gam.outer                package:mgcv                R Documentation

_M_i_n_i_m_i_z_e _G_C_V _o_r _U_B_R_E _s_c_o_r_e _o_f _a _G_A_M _u_s_i_n_g '_o_u_t_e_r' _i_t_e_r_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Estimation of GAM smoothing parameters is most stable if
     optimization of the UBRE or GCV score is outer to the penalized
     iteratively re-weighted least squares scheme used to estimate the
     model given smoothing  parameters.

     This routine optimizes a GCV or UBRE score in this way. Basically
     the GCV or UBRE score is evaluated for each trial set of smoothing
     parameters by estimating the GAM for those smoothing parameters.
     The score is minimized w.r.t. the parameters numerically, using
     'optim' or 'nlm'. Exact derivatives of the score can be used by
     fitting with 'gam.fit2', which improves efficiency and reliability
     relative to relying solely on finite difference derivatives.  

     Note that there is a choise between basing GCV/UBRE scores on the
     deviance or the Pearson statistic: see 'gam.method'.

     Not normally called directly, but rather a service routine for
     'gam'.

_U_s_a_g_e:

     gam.outer(lsp,fscale,family,control,method,gamma,G)

_A_r_g_u_m_e_n_t_s:

     lsp: The log smoothing parameters.

  fscale: Typical scale of the GCV or UBRE score.

  family: the model family.

 control: control argument to pass to 'gam.fit' if pure finite
          differencing is being used.

  method: method list reurned from 'gam.method'. This defines the
          optimization method to use.

   gamma: The degree of freedom inflation factor for the GCV/UBRE
          score.

       G: List produced by 'gam.setup', containing most of what's
          needed to actually fit GAM.

_D_e_t_a_i_l_s:

     Estimation of smoothing parameters by optimizing GCV scores
     obtained at convergence of the P-IRLS iteration was proposed by
     O'Sullivan et al. (1986), and is here termed `outer' iteration.

_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:

     O 'Sullivan, Yandall & Raynor (1986) Automatic smoothing of
     regression functions in generalized linear models. J. Amer.
     Statist. Assoc. 81:96-103.

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

_S_e_e _A_l_s_o:

     'gam.fit2',  'gam', 'mgcv', 'magic'

