aictab | R Documentation |

Show a table of AIC model comparisons

aictab( x, plot = FALSE, bw = FALSE, models = names(x$models)[!names(x$models) %in% c("absdiff", "absunc")], digits = NA )

`x` |
An RSA object |

`plot` |
Should a plot of the AICc table be plotted? |

`bw` |
Should the plot be black & white? |

`models` |
A vector with all model names of the candidate set. Defaults to all polynomial models in the RSA object. |

`digits` |
The output is rounded to this number of digits. No rounding if NA (default). |

- Modnames
Model names.

- K
Number of estimated parameters (including the intercept, residual variance, and, if present in the model, control variables).

- LL
Model log-likelihood.

- AICc
Akaike Information Criterion (corrected).

- Delta_AICc
Difference in AICc between this model and the best model.

- AICcWt
The Akaike weights, also termed "model probabilities" by Burnham and Anderson (2002). Indicates the level of support (i.e., weight of evidence) of a model being the most parsimonious among the candidate model set.

- Cum.Wt
Cumulative Akaike weight. One possible strategy is to restrict interpretation to the "confidence set" of models, that is, discard models with a Cum.Wt > .95 (see Burnham & Anderson, 2002, for details and alternatives).

- evidence.ratio
Likelihood ratio of this model vs. the best model.

- cfi
Comparative Fit Index (CFI).

- R2
Coefficient of determination (R-squared).

- R2.adj
Adjusted R-squared.

- R2.baseline
Only provided if the model contains control variables. Difference in R-squared as compared to the baseline model with intercept and control variables (= the model "null"). This R^2 increment will typically be of interest because it refers to the amount of variance explained by the two predictors X and Y (plus their squared and interaction terms) in the RSA model.

- R2.baseline.p
Only provided if the model contains control variables. p-value for the F-test of the model against the baseline model.

This function is similar to the function `aictab`

in the `AICcmodavg`

package.

Burnham, K. P., & Anderson, D. R. (2002). *Model selection and multimodel inference: A practical information-theoretic approach.* Springer Science & Business Media.

## Not run: data(motcon) r.m <- RSA(postVA~ePow*iPow, motcon, verbose=FALSE) aictab(r.m, plot=TRUE) ## End(Not run)

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