

AIC(nlme)                                    R Documentation

_A_k_a_i_k_e _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_:

     This generic function calculates 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 + 2*npar, where npar
     represents the number of parameters in the fitted
     model. When comparing fitted objects, the smaller the
     AIC, the better the fit.

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

     AIC(object, ...)

_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'.

     ...: optional fitted model objects.

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

     if just one object is provided, returns a numeric value
     with the corresponding AIC; if more than one object are
     provided, returns a `data.frame' with rows correspond-
     ing 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_:

     `logLik', `BIC', `AIC.logLik'

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

     library(nlme)
     data(Orthodont)
     fm1 <- lm(distance ~ age, data = Orthodont) # no random effects
     AIC(fm1)
     fm2 <- lme(distance ~ age, data = Orthodont) # random is ~age
     AIC(fm1, fm2)

