appropriate smoothness for
     each applicable model  term using Generalized Cross Validation
     (GCV) or an Un-Biased Risk Estimator (UBRE), the latter being used
     in cases in which the scale parameter is assumed known. GCV and 
     UBRE are covered in Craven and Wahba (1979) and Wahba (1990). Fit
     method '"magic"' uses Newton or failing that steepest descent
     updates of the smoothing parameters and is particularly
     numerically robust. Fit method '"mgcv"' alternates  grid searches
     for the correct overall level of smoothness for the whole model,
     given the  relative smoothness of terms, with Newton/Steepest
     descent updates of the relative smoothness  of terms, given the
     overall amount of smoothness. 

     Automatic smoothness selection is unlikely to be successful with
     few data, particularly with multiple terms to be selected. The
     'mgcv' method can also fail to find the real minimum of the
     GCV/UBRE score if the model contains many smooth terms that should
     really be completely smooth, or close to it (e.g. a straight line 
     for a default 1-d smooth). The problem is that in this
     circumstance the optimal overall smoothness given the relative
     smoothness of terms may make all terms completely smooth - but
     this will tend to move the smoothing parameters  to a location
     where the GCV/UBRE score is nearly completely flat with respect to
     the smoothing parameters so that Newton and steepest  descent are
     both ineffective. These problems can usually be overcome by
     replacing some completely smooth terms with purely parametric
     model terms. 

     A good example of where smoothing parameter selection can
     ``fail'', but in an unimportant manner is provided by the 
     'rock.gam' example in Venables and Ripley. In this case 3
     smoothing parameters are to estimated from 48 data, which is 
     probably over-ambitious. 'gam' will estimate either 1.4 or 1
     degrees of freedom for the smooth of 'shape', depending on  the
     exact details of model specification (e.g. k value for each 's()'
     term). The lower GCV score is really at 1.4 (and if the other 2
     terms are replaced by straight lines this estimate is always
     returned), but the 'shape' term is in no way significant and the 
     lowest GCV score is obtained by removing it altogether. The
     problem here is that the GCV score contains very little
     information on the optimal  degrees of freedom to associate with a
     term that GCV would suggest should really be dropped. 

     In general the most logically consistent method to use for
     deciding which terms to include in the model is to compare
     GCV/UBRE scores for models with and without the term. More
     generally the score for the model with a smooth term can be
     compared to the score for the model with  the smooth term replaced
     by appropriate parametric terms. Candidates for removal can be
     identified by reference to the approximate p-values provided by
     'summary.gam'. Candidates for replacement by parametric terms are
     smooth terms with estimated degrees of freedom close to their 
     minimum possible.

_A_u_t_h_o_r(_s):

     Simon N. Wood simon@stats.gla.ac.uk

_R_e_f_e_r_e_n_c_e_s:

     Craven and Wahba (1979) Smoothing Noisy Data with Spline
     Functions. Numer. Math. 31:377-403

     Venables and Ripley (1999) Modern Applied Statistics with S-PLUS

     Wahba (1990) Spline Models of Observational Data. SIAM.

     Wood, S.N. (2000)  Modelling and Smoothing Parameter Estimation
     with Multiple  Quadratic Penalties. J.R.Statist.Soc.B
     62(2):413-428

     Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B
     65(1):95-114

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

