bsamGP (version 1.2.6)

predict.blm: Predict method for a blm object

Description

Computes predicted values of Bayesian linear models.

Usage

# S3 method for blm
predict(object, newdata, alpha = 0.05, HPD = TRUE, ...)

Value

A list containing posterior means and 95% credible intervals.

The output list includes the following objects:

wbeta

posterior estimates for regression function.

yhat

posterior estimates for generalised regression function.

Arguments

object

a bsam object

newdata

an optional data matrix or vector with which to predict. If omitted, the fitted values are returned.

alpha

a numeric scalar in the interval (0,1) giving the \(100(1-\alpha)\)% credible intervals.

HPD

a logical variable indicating whether the \(100(1-\alpha)\)% Highest Posterior Density (HPD) intervals are calculated. If HPD=FALSE, the \(100(1-\alpha)\)% equal-tail credible intervals are calculated. The default is TRUE.

...

not used

Details

None.

References

Chen, M., Shao, Q. and Ibrahim, J. (2000) Monte Carlo Methods in Bayesian computation. Springer-Verlag New York, Inc.

See Also

blq, blr, gblr

Examples

if (FALSE) {
	#####################
	# Simulated example #
	#####################

	# Simulate data
	  set.seed(1)

	  n <- 100
	  w <- runif(n)
	  y <- 3 + 2*w + rnorm(n, sd = 0.8)

	  # Fit the model with default priors and mcmc parameters
	  fout <- blr(y ~ w)

	  # Predict
	  new <- rnorm(n)
	  predict(fout, newdata = new)
}