# update

##### Update of a kriging model

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.

##### Usage

```
# 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,...)
```

##### Arguments

- 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

##### Value

Updated km object

##### References

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, http://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*, http://hal.archives-ouvertes.fr/hal-00641108/

##### See Also

##### Examples

```
# 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
# }
```

*Documentation reproduced from package DiceKriging, version 1.5.6, License: GPL-2 | GPL-3*