# plot

##### Diagnostic plot for the validation of a km object

Three plots are currently available, based on the `leaveOneOut.km`

results: one plot of fitted values against response values, one plot of standardized residuals, and one qqplot of standardized residuals.

##### Usage

```
# S4 method for km
plot(x, y, kriging.type = "UK", trend.reestim = FALSE, ...)
```

##### Arguments

- x
an object of class "km" without noisy observations.

- y
not used.

- kriging.type
an optional character string corresponding to the kriging family, to be chosen between simple kriging ("SK") or universal kriging ("UK").

- trend.reestim
should the trend be reestimated when removing an observation? Default to FALSE.

- ...
no other argument for this method.

##### Details

The diagnostic plot has not been implemented yet for noisy observations. The standardized residuals are defined by `( y(xi) - yhat_{-i}(xi) ) / sigmahat_{-i}(xi)`

, where `y(xi)`

is the response at the point `xi`

, `yhat_{-i}(xi)`

is the fitted value when removing the observation `xi`

(see `leaveOneOut.km`

), and `sigmahat_{-i}(xi)`

is the corresponding kriging standard deviation.

##### Value

A list composed of:

a vector of length *n*. The ith coordinate is equal to the kriging mean (including the trend) at the ith observation number when removing it from the learning set,

a vector of length *n*. The ith coordinate is equal to the kriging standard deviation at the ith observation number when removing it from the learning set,

##### Warning

Kriging parameters are not re-estimated when removing one observation. With few points, the re-estimated values can be far from those obtained with the entire learning set. One option is to reestimate the trend coefficients, by setting `trend.reestim=TRUE`

.

##### References

N.A.C. Cressie (1993), *Statistics for spatial data*, Wiley series in probability and mathematical statistics.

J.D. Martin and T.W. Simpson (2005), Use of kriging models to approximate deterministic computer models, *AIAA Journal*, **43** no. 4, 853-863.

M. Schonlau (1997), *Computer experiments and global optimization*, Ph.D. thesis, University of Waterloo.

##### See Also

##### Examples

```
# NOT RUN {
# A 2D example - Branin-Hoo function
# a 16-points factorial design, and the corresponding response
d <- 2; n <- 16
fact.design <- expand.grid(seq(0,1,length=4), seq(0,1,length=4))
fact.design <- data.frame(fact.design); names(fact.design)<-c("x1", "x2")
branin.resp <- data.frame(branin(fact.design)); names(branin.resp) <- "y"
# kriging model 1 : gaussian covariance structure, no trend,
# no nugget effect
m1 <- km(~.^2, design=fact.design, response=branin.resp, covtype="gauss")
plot(m1) # LOO without parameter reestimation
plot(m1, trend.reestim=TRUE) # LOO with trend parameters reestimation
# (gives nearly the same result here)
# }
```

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