AICcmodavg-package        package:AICcmodavg        R Documentation

_M_o_d_e_l _S_e_l_e_c_t_i_o_n _a_n_d _M_u_l_t_i_m_o_d_e_l _I_n_f_e_r_e_n_c_e _B_a_s_e_d _o_n (_Q)_A_I_C(_c)

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

     Description:  This package includes functions to create model
     selection tables based on Akaike's information criterion (AIC) and
     the second-order AIC (AICc), as well as their quasi-likelihood
     counterparts (QAIC, QAICc).  Tables are printed with delta AIC and
     Akaike weights.  The package also includes functions to conduct
     model averaging (multimodel inference) for a given parameter of
     interest or predicted values.  Other handy functions enable the
     computation of relative variable importance, evidence ratios, and
     confidence sets for the best model.  The present version works
     with linear models ('lm' class), generalized linear models ('glm'
     class), linear mixed models ('lme' class), multinomial and ordinal
     logistic regressions ('multinom' and 'polr' classes).

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


       Package:   AICcmodavg
       Type:      Package
       Version:   1.05
       Date:      2009-12-04
       License:   GPL (>=2 )
       LazyLoad:  yes

     This package contains several useful functions for model selection
     and multimodel inference:

   '_A_I_C_c' Computes AIC, AICc, and their quasi-likelihood counterparts
        (QAIC, QAICc).

   '_a_i_c_t_a_b' Constructs model selection tables with number of
        parameters, AIC, delta AIC, Akaike weights or variants based on
        other AICc, QAIC, and QAICc for a set of candidate models.

   '_c_o_n_f_s_e_t' Determines the confidence set for the best model based on
        one of three criteria.

   '_e_v_i_d_e_n_c_e' Computes the evidence ratio between the highest-ranked
        model based on the information criteria selected and a
        lower-ranked model.

   '_i_m_p_o_r_t_a_n_c_e' Computes importance values (w+) for the support of a
        given parameter among set of candidate models.

   '_m_o_d_a_v_g' Computes model-averaged estimate, unconditional standard
        error, and unconditional confidence interval of a parameter of
        interest among a set of candidate models.

   '_m_o_d_a_v_g_p_r_e_d' Computes model-average predictions and unconditional
        SE's among entire set of candidate models.

   '_c__h_a_t' Computes an estimate of variance inflation factor for
        binomial or Poisson GLM's based on Pearson's chi-square.

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

     Marc J. Mazerolle <marc.mazerolle@uqat.ca>.  Special thanks to T.
     Ergon for the original idea of storing candidate models in a list.

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

     Anderson, D. R. (2008) _Model-based inference in the life
     sciences: a primer on evidence_. Springer: New York.

     Burnham, K. P., and Anderson, D. R. (2002) _Model selection and
     multimodel inference: a practical information-theoretic approach_.
     Second edition. Springer: New York. 

     Burnham, K. P., Anderson, D. R. (2004) Multimodel inference:
     understanding AIC and BIC in model selection. _Sociological
     Methods and Research_ *33*, 261-304.

     Mazerolle, M. J. (2006) Improving data analysis in herpetology:
     using Akaike's Information Criterion (AIC) to assess the strength
     of biological hypotheses. _Amphibia-Reptilia_ *27*, 169-180.

