ICtab                 package:bbmle                 R Documentation

_C_o_m_p_u_t_e _t_a_b_l_e _o_f _i_n_f_o_r_m_a_t_i_o_n _c_r_i_t_e_r_i_a _a_n_d _a_u_x_i_l_i_a_r_y _i_n_f_o

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

     Computes information criteria for a series of models, optionally
     giving information about weights, differences between ICs, etc.

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

     ICtab(..., type=c("AIC","BIC","AICc","qAIC","qAICc"),
         weights = FALSE, delta = TRUE, base = FALSE, sort = TRUE,
     nobs, dispersion = 1, mnames, k = 2)
     AICtab(...)
     BICtab(...)
     AICctab(...)
     ## S3 method for class 'ICtab':
     print(x,...)

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

     ...: a list of (logLik or?) mle objects; in the case of 'AICtab'
          etc., could also include other arguments to 'ICtab'

    type: specify information criterion to use

    base: (logical) include base IC values?

 weights: (logical) compute IC weights?

   delta: (logical) compute differences among ICs?

    sort: (logical) sort ICs in increasing order?

    nobs: (integer) number of observations: required for 'type="BIC"'
          or 'type="AICc"' unless objects have an '"nobs"' attribute

dispersion: overdispersion estimate, for computing qAIC: required for
          'type="qAIC"' or 'type="qAICc"' unless objects have a
          '"dispersion"' attribute

  mnames: names for table rows: defaults to names of objects passed

       k: penalty term (largely unused: left at default of 2)

       x: an ICtab object

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

     A data frame containing: 

      IC: information criterion

      df: degrees of freedom/number of parameters

     dIC: difference in IC from minimum-IC model

 weights: exp(-dIC/2)/sum(exp(-dIC/2))

_N_o_t_e:

     (1) The print method uses sensible defaults; all ICs are rounded
     to the nearest 0.1, and IC weights are printed using 'format.pval'
     to print an inequality for values <0.001. (2) The computation of
     degrees of freedom/number of parameters (e.g., whether variance
     parameters are included in the total) varies enormously between
     packages.  As long as the df computations for a given set of
     models is consistent, differences don't matter, but one needs to
     be careful with log likelihoods and models taken from different
     packages.  If necessary one can change the degrees of freedom
     manually by saying 'attr(obj,"df") <- df.new', where 'df.new' is
     the desired number of parameters. (3) Defaults have changed to
     'sort=TRUE', 'base=FALSE', 'delta=TRUE', to match my conviction
     that it rarely makes sense to report the overall values of
     information criteria

_A_u_t_h_o_r(_s):

     Ben Bolker

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

     Burnham and Anderson 2002

