update-NoiseKM-method: Update a 'NoiseKM' Object with New Points

In rlibkriging: Kriging Models using the 'libKriging' Library

Update a NoiseKM Object with New Points

Description

The update method is used when new observations are added to a fitted kriging model. Rather than fitting the model from scratch with the updated observations added, the results of the fit as stored in object are used to achieve some savings.

Usage

## S4 method for signature 'NoiseKM'
update(
  object,
  newX,
  newy,
  newnoise.var,
  newX.alreadyExist = FALSE,
  cov.reestim = TRUE,
  trend.reestim = cov.reestim,
  nugget.reestim = FALSE,
  kmcontrol = NULL,
  newF = NULL,
  ...
)

Arguments

object	A NoiseKM object.
newX	A numeric matrix containing the new design points. It must have object@d columns in correspondence with those of the design matrix used to fit the model which is stored as object@X.
newy	A numeric vector of new response values, in correspondence with the rows of newX.
newnoise.var	Variance of an additional noise on the new response.
newX.alreadyExist	Logical. If TRUE, newX can contain some input points that are already in object@X.
cov.reestim	Logical. If TRUE, the vector theta of correlation ranges will be re-estimated using the new observations as well as the observations already used when fitting object. Only TRUE can be used for now.
trend.reestim	Logical. If TRUE the vector beta of trend coefficients will be re-estimated using all the observations. Only TRUE can be used for now.
nugget.reestim	Logical. If TRUE the nugget effect will be re-estimated using all the observations. Only FALSE can be used for now.
kmcontrol	A list of options to tune the fit. Not available yet.
newF	New trend matrix. XXXY?
...	Ignored.

Details

Without a dedicated update method for the class "NoiseKM", this would have been inherited from the class "km". The dedicated method is expected to run faster. A comparison can be made by coercing a NoiseKM object to a km object with as.km before calling update.

Value

The updated NoiseKM object.

Author(s)

Yann Richet [e-mail]

See Also

as.km to coerce a NoiseKM object to the class "km".

Examples

f <- function(x) 1 - 1 / 2 * (sin(12 * x) / (1 + x) + 2 * cos(7 * x) * x^5 + 0.7)
plot(f)
set.seed(123)
X <- as.matrix(runif(5))
y <- f(X) + 0.01*rnorm(nrow(X))
points(X, y, col = "blue")
KMobj <- NoiseKM(design = X, response = y, noise=rep(0.01^2,5), covtype = "gauss")
x <-  seq(from = 0, to = 1, length.out = 101)
p_x <- predict(KMobj, x)
lines(x, p_x$mean, col = "blue")
lines(x, p_x$lower95, col = "blue")
lines(x, p_x$upper95, col = "blue")
newX <- as.matrix(runif(3))
newy <- f(newX) + 0.01*rnorm(nrow(newX))
points(newX, newy, col = "red")

## replace the object by its udated version
KMobj <- update(KMobj, newX=newX, newy=newy, newnoise.var=rep(0.01^2,3))

x <- seq(from = 0, to = 1, length.out = 101)
p2_x <- predict(KMobj, x)
lines(x, p2_x$mean, col = "red")
lines(x, p2_x$lower95, col = "red")
lines(x, p2_x$upper95, col = "red")

rlibkriging documentation built on Oct. 3, 2024, 1:06 a.m.