Description Usage Arguments Details Value Warning References See Also
Three plots are currently available, based on the influence
results: one plot of fitted values against response values, one plot
of standardized residuals, and one qqplot of standardized residuals.
1 2 3 |
x |
An object with S3 class |
y |
Not used. |
kriging.type |
Optional 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 |
which |
A subset of 1, 2, 3 indicating which figures to plot (see
|
... |
No other argument for this method. |
The standardized residuals are defined by (y(xi) - yhat_{-i}(xi)) /
sigmahat_{-i}(xi), where y(xi) is the response at the
location xi,
yhat_{-i}(xi) is the fitted
value when the i-th observation is omitted (see
influence.gp
), and
sigmahat_{-i}(xi) is the
corresponding kriging standard deviation.
A list composed of the following elements where n is the total number of observations.
mean |
A vector of length n. The i-th element is the kriging mean (including the trend) at the i-th observation number when removing it from the learning set. |
sd |
A 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. |
Only trend parameters are re-estimated when removing one observation. When the number n of observations is small, re-estimated values can substantially differ from those obtained with the whole learning set.
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.
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.
predict.gp
and influence.gp
, the
predict
and influence
methods for "gp"
.
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