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,...)
```

object

Kriging model of `km`

class.

newX

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.

newy

Matrix with one column and r rows corresponding to the r
responses at the r locations `newX`

.

newX.alreadyExist

Boolean: indicate whether the locations `newX`

are all news or not.

cov.reestim

Should the covariance parameters
of the `km`

object be re-estimated?

trend.reestim

Should the trend parameters be re-estimated?

nugget.reestim

Should the nugget effect be re-estimated?

newnoise.var

Vector containing the noise variance at each new observations.

kmcontrol

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`

newF

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 # }