ictab {AICcmodavg} | R Documentation |

## Create Model Selection Tables from User-supplied Information Criterion

### Description

This function creates a model selection table from information criterion values supplied by the user. The table ranks the models based on the values of the information criterion and also displays delta values and information criterion weights.

### Usage

```
ictab(ic, K, modnames = NULL, sort = TRUE, ic.name = NULL)
```

### Arguments

`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 |

`sort` |
logical. If |

`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. |

### Details

`ictab`

constructs a model selection table based on the
information criterion values supplied by the user. 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
`aictab`

or `bictab`

.

### Value

`ictab`

creates an object of class `ictab`

with the
following components:

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

`K` |
the number of estimated parameters for each model. |

`IC` |
the values of the information criterion input by the
user. If a value for |

`Delta_IC` |
the delta information criterion component comparing each model to 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. |

`ICWt` |
the information criterion 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 information criterion weights. These
are only meaningful if results in table are sorted in decreasing
order of Akaike weights (i.e., |

### 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.

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.

### See Also

`aictabCustom`

, `confset`

, `evidence`

,
`modavgCustom`

, `modavgIC`

### Examples

```
##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)
##generate WAIC table
ictab(ic = waic, K = effK, modnames = Modnames,
sort = TRUE, ic.name = "WAIC")
```

*AICcmodavg*version 2.3-3 Index]