AIC-methods               package:aod               R Documentation

_M_e_t_h_o_d_s _f_o_r _F_u_n_c_t_i_o_n "_A_I_C" _i_n _P_a_c_k_a_g_e "_a_o_d"

_D_e_s_c_r_i_p_t_i_o_n:

     Extracts the Akaike information criterion (AIC) from fitted models
     of formal class "glimML".

_U_s_a_g_e:

     ## S4 method for signature 'glimML':
     AIC(object, ..., k = 2)

_A_r_g_u_m_e_n_t_s:

  object: A fitted model of formal class "glimML" (functions 'betabin'
          or 'negbin').

     ...: An optional list of fitted models separated by commas.

       k: A numeric scalar, with a default value set to 2, thus
          providing the regular AIC.

_M_e_t_h_o_d_s:

     _A_N_Y Generic function: see 'AIC'.

     _g_l_i_m_M_L Extract the AIC from models of formal class "glimML",
          fitted by functions  'betabin' and 'negbin'. The AIC is
          defined as -2*log-likelihood + 2*npar,  where npar represents
          the number of parameters in the fitted model.

_R_e_f_e_r_e_n_c_e_s:

     Burnham, K.P., Anderson, D.R., 2002. _Model selection and
     multimodel inference: a practical information-theoretic approach_.
     New-York, Springer-Verlag, 496 p.

_S_e_e _A_l_s_o:

     Check the examples in 'betabin' and see 'AIC' in package 'stats'.

