CaliCo (version 0.1.1)

forecast: Generates a forecast base on calibration run with calibrate

Description

forecast is a function that allows to generate a new model.class in which the prediction is done with the Maximum A Posteriori

Usage

forecast(modelfit, x.new)

Arguments

modelfit

a calibrate.class object

x.new

newdata for the prediction

Value

return a model.class (see model.class for more details)

Details

Note that all the methods for a model.class object are availble. Be careful with the x in the plot function. It needs to be the x-axis of calibrated data and predicted data.

See Also

model, prior, calibrate, sequentialDesign

Examples

# NOT RUN {
###################### The code to calibrate
X <- cbind(seq(0,1,length.out=10),seq(0,1,length.out=10))
code <- function(X,theta)
{
  return((6*X[,1]*theta[2]-2)^2*theta[1]*sin(theta[3]*X[,2]-4))
}
Yexp <- code(X,c(1,1,11))+rnorm(10,0,0.1)

############### For the first model
###### Definition of the model
md <- model(code,X,Yexp,"model1")
###### Definition of the prior densities
pr <- prior(type.prior=c("gaussian","gaussian","gaussian","gamma"),opt.prior=
list(c(1,0.01),c(1,0.01),c(11,3),c(2,0.1)))
###### Definition of the calibration options
opt.estim=list(Ngibbs=200,Nmh=600,thetaInit=c(1,1,11,0.1),r=c(0.3,0.3),
sig=diag(4),Nchains=1,burnIn=100)
###### Run the calibration
mdfit <- calibrate(md,pr,opt.estim)
###### Prediction between 1 and 1.2
X.new <- cbind(seq(1,1.2,length.out=10),seq(1,1.2,length.out=10))
fr <- forecast(mdfit,X.new)
print(fr)
plot(fr,c(X[,1],X.new[,1]))
# }