Update a km object when one or many new
observations are added. Many, but not all, fields of the
km object need to be recalculated when new observations are added.
It is also possible to modify the k last (existing) observations.
# S4 method for km
update(object, newX, newy, newX.alreadyExist = FALSE,
cov.reestim = TRUE, trend.reestim = TRUE, nugget.reestim = FALSE,
newnoise.var = NULL, kmcontrol = NULL, newF = NULL,...)Kriging model of km class.
Matrix with object@d columns and r rows
corresponding to the r locations of the observations to be
updated. These locations can be new locations or existing ones.
Matrix with one column and r rows corresponding to the r
responses at the r locations newX.
Boolean: indicate whether the locations newX are all news or not.
Should the covariance parameters
of the km object be re-estimated?
Should the trend parameters be re-estimated?
Should the nugget effect be re-estimated?
Vector containing the noise variance at each new observations.
Optional list representing the control variables for
the re-estimation of the kriging model once new points are
sampled. The items are the same as in km
Optional matrix containing the value of the trend at the new locations. Setting this argument avoids a call to an expensive function.
Further arguments
Updated km object
Bect J., Ginsbourger D., Li L., Picheny V., Vazquez E. (2010), Sequential design of computer experiments for the estimation of a probability of failure, Statistics and Computing, pp.1-21, 2011, https://arxiv.org/abs/1009.5177
Chevalier C., Bect J., Ginsbourger D., Vazquez E., Picheny V., Richet Y. (2011), Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set, https://hal.archives-ouvertes.fr/hal-00641108/
# NOT RUN {
set.seed(8)
N <- 9 # number of observations
testfun <- branin
# a 9 points initial design
design <- expand.grid(x1=seq(0,1,length=3), x2=seq(0,1,length=3))
response <- testfun(design)
# km object with matern3_2 covariance
# params estimated by ML from the observations
model <- km(formula = ~., design = design,
response = response, covtype = "matern3_2")
model@covariance
newX <- matrix(c(0.4,0.5), ncol = 2) #the point that we are going to add in the km object
newy <- testfun(newX)
newmodel <- update(object = model, newX = newX, newy = newy, cov.reestim = TRUE)
newmodel@covariance
# }