| confint.gekm | R Documentation |
Determines confidence intervals for the estimated regression coefficients.
## S3 method for class 'gekm'
confint(object, parm, level = 0.95, scale = FALSE, ...)
object |
an object of class |
parm |
a |
level |
confidence level for calculating confidence intervals. Default is |
scale |
|
... |
further arguments, currently not used. |
A matrix with the lower and upper bounds of the confidence intervals for each parameter.
Carmen van Meegen
Koehler, J. and Owen, A. (1996). Computer Experiments. In Ghosh, S. and Rao, C. (eds.), Design and Analysis of Experiments, volume 13 of Handbook of Statistics, pp. 261–308. Elsevier Science. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/S0169-7161(96)13011-X")}.
Santner, T. J., Williams, B. J., and Notz, W. I. (2018). The Design and Analysis of Computer Experiments. 2nd edition. Springer-Verlag.
gekm for fitting a (gradient-enhanced) Kriging model.
coef for extracting the (matrix of) coefficients.
vcov for calculating the covaraince matrix of the regression coefficients.
## 1-dimensional example: Oakley and O’Hagan (2002)
# Define test function and its gradient
f <- function(x) 5 + x + cos(x)
fGrad <- function(x) 1 - sin(x)
# Generate coordinates and calculate slopes
x <- seq(-5, 5, length = 5)
y <- f(x)
dy <- fGrad(x)
dat <- data.frame(x, y)
deri <- data.frame(x = dy)
# Fit gradient-enhanced Kriging model
gekm.1d <- gekm(y ~ ., data = dat, deriv = deri, covtype = "gaussian", theta = 1)
# Determine confidence intervals
confint(gekm.1d)
confint(gekm.1d, scale = TRUE)
confint(gekm.1d, parm = "x", scale = TRUE)
confint(gekm.1d, parm = 1, scale = TRUE)
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