mgcv.control              package:mgcv              R Documentation

_S_e_t_t_i_n_g _m_g_c_v _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 generalized ridge
     regression problem using routine 'mgcv'.

_U_s_a_g_e:

     mgcv.control(conv.tol=1e-7,max.half=20,target.edf=NULL,min.edf=-1)

_A_r_g_u_m_e_n_t_s:

conv.tol: The convergence tolerance.

max.half: successive step halvings are employed if the Newton method
          and then the steepest descent backup fail to improve the
          UBRE/GCV score. This is how many to use before giving up.

target.edf: If this is non-null it indicates that cautious optimization
          should be used, which  opts for the local minimum closest to
          the target model edf if there are multiple local minima in
          the  GCV/UBRE score.

 min.edf: Lower bound on the model edf. Useful for avoiding numerical
          problems at high smoothing parameter values. Negative for
          none.

_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

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

_S_e_e _A_l_s_o:

     'mgcv'

