R: Update of a kriging model

update {DiceKriging} | R Documentation

## Update of a kriging model

### Description

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 signature '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

### Author(s)

Clement Chevalier (IMSV, Switzerland, and IRSN, France)

### 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

`km`

### Examples

    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

[Package DiceKriging version 1.5.5 Index]