predict.bsam {bsamGP} R Documentation

## Predict method for a bsam object

### Description

Computes the predicted values of Bayesian spectral analysis models.

### Usage

## S3 method for class 'bsam'
predict(object, newp, newnp, alpha = 0.05, HPD = TRUE, type = "response", ...)


### Arguments

 object a bsam 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. type the type of prediction required. type = "response" gives the posterior predictive samples as default. The "mean" option returns expectation of the posterior estimates. ... not used

None.

### Value

A list object of class predict.bsam 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 either response or expectation of response. For gbsar, it gives posterior estimates for expectation of response. fxResid posterior estimates for fitted parametric residuals. Not applicable for gbsar.

bsaq, bsar, gbsar

### Examples

## Not run:

##########################################
# Increasing Convex to Concave (S-shape) #
##########################################

# simulate data
f <- function(x) 5*exp(-10*(x - 1)^4) + 5*x^2

set.seed(1)

n <- 100
x <- runif(n)
y <- f(x) + rnorm(n, sd = 1)

# Number of cosine basis functions
nbasis <- 50

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