evidence             package:AICcmodavg             R Documentation

_C_o_m_p_u_t_e _E_v_i_d_e_n_c_e _R_a_t_i_o _B_e_t_w_e_e_n _T_w_o _M_o_d_e_l_s

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

     This function compares two models of a candidate model set based
     on their evidence ratio (i.e., ratio of Akaike weights).  The
     default computes the evidence ratio of the Akaike weights between
     the top-ranked model and a lower-ranked model.  You must supply a
     model selection table of class 'aictab' as the first argument.

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

     evidence(aic.table, model.high = "top", model.low)

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

aic.table: a model selection table of class 'aictab' such as that
          produced by 'aictab'. 

model.high: the top-ranked model (default), or alternatively, the name
          of another model as it appears in the model selection table. 

model.low: the name of a lower-ranked model such as it appears in the
          model selection table. 

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

     The default compares the Akaike weights of the top-ranked model to
     another model of the candidate model set.  The evidence ratio can
     be interpreted as the number of times a given model is more
     parsimonious than a lower-ranked model.  If one desires an
     evidence ratio that does not involve a comparison with the
     top-ranking model, the name of the required model must be
     specified in the model.high argument.

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

     'evidence' produces an object of class 'evidence' with the
     following components: 

Model.high: the top-ranked model among the two compared. 

Model.low: the lower-ranked model among the two compared. 

Ev.ratio: the evidence ratio between the two models compared.     

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

     Marc J. Mazerolle

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

     Burnham, K. P., 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', 'importance', 'confset',
     'modavgpred'

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

     ##run example from Burnham and Anderson (2002, p. 183) with two
     ##non-nested models
     data(pine)
     Cand.set <- list( )
     Cand.set[[1]] <- lm(y ~ x, data = pine)
     Cand.set[[2]] <- lm(y ~ z, data = pine)

     ##assign model names
     Modnames <- c("raw density", "density corrected for resin content")

     ##compute model selection table
     aicctable.out <- aictab(cand.set = Cand.set, modnames = Modnames)

     ##compute evidence ratio
     evidence(aic.table = aicctable.out, model.low = "raw density")           
     ##round to 4 digits after decimal point
     print(evidence(aic.table = aicctable.out, model.low = "raw density"),
     digits = 4)

     ##run models for the 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")

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

     ##compute AICc table
     aic.table.1 <- aictab(cand.set = Cand.models, modnames = Modnames,
     second.ord = TRUE)

     ##compute evidence ratio between best model and second-ranked model
     evidence(aic.table = aic.table.1, model.high = "top", model.low =
     "mod1")  

     ##compute evidence ratio between second-best model and third-ranked model 
     evidence(aic.table = aic.table.1, model.high = "mod1", model.low =
     "mod3")

