logLik.gam               package:mgcv               R Documentation

_E_x_t_r_a_c_t _t_h_e _l_o_g _l_i_k_e_l_i_h_o_o_d _f_o_r _a _f_i_t_t_e_d _G_A_M

_D_e_s_c_r_i_p_t_i_o_n:

     Function to extract the log-likelihood for a fitted 'gam' model
     (note that the models are usually fitted by penalized likelihood
     maximization).

_U_s_a_g_e:

     ## S3 method for class 'gam':
     logLik(object,...)

_A_r_g_u_m_e_n_t_s:

  object: fitted model objects of class 'gam' as produced by 'gam()'.

     ...: un-used in this case

_D_e_t_a_i_l_s:

     Modification of 'logLik.glm' which corrects the degrees of freedom
     for use with 'gam' objects. 

     The function is provided so that 'AIC' functions correctly with
     'gam' objects, and uses the appropriate degrees of freedom
     (accounting for penalization). Note,  when using 'AIC' for
     penalized models, that the degrees of freedom are the effective
     degrees of freedom and not the number of parameters, and the model
     maximizes the penalized likelihood, not the actual likelihood!
     This seems to be reasonably well founded for the known scale
     parameter case (see Hastie and Tibshirani, 1990, section 6.8.3 and
     also Wood 2008),  and in fact in this case the default smoothing
     parameter estimation criterion is effectively this modified AIC.

_V_a_l_u_e:

     Standard 'logLik' object: see 'logLik'.

_A_u_t_h_o_r(_s):

     Simon N. Wood simon.wood@r-project.org based directly on
     'logLik.glm'

_R_e_f_e_r_e_n_c_e_s:

     Hastie and Tibshirani, 1990, Generalized Additive Models.

     Wood, S.N. (2008) Fast stable direct fitting and smoothness
     selection for generalized additive models. J.R.Statist.Soc.B
     70(3):495-518

_S_e_e _A_l_s_o:

     'AIC'