Plots the posterior distributions of coefficients derived from Bayesian model averaging
Displays plots of the posterior distributions of the coefficients generated by Bayesian model averaging over linear regression.
## S3 method for class 'coef.bas' plot(x, e = 1e-04, subset = 1:x$n.vars, ask = TRUE, ...)
x |
object of class coef.bas |
e |
optional numeric value specifying the range over which the distributions are to be graphed. |
subset |
optional numerical vector specifying which variables to graph (including the intercept) |
ask |
Prompt for next plot |
... |
other parameters to be passed to |
Produces plots of the posterior distributions of the coefficients under model averaging. The posterior probability that the coefficient is zero is represented by a solid line at zero, with height equal to the probability. The nonzero part of the distribution is scaled so that the maximum height is equal to the probability that the coefficient is nonzero.
The parameter e specifies the range over which the distributions are
to be graphed by specifying the tail probabilities that dictate the range to
plot over.
For mixtures of g-priors, uncertainty in g is not incorporated at this time, thus results are approximate
based on function plot.bic by Ian Painter in package BMA;
adapted for 'bas' class by Merlise Clyde clyde@stat.duke.edu
Hoeting, J.A., Raftery, A.E. and Madigan, D. (1996). A method for simultaneous variable selection and outlier identification in linear regression. Computational Statistics and Data Analysis, 22, 251-270.
## Not run: library(MASS) data(UScrime) UScrime[,-2] <- log(UScrime[,-2]) crime_bic <- bas.lm(y ~ ., data=UScrime, n.models=2^15, prior="BIC") plot(coefficients(crime_bic), ask=TRUE) ## End(Not run)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.