| GP.predict {BayesGPfit} | R Documentation |
Gaussian process predictions
Description
Gaussian process predictions
Usage
GP.predict(GP_fit, newx, CI = TRUE)
Arguments
GP_fit |
An output object of function GP.Bayes.fit or GP.fast.Bayes.fit. Please refer to them for details. |
newx |
A matrix of real numbers as new grid points for preditions. |
CI |
A logical value indicating prediction. |
Value
A list object When CI is FALSE, the object consists of three elements:
- f
Posterior predictive mean values of the curves.
- x
The grid points for prediction, i.e. "newx".
- work_x
The standardized grid points for prediction.
When CI is FALSE, the object consists of four elements:
- mean
A list object for posterior predictive mean of the curve,consisting of two elements (f is a vector for the curve values; x is a vector or matrix for points evaluated).
- work_x
A matrix of real numbers for the standardized grid points for the model fitting. It has the same dimension as "newx".
- uci
A list object for 95% upper bound of the predictive creditible interval (uci) of the curve, consisting of two elements (f is a vector for curve values; x is a vector or matrix for points evaluated).
- lci
A list object for 95% lower bound of the predictive creditibel interval (lci) of the curve, consisting of two elements (f is a vector for curve value; x is a vector or matrix for points evaluated).
Author(s)
Jian Kang <jiankang@umich.edu>
Examples
set.seed(1224)
traindat = list()
traindat$x = GP.generate.grids(d=2,num_grids=30,random=TRUE)
testdat = list()
testdat$x = GP.generate.grids(d=2,num_grids=30,random=FALSE)
curve = GP.simulate.curve.fast(rbind(traindat$x,testdat$x),a=0.01,b=0.5,poly_degree=20L)
train_curve = list(f=curve$f[1:nrow(traindat$x)],x=traindat$x)
test_curve = list(f=curve$f[nrow(traindat$x)+1:nrow(testdat$x)],x=testdat$x)
traindat$f = train_curve$f + rnorm(length(train_curve$f),sd=1)
testdat$f = test_curve$f + rnorm(length(test_curve$f),sd=1)
fast_fit = GP.fast.Bayes.fit(traindat$f,traindat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE)
reg_fit = GP.Bayes.fit(traindat$f,traindat$x,a=0.01,b=0.5,poly_degree=20L,progress_bar = TRUE)
fast_pred = GP.predict(fast_fit,testdat$x,CI=TRUE)
reg_pred = GP.predict(reg_fit,testdat$x,CI=TRUE)
pmse = c(fast = mean((fast_pred$mean$f-test_curve$f)^2),
reg = mean((reg_pred$mean$f-test_curve$f)^2))
print(pmse)
curves = list(true = test_curve,
Bayes = reg_pred$mean,
fast = fast_pred$mean)
GP.plot.curves(curves,main="Posterior predictive mean")