

BIC(nlme)                                    R Documentation

_B_a_y_e_s_i_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_:

     This generic function calculates the Bayesian informa-
     tion criterion, also known as Schwarz's Bayesian crite-
     rion (SBC), for one or several fitted model objects for
     which a log-likelihood value can be obtained, according
     to the formula -2*log-likelihood + npar*log(nobs),
     where npar  represents the number of parameters and
     nobs the number of observations in the fitted model.

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

     BIC(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 BIC; 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 BIC.

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

     Jose Pinheiro and Douglas Bates

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

     Schwarz, G. (1978) "Estimating the Dimension of a
     Model", Annals of Statistics, 6, 461-464.

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

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

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

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

