BIC-methods              package:bbmle              R Documentation

_L_o_g _l_i_k_e_l_i_h_o_o_d_s _a_n_d _m_o_d_e_l _s_e_l_e_c_t_i_o_n _f_o_r _m_l_e_2 _o_b_j_e_c_t_s

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

     Various functions for likelihood-based and information-theoretic
     model selection of likelihood models

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

     ## S4 method for signature 'ANY, mle2, logLik':
     BIC(object,...)
     ## S4 method for signature 'ANY, mle2, logLik':
     AICc(object,...,nobs,k=2)
     ## S4 method for signature 'ANY, mle2, logLik':
     qAIC(object,...,k=2)
     ## S4 method for signature 'ANY, mle2, logLik':
     qAICc(object,...,nobs,k=2)

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

  object: A 'logLik' or 'mle2' object

     ...: An optional list of additional 'logLik' or 'mle2' objects
          (fitted to the same data set).

    nobs: Number of observations (sometimes  obtainable as an attribute
          of the fit or of the log-likelihood)

       k: penalty parameter (nearly always left at its default value of
          2)

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

     Further arguments to 'BIC' can be specified in the '...' list:
     'delta' (logical) specifies whether to include a column for
     delta-BIC in the output.

_V_a_l_u_e:

     A table of the BIC values, degrees of freedom, and possibly
     delta-BIC values relative to the minimum-BIC model

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


     _l_o_g_L_i_k 'signature(object = "mle2")': Extract maximized
          log-likelihood.

     _A_I_C 'signature(object = "mle2")': Calculate Akaike Information
          Criterion

     _A_I_C_c 'signature(object = "mle2")': Calculate small-sample
          corrected Akaike Information Criterion

     _B_I_C 'signature(object = "mle2")': Calculate Bayesian (Schwarz)
          Information Criterion

     _B_I_C 'signature(object = "logLik")': Calculate Bayesian (Schwarz)
          Information Criterion

     _B_I_C 'signature(object = "ANY")': Calculate Bayesian (Schwarz)
          Information Criterion

     _a_n_o_v_a 'signature(object="mle2")': Likelihood Ratio Test
          comparision of different models

_N_o_t_e:

     This is implemented in an ugly way and could probably be improved!

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

       x <- 0:10
       y <- c(26, 17, 13, 12, 20, 5, 9, 8, 5, 4, 8)
       (fit <- mle2(y~dpois(lambda=ymax/(1+x/xhalf)),
       start=list(ymax=25,xhalf=3)))
       (fit2 <- mle2(y~dpois(lambda=(x+1)*slope),
       start=list(slope=1)))
       BIC(fit,nobs=length(x))
       BIC(fit,fit2,nobs=length(x))
       

