image.bma: Images of models used in Bayesian model averaging

Description

Creates an image of the models selected using bas.

Usage

## S3 method for class 'bma':
image(x, top.models=20, intensity=TRUE, prob=TRUE, log=TRUE,
rotate=TRUE, color="rainbow", subset=NULL, offset=.75, digits=3,
vlas=2,plas=0,rlas=0, ...)

Arguments

An object of type 'bma' created by BAS

top.models

Number of the top ranked models to plot

intensity

Logical variable, when TRUE image intensity is proportional to the probability or log(probability) of the model, when FALSE, intensity is binary indicating just presence (light) or absence (dark) of a variable.

prob

Logical variable for whether the area in the image for each model should be proportional to the posterior probability (or log probability) of the model (TRUE) or with equal area (FALSE).

log

Logical variable indicating whether the intensities should be based on log posterior odds (TRUE) or posterior probabilities (FALSE). The log of the posterior odds is for comparing the each model to the worst model in the top.models.

rotate

Should the image of models be rotated so that models are on the y-axis and variables are on the x-axis (TRUE)

color

The color scheme for image intensities. The value "rainbow" uses the rainbow palette. The value "blackandwhite" produces a black and white image (greyscale image)

subset

indices of variables to include in plot; 1 is the intercept

offset

numeric value to add to intensity

digits

number of digits in posterior probabilities to keep

vlas

las parameter for placing variable names; see par

plas

las parameter for posterior probability axis

rlas

las parameter for model ranks

...

Other parameters to be passed to the image and axis functions.

Details

Creates an image of the model space sampled using bas. If a subset of the top models are plotted, then probabilities are renormalized over the subset.

References

Clyde, M. (1999) Bayesian Model Averaging and Model Search Strategies (with discussion). In Bayesian Statistics 6. J.M. Bernardo, A.P. Dawid, J.O. Berger, and A.F.M. Smith eds. Oxford University Press, pages 157-185.

Examples

Run this code

data("Hald")
hald.ZSprior =  bas.lm(Y~ ., data=Hald,  prior="ZS-null")
image(hald.ZSprior, subset=-1)