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

In BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

Images of models used in Bayesian model averaging

Description

Creates an image of the models selected using bas.

Usage

## S3 method for class 'bas'
image(
  x,
  top.models = 20,
  intensity = TRUE,
  prob = TRUE,
  log = TRUE,
  rotate = TRUE,
  color = "rainbow",
  subset = NULL,
  drop.always.included = FALSE,
  offset = 0.75,
  digits = 3,
  vlas = 2,
  plas = 0,
  rlas = 0,
  ...
)

Arguments

x	A BMA object of type 'bas' 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/exclude in plot
drop.always.included	logical variable to drop variables that are always forced into the model. FALSE by default.
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.

Note

Suggestion to allow area of models be proportional to posterior probability due to Thomas Lumley

Author(s)

Merlise Clyde [e-mail]

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.

See Also

bas

Other bas methods: BAS, bas.lm(), coef.bas(), confint.coef.bas(), confint.pred.bas(), diagnostics(), fitted.bas(), force.heredity.bas(), plot.confint.bas(), predict.bas(), predict.basglm(), summary.bas(), update.bas(), variable.names.pred.bas()

Other bas plots: plot.bas(), plot.coef.bas()

Examples

require(graphics)
data("Hald")
hald.ZSprior <- bas.lm(Y ~ ., data = Hald, prior = "ZS-null")
image(hald.ZSprior, drop.always.included = TRUE) # drop the intercept

BAS documentation built on Sept. 30, 2024, 9:19 a.m.