influence.gp: Diagnostics for a Gaussian Process Model, Based on...
In kergp: Gaussian Process Laboratory
Description
Usage
Arguments
Details
Value
Warning
Author(s)
References
See Also
Description
Cross Validation by leave-one-out for a gp object.
Usage
1
2
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
link
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
kergp documentation built on March 18, 2021, 5:06 p.m.
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Description Usage Arguments Details Value Warning Author(s) References See Also
Description
Cross Validation by leave-one-out for a gp object.
Usage
1 2 |
Arguments
model |
An object of class |
type |
Character string corresponding to the GP "kriging" family, to be
chosen between simple kriging ( |
trend.reestim |
Should the trend be re-estimated when removing an observation?
Default to |
... |
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.reestimand(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 link
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
R Package Documentation
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