predict.blm | R Documentation |
Computes predicted values of Bayesian linear models.
## S3 method for class 'blm' predict(object, newdata, alpha = 0.05, HPD = TRUE, ...)
object |
a |
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-α)% credible intervals. |
HPD |
a logical variable indicating whether the 100(1-α)% Highest Posterior Density (HPD) intervals are calculated.
If |
... |
not used |
None.
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. |
Chen, M., Shao, Q. and Ibrahim, J. (2000) Monte Carlo Methods in Bayesian computation. Springer-Verlag New York, Inc.
blq
, blr
, gblr
## Not run: ##################### # 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) ## End(Not run)
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