AICcmodavg (version 2.0-3)

aictabCustom: Custom Creation of Model Selection Tables from User-supplied Input

Description

This function creates a model selection table from model input (log-likelihood, number of estimated parameters) supplied by the user instead of extracting the values automatically from a list of candidate models. The models are ranked based on one of the following information criteria: AIC, AICc, QAIC, QAICc. The table ranks the models based on the selected information criteria and also provides delta AIC and Akaike weights.

Usage

aictabCustom(logL, K, modnames = NULL, second.ord = TRUE, nobs = NULL,
             sort = TRUE, c.hat = 1)

Arguments

logL

a vector of log-likelihood values for the models 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 facilitate the identification of each model in the model selection table. If NULL, the function uses the names in the cand.set list of candidate models (i.e., a named list). If no names appear

second.ord

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

nobs

the sample size required to compute the AICc or QAICc.

sort

logical. If TRUE, the model selection table is ranked according to the (Q)AIC(c) values.

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/tri

Value

• aictabCustom creates an object of class aictab with the following components:
• Modname
  the names of each model of the candidate model set.
• K
  the number of estimated parameters for each model.
• (Q)AIC(c)
  the information criteria requested for each model (AICc, AICc, QAIC, QAICc).
• Delta_(Q)AIC(c)
  the appropriate delta AIC component depending on the information criteria selected.
• ModelLik
  the relative likelihood of the model given the data (exp(-0.5*delta[i])). This is not to be confused with the likelihood of the parameters given the data. The relative likelihood can then be normalized across all models to get the model probabilities.
• (Q)AIC(c)Wt
  the Akaike weights, also termed "model probabilities" sensu Burnham and Anderson (2002) and Anderson (2008). These measures indicate the level of support (i.e., weight of evidence) in favor of any given model being the most parsimonious among the candidate model set.
• Cum.Wt
  the cumulative Akaike weights. These are only meaningful if results in table are sorted in decreasing order of Akaike weights (i.e., sort = TRUE).
• c.hat
  if c.hat was specified as an argument, it is included in the table.
• LL
  if c.hat = 1 and parameters estimated by maximum likelihood, the log-likelihood of each model.
• Quasi.LL
  if c.hat > 1, the quasi log-likelihood of each model.

Details

aictabCustom constructs a model selection table based on one of the four information criteria: AIC, AICc, QAIC, and QAICc. This function is most useful when model input is imported into R from other software (e.g., Program MARK, PRESENCE) or for model classes that are not yet supported by aictab.

References

Anderson, D. R. (2008) Model-based Inference in the Life Sciences: a primer on evidence. Springer: New York.

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

Dail, D., Madsen, L. (2011) Models for estimating abundance from repeated counts of an open population. Biometrics 67, 577--587.

Lebreton, J.-D., Burnham, K. P., Clobert, J., Anderson, D. R. (1992) Modeling survival and testing biological hypotheses using marked animals: a unified approach with case-studies. Ecological Monographs 62, 67--118.

MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A., Langtimm, C. A. (2002) Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248--2255.

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.

Royle, J. A. (2004) N-mixture models for estimating population size from spatially replicated counts. Biometrics 60, 108--115.

See Also

AICcCustom, confset, c_hat, evidence, modavgCustom

Examples

##vector with model LL's
LL <- c(-38.8876, -35.1783, -64.8970)

##vector with number of parameters
Ks <- c(7, 9, 4)

##create a vector of names to trace back models in set
Modnames <- c("Cm1", "Cm2", "Cm3")

##generate AICc table
aictabCustom(logL = LL, K = Ks, modnames = Modnames, nobs = 121,
             sort = TRUE)