

extractAIC {base}                            R Documentation

_E_x_t_r_a_c_t _A_I_C _f_r_o_m _a _F_i_t_t_e_d _M_o_d_e_l

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

     Computes the (generalized) Akaike Information Criterion
     for a fitted parametric model.

_U_s_a_g_e_:

     extractAIC    (fit, scale,     k = 2, ...)
     extractAIC.lm (fit, scale = 0, k = 2, ...)
     extractAIC.glm(fit, scale = 0, k = 2, ...)
     extractAIC.aov(fit, scale = 0, k = 2, ...)
     extractAIC.coxph  (fit, scale, k = 2, ...)
     extractAIC.negbin (fit, scale, k = 2, ...)
     extractAIC.survreg(fit, scale, k = 2, ...)

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

     fit: fitted model, usually the result of a fitter like
          `lm'.

   scale: optional numeric specifying the scale parameter of
          the model, see `scale' in `step'.

       k: numeric specifying the ``weight'' of the equiva-
          lent degrees of freedom (=:`edf') part in the AIC
          formula.

     ...: further arguments (currently unused in base R).

_D_e_t_a_i_l_s_:

     The criterion used is

                   AIC = - 2*log L +  k * edf,

     where L is the likelihood and `edf' the equivalent
     degrees of freedom (i.e., the number of parameters for
     usual parametric models) of `fit'.

     For generalized linear models (i.e., for `lm', `aov',
     and `glm'), -2log L is the deviance, as computed by
     `deviance(fit)'.

     `k = 2' corresponds to the traditional AIC, using `k =
         log(n)' provides the BIC (Bayes IC) instead.

     For further information, particularly about `scale',
     see `step'.

_V_a_l_u_e_:

     A numeric vector of length 2, giving

     edf: the ``equivalent degrees of freedom'' of the fit-
          ted model `fit'.

     AIC: the (generalized) Akaike Information Criterion for
          `fit'.

_N_o_t_e_:

     These functions are used in `add1', `drop1' and `step'
     and that may be their main use.

_A_u_t_h_o_r_(_s_)_:

     B. D. Ripley

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

     Venables, W. N. and B. D. Ripley (1997).  Modern
     Applied Statistics with S-PLUS.  New York: Springer
     (2nd ed).

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

     `deviance', `add1', `step'

_E_x_a_m_p_l_e_s_:

     example(glm)
     extractAIC(glm.D93)#>>  5  15.129

