predict.CGP {CGP} | R Documentation |
Compute predictions from the composite Gaussian process (CGP) model. 95% prediction intervals can also be calculated.
## S3 method for class 'CGP'
predict(object, newdata = NULL, PI = FALSE, ...)
object |
An object of class " |
newdata |
Optional. The matrix of predictive input locations, where each row of |
PI |
If |
... |
For compatibility with generic method |
Given an object of “CGP
” class, this function predicts responses at unobserved newdata
locations. If the PI
is set to be TRUE
, 95% predictions intervals are also computed.
If newdata
is equal to the design matrix of the object
, predictions from the CGP model will be identical to the yobs
component of the object
and the width of the prediction intervals will be shrunk to zero. This is due to the interpolating property of the predictor.
The function returns a list containing the following components:
Yp |
Vector of predictive values at |
gp |
Vector of predictive values at |
lp |
Vector of predictive values at |
v |
Vector of predictive standardized local volatilities at |
Y_low |
If |
Y_up |
If |
Shan Ba <shanbatr@gmail.com> and V. Roshan Joseph <roshan@isye.gatech.edu>
Ba, S. and V. Roshan Joseph (2012) “Composite Gaussian Process Models for Emulating Expensive Functions”. Annals of Applied Statistics, 6, 1838-1860.
### A simulated example from Gramacy and Lee (2012) ``Cases for the nugget
### in modeling computer experiments''. \emph{Statistics and Computing}, 22, 713-722.
#Training data
X<-c(0.775,0.83,0.85,1.05,1.272,1.335,1.365,1.45,1.639,1.675,
1.88,1.975,2.06,2.09,2.18,2.27,2.3,2.36,2.38,2.39)
yobs<-sin(10*pi*X)/(2*X)+(X-1)^4
#Testing data
UU<-seq(from=0.7,to=2.4,by=0.001)
y_true<-sin(10*pi*UU)/(2*UU)+(UU-1)^4
plot(UU,y_true,type="l",xlab="x",ylab="y")
points(X,yobs,col="red")
## Not run:
#Fit the CGP model
mod<-CGP(X,yobs)
summary(mod)
mod$objval
#-40.17315
mod$lambda
#0.01877432
mod$theta
#2.43932
mod$alpha
#578.0898
mod$bandwidth
#1
mod$rmscv
#0.3035192
#Predict for the testing data 'UU'
modpred<-predict(mod,UU)
#Plot the fitted CGP model
#Red: final predictor; Blue: global trend
lines(UU,modpred$Yp,col="red",lty=3,lwd=2)
lines(UU,modpred$gp,col="blue",lty=5,lwd=1.8)
## End(Not run)