Compute Credible (Bayesian Confidence) Intervals for a BAS predict object
Compute credible intervals for in-sample or out of sample prediction or for the regression function
## S3 method for class 'pred.bas' confint(object, parm, level = 0.95, nsim = 10000, ...)
object |
an object created by |
parm |
character variable, "mean" or "pred". If missing parm='pred'. |
level |
the nominal level of the (point-wise) credible interval |
nsim |
number of Monte Carlo simulations for sampling methods with BMA |
... |
optional arguments to pass on to next function call; none at this time. |
This constructs approximate 95 percent Highest Posterior Density intervals for 'pred.bas' objects. If the estimator is based on model selection, the intervals use a Student t distribution using the estimate of g. If the estimator is based on BMA, then nsim draws from the mixture of Student t distributions are obtained with the HPD interval obtained from the Monte Carlo draws.
a matrix with lower and upper level * 100 percent credible intervals for either the mean of the regression function or predicted values.
Merlise A Clyde
Other bas methods:
BAS,
bas.lm(),
coef.bas(),
confint.coef.bas(),
diagnostics(),
fitted.bas(),
force.heredity.bas(),
image.bas(),
plot.confint.bas(),
predict.basglm(),
predict.bas(),
summary.bas(),
update.bas(),
variable.names.pred.bas()
Other CI methods:
confint.coef.bas(),
plot.confint.bas()
data("Hald")
hald.gprior = bas.lm(Y~ ., data=Hald, alpha=13, prior="g-prior")
hald.pred = predict(hald.gprior, estimator="BPM", predict=FALSE, se.fit=TRUE)
confint(hald.pred, parm="mean")
confint(hald.pred) #default
hald.pred = predict(hald.gprior, estimator="BMA", predict=FALSE, se.fit=TRUE)
confint(hald.pred)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.