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.  To change the default
     choise of fitting method, see 'gam.method'.

_U_s_a_g_e:

     gam.control(irls.reg=0.0,epsilon = 1e-06, maxit = 100,globit = 20,
                 mgcv.tol=1e-7,mgcv.half=15,nb.theta.mult=10000, trace = FALSE,
                 rank.tol=.Machine$double.eps^0.5,absorb.cons=TRUE,
                 max.tprs.knots=5000,nlm=list(),optim=list()) 

_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 not be used if the underlying fitting method
          is 'mgcv' (not the default - see 'gam.method' for details). 

 epsilon: This is used for judging conversion of the GLM IRLS loop in
          'gam.fit' or 'gam.fit2' .

   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 only
          fitting based on 'mgcv' (not default) makes  any 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.

rank.tol: The tolerance used to estimate the rank of the fitting
          problem, for methods which deal with rank deficient cases
          (basically all except those based on 'mgcv').

absorb.cons: If 'TRUE' then the GAM is set up using a parameterization
          which requires no further constraint. Usually this means that
          all the smooths are automatically centered (i.e. they sum to
          zero over the covariate values). If 'FALSE' then the ordinary
          parameterizations of the smooths are used, which require
          constraints to be imposed during fitting.

max.tprs.knots: This is the default initial maximum number of knots to
          allow when constructing a t.p.r.s bases ('bs="tp"'). The set
          up cost (and storage) for these smooths scales as the square
          of the number of initial knots, so if it's too high you can
          appear to freeze R. Usually one would want to use an
          alternative smoothing basis (or 'te' terms), or the approach
          illustrated in the examples in 'gam', rather than simply
          increasing this default.

     nlm: list of control parameters to pass to 'nlm' if this is used
          for outer estimation of smoothing parameters. See details.

   optim: list of control parameters to pass to 'optim' if this is used
          for outer estimation of smoothin parameters. See details.

_D_e_t_a_i_l_s:

     When outer iteration is used for fitting then the control list
     'nlm' stores control arguments for calls to routine 'nlm'. The
     list has the following named elements: (i) 'ndigit' is the number
     of significant digits in the GCV/UBRE score - by default this is
     worked out from 'epsilon'; (ii) 'gradtol' is the tolerance used to
     judge convergence of the gradient of the GCV/UBRE score to zero -
     by default set to '100*epsilon'; (iii) 'stepmax' is the maximum
     allowable log smoothing parameter step - defaults to 2; (iv)
     'steptol' is the minimum allowable step length - defaults to 1e-4;
     (v) 'iterlim' is the maximum number of optimization steps allowed
     - defaults to 200; (vi) 'check.analyticals' indicates whether the
     built in exact derivative calculations should be checked
     numerically - defaults to 'FALSE'. Any of these which are not
     supplied and named in the list are set to their default values.

     Outer iteration using 'optim' is controlled using list 'optim',
     which currently has one element: 'factr' which takes default value
     1e7.

     When fitting is been done by calls to routine '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.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. (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

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

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

_S_e_e _A_l_s_o:

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

