aictab {RSA}

Show a table of AIC model comparisons

Description

Show a table of AIC model comparisons

Usage

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

Arguments

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).

Value

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.

Note

This function is similar to the function aictab in the AICcmodavg package.

References

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

Examples

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

## End(Not run)