importance            package:AICcmodavg            R Documentation

_C_o_m_p_u_t_e _I_m_p_o_r_t_a_n_c_e _V_a_l_u_e_s _o_f _V_a_r_i_a_b_l_e

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

     This function calculates the relative importance of variables (w+)
     based on the sum of Akaike weights (model probabilities) of the
     models that include the variable.  Note that this measure of
     evidence is only appropriate when the variable appears in the same
     number of models as those that do not include the variable.

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

     importance(cand.set, parm, modnames, c.hat = 1, second.ord = TRUE,
     nobs = NULL)  

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

cand.set: a list storing each of the models in the candidate model set. 

    parm: the parameter of interest for which a measure of relative
          importance is required.  

modnames: a character vector of model names to facilitate the
          identification of each model in the model selection table. 

   c.hat: value of overdispersion parameter (i.e., variance inflation
          factor) such as that obtained from 'c_hat'.  Note that values
          of c.hat different  from 1 are only appropriate for binomial
          GLM's with trials > 1 (i.e., success/trial or cbind(success,
          failure) syntax) or with Poisson GLM's. If c.hat > 1,
          'aictab' will return the quasi-likelihood analogue of the
          information criteria requested. 

second.ord: logical. If TRUE, the function returns the second-order
          Akaike information criterion (i.e., AICc). 

    nobs: this argument allows to specify a numeric value other than
          total sample size to compute the AICc.  This is relevant only
          for linear mixed models where sample size is not
          straightforward.  In such cases, one might use total number
          of observations or number of independent clusters as the
          value of 'nobs'. 

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

     'importance' returns an object of class 'importance' consisting of
     the following components: 

    parm: the parameter for which an importance value is required.

  w.plus: the parameter for which an importance value is required.

 w.minus: the sum of Akaike weights for the models that exclude the
          parameter of interest

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

     Marc J. Mazerolle

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

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

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

     'AICc', 'aictab', 'c_hat', 'modavg', 'evidence', 'confset',
     'modavgpred'

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

     ##example on Orthodont data set in nlme
     require(nlme)

     ##set up candidate model list
     Cand.models <- list()
     Cand.models[[1]] <- lme(distance ~ age, data = Orthodont, method = "ML")
     ##random is ~ age | Subject
     Cand.models[[2]] <- lme(distance ~ age + Sex, data = Orthodont, random =
     ~ 1, method = "ML")
     Cand.models[[3]] <- lme(distance ~ 1, data = Orthodont, random = ~ 1,
     method = "ML") 
     Cand.models[[4]] <- lme(distance ~ Sex, data = Orthodont, random = ~ 1,
     method = "ML") 

     ##create a vector of model names
     Modnames <- NULL
     for (i in 1:length(Cand.models)) {
     Modnames[i] <- paste("mod", i, sep = "")
     }

     importance(cand.set = Cand.models, parm = "age", modnames = Modnames,
     second.ord = TRUE, nobs = NULL)
     ##round to 4 digits after decimal point
     print(importance(cand.set = Cand.models, parm = "age", modnames = Modnames,
     second.ord = TRUE, nobs = NULL), digits = 4)

