AIC                  package:stats                  R Documentation

_A_k_a_i_k_e'_s _A_n _I_n_f_o_r_m_a_t_i_o_n _C_r_i_t_e_r_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Generic function calculating the Akaike information criterion for
     one or several fitted model objects for which a log-likelihood
     value can be obtained, according to the formula -2*log-likelihood
     + k*npar, where npar represents the number of parameters in the
     fitted model, and k = 2 for the usual AIC, or k = log(n) (n the
     number of observations) for the so-called BIC or SBC (Schwarz's
     Bayesian criterion).

_U_s_a_g_e:

     AIC(object, ..., k = 2)

_A_r_g_u_m_e_n_t_s:

  object: a fitted model object, for which there exists a 'logLik'
          method to extract the corresponding log-likelihood, or an
          object inheriting from class 'logLik'.

     ...: optionally more fitted model objects.

       k: numeric, the "penalty" per parameter to be used; the default
          'k = 2' is the classical AIC.

_D_e_t_a_i_l_s:

     The default method for 'AIC', 'AIC.default()' entirely relies on
     the existence of a 'logLik' method computing the log-likelihood
     for the given class.

     When comparing fitted objects, the smaller the AIC, the better the
     fit.

     The log-likelihood and hence the AIC is only defined up to an
     additive constant.  Different constants have conventionally be
     used for different purposes and so 'extractAIC' and 'AIC' may give
     different values (and do for models of class '"lm"': see the help
     for 'extractAIC').

_V_a_l_u_e:

     If just one object is provided, returns a numeric value with the
     corresponding AIC (or BIC, or ..., depending on 'k'); if more than
     one object are provided, returns a 'data.frame' with rows
     corresponding to the objects and columns representing the number
     of parameters in the model ('df') and the AIC.

_A_u_t_h_o_r(_s):

     Jose Pinheiro and Douglas Bates

_R_e_f_e_r_e_n_c_e_s:

     Sakamoto, Y., Ishiguro, M., and Kitagawa G. (1986). _Akaike
     Information Criterion Statistics_. D. Reidel Publishing Company.

_S_e_e _A_l_s_o:

     'extractAIC', 'logLik'.

_E_x_a_m_p_l_e_s:

     lm1 <- lm(Fertility ~ . , data = swiss)
     AIC(lm1)
     stopifnot(all.equal(AIC(lm1),
                         AIC(logLik(lm1))))
     ## a version of BIC or Schwarz' BC :
     AIC(lm1, k = log(nrow(swiss)))

