predict.SSM: Point prediction of smooth supersaturated models.

In peterrobertcurtis/SSM: Fit and Analyze Smooth Supersaturated Models

Description

This method gives the prediction of an SSM object at a point. If the SSM has a metamodel error estimate then a (1 - α) credible interval is also output.

Usage

## S3 method for class 'SSM'
predict(object, x, alpha = 0.05, ...)

Arguments

object	An SSM object.
x	A d length vector identifying the prediction point.
alpha	(optional) A number in [0, 1] for the (1 - α) metamodel error estimate credible interval. Set to 0.05 by default.
...	further arguments passed to or from other methods.

Value

Either a number if the SSM has no metamodel error estimating Gaussian process, or three numbers giving the model prediction ($model), and the lower and upper bounds of the credible interval ($lower and $upper) respectively.

Examples

data(attitude)
X <- transform11(attitude[ 2:7])
Y <- attitude[ , 1]
# with no metamodel error estimating GP.
s <- fit.ssm(X, Y)
predict(s, rep(1,6))

# with metamodel error estimating GP.
s <- fit.ssm(X, Y, GP = TRUE)
predict(s, rep(1,6))

peterrobertcurtis/SSM documentation built on May 25, 2019, 2:10 a.m.