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' arguments 'method' and
     'optimizer'.

_U_s_a_g_e:

     gam.control(irls.reg=0.0,epsilon = 1e-06, maxit = 100,
                 mgcv.tol=1e-7,mgcv.half=15, trace = FALSE,
                 rank.tol=.Machine$double.eps^0.5,
                 nlm=list(),optim=list(),newton=list(),
                 outerPIsteps=1,idLinksBases=TRUE,scalePenalty=TRUE,
                 keepData=FALSE) 

_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. 

 epsilon: This is used for judging conversion of the GLM IRLS loop in
          'gam.fit' or 'gam.fit3'.

   maxit: Maximum number of IRLS iterations to perform.

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.

   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').

     nlm: list of control parameters to pass to 'nlm' if this is used
          for outer estimation of smoothing parameters (not default).
          See details.

   optim: list of control parameters to pass to 'optim' if this is used
          for outer estimation of smoothing parameters (not default).
          See details.

  newton: list of control parameters to pass to default Newton
          optimizer used for outer estimation of log smoothing
          parameters. See details.

outerPIsteps: The number of performance interation steps used to
          initialize outer iteration. Less than 1 means that only one
          performance iteration step is taken to get the function
          scale, but the corresponding smoothing parameter estimates
          are discarded. 

idLinksBases: If smooth terms have their smoothing parameters linked
          via  the 'id' mechanism (see 's'), should they also have the
          same  bases. Set this to 'FALSE' only if you are sure you
          know what you are doing  (you should almost surely set
          'scalePenalty' to 'FALSE' as well in this  case).

scalePenalty: 'gamm' is somewhat sensitive to the absolute scaling  of
          the penalty matrices of a smooth relative to its model
          matrix. This option rescales  the penalty matrices to
          accomodate this problem. Probably should be set to 'FALSE' 
          if you are linking smoothing parameters but have set
          'idLinkBases' to 'FALSE'.

keepData: Should a copy of the original 'data' argument be kept in the
          'gam'  object? Strict compatibility with class 'glm' would
          keep it, but it wastes space to do so. 

_D_e_t_a_i_l_s:

     Outer iteration using 'newton' is controlled by the list 'newton'
     with the following elements: 'conv.tol' (default 1e-6) is the
     relative convergence tolerance; 'maxNstep' is the maximum length
     allowed for an element of the Newton search direction (default 5);
     'maxSstep' is the maximum length allowed for an element of the
     steepest descent direction (only used if Newton fails - default
     2); 'maxHalf' is the maximum number of step halvings to permit
     before giving up (default 30).

     If outer iteration using 'nlm' 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.

_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:

     Wood, S.N. (2004) Stable and efficient multiple smoothing
     parameter estimation for generalized additive models. J. Amer.
     Statist. Ass.99:673-686.

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

