R: Plot Variable Importance

plot.importance {LaplacesDemon} | R Documentation

## 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 software@bayesian-inference.com

### 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`

[Package LaplacesDemon version 16.1.1 Index]