plot.importance: Plot Variable Importance

In LaplacesDemonR/LaplacesDemon: Complete Environment for Bayesian Inference

Plot Variable Importance

Description

This may be used to plot variable importance with BPIC, predictive concordance, a discrepancy statistic, or the L-criterion regarding an object of class importance.

Usage

## S3 method for class 'importance'
plot(x, Style="BPIC", ...)

Arguments

x	This required argument is an object of class importance.
Style	When Style="BPIC", BPIC is shown, and BPIC is the default. Otherwise, predictive concordance is plotted when Style="Concordance", a discrepancy statistic is plotted when Style="Discrep", or the L-criterion is plotted when Style="L-criterion".
...	Additional arguments are unused.

Details

The x-axis is either BPIC (Ando, 2007), predictive concordance (Gelfand, 1996), a discrepancy statistic (Gelman et al., 1996), or the L-criterion (Laud and Ibrahim, 1995) of the Importance function (depending on the Style argument), and variables are on the y-axis. A more important variable is associated with a dot that is plotted farther to the right. For more information on variable importance, see the Importance function.

Author(s)

Statisticat, LLC [e-mail]

References

Ando, T. (2007). "Bayesian Predictive Information Criterion for the Evaluation of Hierarchical Bayesian and Empirical Bayes Models". Biometrika, 94(2), p. 443–458.

Gelfand, A. (1996). "Model Determination Using Sampling Based Methods". In Gilks, W., Richardson, S., Spiegehalter, D., Chapter 9 in Markov Chain Monte Carlo in Practice. Chapman and Hall: Boca Raton, FL.

Gelman, A., Meng, X.L., and Stern H. (1996). "Posterior Predictive Assessment of Model Fitness via Realized Discrepancies". Statistica Sinica, 6, p. 733–807.

Laud, P.W. and Ibrahim, J.G. (1995). "Predictive Model Selection". Journal of the Royal Statistical Society, B 57, p. 247–262.

See Also

Importance

LaplacesDemonR/LaplacesDemon documentation built on April 1, 2024, 7:22 a.m.