predict.bsamdpm: Predict method for a bsamdpm object

Description

Computes the predicted values of Bayesian spectral analysis models with Dirichlet process mixture errors.

Usage

# S3 method for bsamdpm
predict(object, newp, newnp, alpha = 0.05, HPD = TRUE, ...)

Value

A list object of class predict.bsamdpm containing posterior means and \(100(1-\alpha)\)% credible intervals.

The output list includes the following objects:

fxobs: posterior estimates for unknown functions over observation.
wbeta: posterior estimates for parametric part.
yhat: posterior estimates for fitted values of response.

Arguments

object: a bsamdpm object
newp: an optional data of parametric components with which to predict. If omitted, the fitted values are returned.
newnp: an optional data of nonparametric components 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.

Examples

Run this code

if (FALSE) {

#####################
# Increasing-convex #
#####################

# Simulate data
set.seed(1)

n <- 200
x <- runif(n)
e <- c(rnorm(n/2, sd = 0.5), rnorm(n/2, sd = 3))
y <- exp(6*x - 3) + e

# Number of cosine basis functions
nbasis <- 50

# Fit the model with default priors and mcmc parameters
fout <- bsardpm(y ~ fs(x), nbasis = nbasis, shape = 'IncreasingConvex')

# Prediction
xnew <- runif(n)
predict(fout, newnp = xnew)

}