influence.gp: Diagnostics for a Gaussian Process Model, Based on...

In kergp: Gaussian Process Laboratory

Diagnostics for a Gaussian Process Model, Based on Leave-One-Out

Description

Cross Validation by leave-one-out for a gp object.

Usage

## S3 method for class 'gp'
influence(model, type = "UK", trend.reestim = TRUE, ...)

Arguments

model	An object of class "gp".
type	Character string corresponding to the GP "kriging" family, to be chosen between simple kriging ("SK"), or universal kriging ("UK").
trend.reestim	Should the trend be re-estimated when removing an observation? Default to TRUE.
...	Not used.

Details

Leave-one-out (LOO) consists in computing the prediction at a design point when the corresponding observation is removed from the learning set (and this, for all design points). A quick version of LOO based on Dubrule's formula is also implemented; It is limited to 2 cases:

• (type == "SK") & !trend.reestim and

• (type == "UK") & trend.reestim.

Value

A list composed of the following elements, where n is the total number of observations.

mean	Vector of length n. The i-th element is the kriging mean (including the trend) at the ith observation number when removing it from the learning set.
sd	Vector of length n. The i-th element is the kriging standard deviation at the i-th observation number when removing it from the learning set.

Warning

Only trend parameters are re-estimated when removing one observation. When the number n of observations is small, the re-estimated values can be far away from those obtained with the entire learning set.

Author(s)

O. Roustant, D. Ginsbourger.

References

F. Bachoc (2013), "Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification". Computational Statistics and Data Analysis, 66, 55-69 \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1016/j.csda.2013.03.016")}

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

O. Dubrule (1983), "Cross validation of Kriging in a unique neighborhood". Mathematical Geology, 15, 687-699.

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

predict.gp, plot.gp

kergp documentation built on April 4, 2025, 2:29 a.m.