gam.method               package:mgcv               R Documentation

_S_e_t_t_i_n_g _G_A_M _f_i_t_t_i_n_g _m_e_t_h_o_d

_D_e_s_c_r_i_p_t_i_o_n:

     This is a function of package 'mgcv' which allows  selection of
     the numerical method used to optimize the  smoothing parameter 
     estimation criterion for a gam. It is used to set argument
     'method' of 'gam'.

_U_s_a_g_e:

     gam.method(gam="outer",outer="newton",gcv="deviance",
                family=NULL)

_A_r_g_u_m_e_n_t_s:

     gam: Which method to use in the generalized case (i.e. all case
          other than gaussian with identity link). '"perf"' for the
          performance iteration (see details) with 'magic' as the basic
          estimation engine. '"perf.outer"' for 'magic' based
          performance iteration followed by outer iteration (see
          details). '"outer"' for pure outer iteration.

   outer: The optimization approach to use to optimize log smoothing
          parameters by outer iteration.'"newton"' (default) for
          modified Newton method backed up by steepest descent, based
          on exact first and second derivatives. '"bfgs"' for a hybrid
          Newton- Quasi-Newton approach which can be faster for models
          with many smoothing parameters. '"nlm"' to use  'nlm' with
          exact first derivatives to optimize the smoothness selection
          criterion. '"nlm.fd"' to use 'nlm' with finite differenced
          first derivatives (slower and less reliable). '"optim"' to
          use the '"L-BFGS-B"' quasi-Newton method option of routine
          'optim', with exact first derivatives.

     gcv: One of '"deviance"' or '"GACV"', specifying the flavour of
          GCV to use with outer iteration. '"deviance"' simply replaces
          the residual sum of squares term in a GCV score with the
          deviance, following Hastie and Tibshirani (1990, section
          6.9). '"GACV"' (only available with outer method '"newton"') 
          uses a varient of Gu and Xiang's (2001) generalized
          approximate cross validation, modified to deal with arbitrary
          link-error combinations (see Wood, 2008).  

  family: The routine is called by 'gam' to check the supplied method
          argument. In this circumstance the family argument is passed,
          to check that it works with the specified method. 

_D_e_t_a_i_l_s:

     The default methods used by 'gam' are based on Newton type
     optimization of GCV/UBRE/AIC scores with respect to smoothing
     parameters, as described in Wood (2004)  and Wood (2008) for the
     additive and generalized additive model cases respectively.  The
     smoothing criteria (GCV etc) are evaluated for the fitted model
     itself (rather than some working  approximate model). Since this
     involves optimizing the criteria `outside' the PIRLS or LS method
     used  for fitting, it is referred to as `outer' iteration. 

     In the generalized case several alternative optimisation methods
     can be used for outer optimization. Usually the fastest and most
     reliable approach is to use a modified Newton optimizer with exact
     first and second derivatives, and this is the default. However if
     there are large numbers of smoothing parameters then it can be
     faster to use a hybrid Newton-BFGS quasi-Newton approach, which
     avoids the expense of  frequent full second derivative evaluation.

     'nlm' can be used with finite differenced first derivatives. This
     is not ideal theoretically, since  it is possible for the finite
     difference estimates of derivatives to be very badly in error on
     rare occasions when the P-IRLS convergence tolerance is close to
     being matched exactly, so that two components of a finite
     differenced derivative require different numbers of iterations of
     P-IRLS in their evaluation. An alternative is provided in which
     'nlm' uses numerically exact first derivatives, this is faster and
     less problematic than the other scheme. A further alternative is
     to use a quasi-Newton scheme with exact derivtives, based on
     'optim'. In practice this usually seems to be slower than the
     'nlm' method. 

     The alternative approach of `performance oriented iteration' was
     suggested by Gu  (and is rather similar to the PQL method in
     generalized linear mixed modelling). At each step of the P-IRLS
     (penalized iteratively reweighted least squares) iteration,  by
     which a gam is fitted, the smoothing parameters are estimated by
     GCV or UBRE applied to the working penalized linear modelling
     problem. In most cases, this process converges and gives
     smoothness estimates that perform well (see e.g. Wood 2004). 

     The performance iteration has two disadvantages. (i) in the
     presence of co-linearity or concurvity (a frequent problem when
     spatial smoothers are included in a model with other covariates)
     then the process can fail to converge. Suppose we start with some
     coefficient and smoothing parameter estimates, implying a working
     penalized linear model: the optimal smoothing parameters and
     coefficients for this working model may in turn imply a working 
     model for which the original estimates are better than the most
     recent estimates. This sort of effect can prevent convergence.

     Secondly it is often possible to find a set of smoothing
     parameters that result in a lower GCV or UBRE score, for the final
     working model, than the final score that results from the
     performance iterations. This is because the performance iteration
     is only approximately optimizing this score (since optimization is
     only performed on the working model). The disadvantage here is not
     that the model with lower score would perform better (it usually
     doesn't), but rather that it makes model comparison on the basis
     of GCV/UBRE score rather difficult. 

     In summary: performance iteration can fail to converge. It may
     occasionally be  faster than outer iteration, but since the Wood
     (2008) method `outer' iteration  is faster in most cases.

_A_u_t_h_o_r(_s):

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

_R_e_f_e_r_e_n_c_e_s:

     Gu and Wahba (1991) Minimizing GCV/GML scores with multiple
     smoothing parameters via the Newton method. SIAM J. Sci. Statist.
     Comput. 12:383-398

     Wood, S.N. (2004) Stable and efficient multiple smoothing
     parameter estimation for generalized additive models. J. Amer.
     Statist. Ass.

     Wood, S.N. (2008) Fast stable direct fitting and smoothness
     selection for generalized additive models. J.R.Statist.Soc.B
     70(3):495-518

     <URL: http://www.maths.bath.ac.uk/~sw283/>

_S_e_e _A_l_s_o:

     'gam.control'   'gam', 'gam.fit', 'glm.control'

