modavgIC {AICcmodavg}  R Documentation 
This function modelaverages the estimate of a parameter of interest among a set of candidate models, and computes the unconditional standard error and unconditional confidence intervals as described in Buckland et al. (1997) and Burnham and Anderson (2002). Computations are based on the values of the information criterion supplied manually by the user.
modavgIC(ic, K, modnames = NULL, estimate, se, uncond.se = "revised", conf.level = 0.95, ic.name = NULL)
ic 
a vector of information criterion values for each model in the candidate model set. 
K 
a vector containing the number of estimated parameters for each model in the candidate model set. 
modnames 
a character vector of model names to identify each model in the
model selection table. If 
estimate 
a vector of estimates for each of the models in the candidate model set. Estimates can be either beta estimates for a parameter of interest or a single prediction from each model. 
se 
a vector of standard errors for each of the estimates appearing in the

uncond.se 
either, 
conf.level 
the confidence level (1  α) requested for the computation of unconditional confidence intervals. 
ic.name 
a character string denoting the name of the information criterion input by the user. This character string will appear in certain column labels of the model selection table. 
modavgIC
computes a modelaveraged estimate from the vector
of parameter estimates specified in estimate
. Estimates and
their associated standard errors must be specified in the same order
as the values of the information criterion, the number of estimated
parameters, and the model names. Estimates provided may be for a
parameter of interest (i.e., beta estimates) or predictions from each
model. This function is most useful for information criterion other
than AIC, AICc, QAIC, and QAICc (e.g., WAIC: Watanabe 2010) or for
classes not supported by modavg
, modavgCustom
, or
modavgPred
.
modavgIC
creates an object of class modavgIC
with
the following components:
Mod.avg.table 
the model selection table. 
Mod.avg.est 
the modelaveraged estimate. 
Uncond.SE 
the unconditional standard error for the modelaveraged estimate. 
Conf.level 
the confidence level used to compute the confidence interval. 
Lower.CL 
the lower confidence limit. 
Upper.CL 
the upper confidence limit. 
Marc J. Mazerolle
Anderson, D. R. (2008) Modelbased Inference in the Life Sciences: a primer on evidence. Springer: New York.
Buckland, S. T., Burnham, K. P., Augustin, N. H. (1997) Model selection: an integral part of inference. Biometrics 53, 603–618.
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical informationtheoretic approach. Second edition. Springer: New York.
Watanabe, S. (2010) Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11, 3571–3594.
aictabCustom
, ictab
,
modavg
, modavgCustom
,
modavgShrink
, modavgPred
## Not run: ##model averaging parameter estimate based on WAIC ##create a vector of names to trace back models in set Modnames < c("global model", "interactive model", "additive model", "invertpred model") ##WAIC values waic < c(105.74, 107.36, 108.24, 100.57) ##number of effective parameters effK < c(7.45, 5.61, 6.14, 6.05) ##vector of predictions Preds < c(0.106, 0.137, 0.067, 0.050) ##vector of SE's for prediction Ses < c(0.128, 0.159, 0.054, 0.039) ##compute modelaveraged estimate and unconditional SE based on WAIC modavgIC(ic = waic, K = effK, modnames = Modnames, estimate = Preds, se = Ses, ic.name = "WAIC") ## End(Not run)