gam.control               package:mgcv               R Documentation

_S_e_t_t_i_n_g _G_A_M _f_i_t_t_i_n_g _d_e_f_a_u_l_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     This is an internal function of package 'mgcv' which allows
     control of the numerical options for fitting a GAM.  Typically
     users will want to modify the defaults if model fitting fails to
     converge, or if the warnings are generated which suggest a  loss
     of numerical stability during fitting.

_U_s_a_g_e:

     gam.control(irls.reg=0.0,epsilon = 1e-04, maxit = 20,globit = 20,
                 mgcv.tol=1e-6,mgcv.half=15,nb.theta.mult=10000, trace = FALSE,
                 fit.method="magic",perf.iter=TRUE,rank.tol=.Machine$double.eps^0.5) 

_A_r_g_u_m_e_n_t_s:

irls.reg: For most models this should be 0. The iteratively re-weighted
          least squares method by which GAMs are fitted  can fail to
          converge in some circumstances. For example data with many
          zeroes can cause  problems in a model with a log link,
          because a mean of zero corresponds to an infinite range of
          linear predictor  values. Such convergence problems are
          caused by a fundamental lack of identifiability, but do not
          show up as  lack of identifiability in the penalized linear
          model problems that have to be solved at each stage of
          iteration. In such circumstances it is possible to apply a
          ridge regression penalty to the model to impose
          identifiability, and  'irls.reg' is the size of the penalty.
          The penalty can only be used if 'fit.method=="magic"'. 

 epsilon: This is used for judging conversion of the GLM IRLS loop in
          'gam.fit'.

   maxit: Maximum number of IRLS iterations to perform using cautious
          GCV/UBRE optimization, after 'globit'  IRLS iterations with
          normal GCV optimization have been performed. Note that fit
          method '"magic"' makes no distinction  between cautious and
          global optimization.

  globit: Maximum number of IRLS iterations to perform with normal
          GCV/UBRE optimization. If convergence is not achieved after
          these  iterations then a further 'maxit' iterations will be
          performed using cautious GCV/UBRE optimization. 

mgcv.tol: The convergence tolerance parameter to use in GCV/UBRE
          optimization.

mgcv.half: If a step of  the GCV/UBRE optimization method leads to a
          worse GCV/UBRE score, then the step length is halved. This is
          the number of halvings to try before giving up.

nb.theta.mult: Controls the limits on theta when negative binomial
          parameter is to be estimated. Maximum theta is set to  the
          initial value multiplied by 'nb.theta.mult', while the
          minimum value is set to the initial value divided by 
          'nb.theta.mult'. 

   trace: Set this to 'TRUE' to turn on diagnostic output.

fit.method: set to '"mgcv"' to use the method described in Wood (2000).
          Set to '"magic"' to use a  newer numerically more stable
          method, which allows regularization and mixtures of fixed and
          estimated smoothing parameters.  Set to '"fastest"' to use
          '"mgcv"' for single penalty models and '"magic"' otherwise.

perf.iter: set to 'TRUE' to use Gu's approach to finding smoothing 
          parameters in which GCV or UBRE is applied to the penalized
          linear modelling problem produced at each IRLS iteration.
          This method is very fast, but means that it  is often
          possible to find smoothing parameters yielding a slightly
          lower GCV/UBRE score by perturbing some of the estimated 
          smoothing parameters a little. Technically this occurs
          because the performance iteration effectively neglects the
          dependence of the  iterative weights on the smoothing
          parameters. Setting 'perf.iter' to 'FALSE' uses O'Sullivan's
          approach in which the IRLS is run to convergence for each
          trial set of smoothing parameters. This is much slower, since
          it uses 'nlm' with  finite differences and requires many more
          IRLS steps. 

rank.tol: The tolerance used to estimate rank when using
          'fit.method="magic"'.

_D_e_t_a_i_l_s:

     With fit method '"mgcv"',  'maxit' and 'globit' control the
     maximum iterations of the IRLS algorithm, as follows:  the
     algorithm will first execute up to 'globit' steps in which the
     GCV/UBRE algorithm performs a global search for the best overall 
     smoothing parameter at every iteration. If convergence is not
     achieved within 'globit' iterations, then a further  'maxit' steps
     are taken, in which the overall smoothing parameter estimate is
     taken as the  one locally minimising the GCV/UBRE score and
     resulting in the lowest EDF change. The difference  between the
     two phases is only significant if the GCV/UBRE function develops
     more than one minima.  The reason for this approach is that the
     GCV/UBRE score for the IRLS problem can develop `phantom' minimima
     for some models: these are minima which are not present in the
     GCV/UBRE score of the IRLS problem resulting from moving the
     parameters to the minimum! Such minima can lead to convergence 
     failures, which are usually fixed by the second phase.

_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:

     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. (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/>

_S_e_e _A_l_s_o:

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

