dictab {AICcmodavg} | R Documentation |

## Create Model Selection Tables from Bayesian Analyses

### Description

This function creates a model selection table based on the deviance
information criterion (DIC). The table ranks the models based on the
DIC and also provides delta DIC and DIC weights. `dictab`

selects
the appropriate function to create the model selection table based on
the object class. The current version works with objects of `bugs`

,
`rjags`

, `jagsUI`

classes.

### Usage

```
dictab(cand.set, modnames = NULL, sort = TRUE, ...)
## S3 method for class 'AICbugs'
dictab(cand.set, modnames = NULL, sort = TRUE, ...)
## S3 method for class 'AICrjags'
dictab(cand.set, modnames = NULL, sort = TRUE, ...)
## S3 method for class 'AICjagsUI'
dictab(cand.set, modnames = NULL, sort = TRUE, ...)
```

### Arguments

`cand.set` |
a list storing each of the models 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 |

`sort` |
logical. If |

`...` |
additional arguments passed to the function. |

### Details

`dictab`

internally creates a new class for the `cand.set`

list of candidate models, according to the contents of the list. The
current function is implemented for `bugs`

, `jags`

,
`jagsUI`

classes. The function constructs a model selection table
based on the DIC (Spiegelhalter et al. 2002). Note that DIC might not
be appropriate to select among a set of hierarchical models and that
modifications to the information criterion have been proposed (Millar
2009).

### Value

`dictab`

creates an object of class `dictab`

with the
following components:

`Modname` |
the name of each model of the candidate model set. |

`pD` |
the effective number of estimated parameters for each model. |

`DIC` |
the deviance information criterion for each model. |

`Delta_DIC` |
the delta DIC of each model, measuring the difference in DIC between each model and the top-ranked model. |

`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. |

`DICWt` |
the DIC weights, 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 DIC weights. These are only meaningful
if results in table are sorted in decreasing order of DIC weights
(i.e., |

`Deviance` |
the deviance of each model. |

### Author(s)

Marc J. Mazerolle

### 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.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P., van der Linde,
A. (2002). Bayesian measures of complexity and fit. *Journal of the
Royal Statistical Society, Series B* **64**, 583–639.

### See Also

`aictabCustom`

, `aictab`

, `confset`

,
`DIC`

, `evidence`

### Examples

```
##from ?jags example in R2jags package
## Not run:
require(R2jags)
model.file <- system.file(package="R2jags", "model", "schools.txt")
file.show(model.file)
##data
J <- 8.0
y <- c(28.4,7.9,-2.8,6.8,-0.6,0.6,18.0,12.2)
sd <- c(14.9,10.2,16.3,11.0,9.4,11.4,10.4,17.6)
jags.data <- list (J = J, y = y, sd = sd)
jags.inits <- function(){
list(theta=rnorm(J, 0, 100), mu=rnorm(1, 0, 100),
sigma=runif(1, 0, 100))
}
jags.parameters <- c("theta", "mu", "sigma")
##run model
schools.sim <- jags(data = jags.data, inits = jags.inits,
parameters = jags.parameters,
model.file = model.file,
n.chains = 3, n.iter = 10)
#note that n.iter should be higher
##set up in list
Cand.mods <- list(schools.sim)
Model.names <- "hierarchical model"
##other models can be added to Cand.mods
##to compare them to the top model
##model selection table
dictab(cand.set = Cand.mods, modnames = Model.names)
detach(package:R2jags)
## End(Not run)
```

*AICcmodavg*version 2.3-3 Index]