plot.gp {kergp} | R Documentation |
Diagnostic Plot for the Validation of a gp
Object
Description
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.
Usage
## S3 method for class 'gp'
plot(x, y, kriging.type = "UK",
trend.reestim = TRUE, which = 1:3, ...)
Arguments
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 |
... |
No other argument for this method. |
Details
The standardized residuals are defined by [y(\mathbf{x}_i) -
\widehat{y}_{-i}(\mathbf{x}_i)] /
\widehat{\sigma}_{-i}(\mathbf{x}_i)
, where y(\mathbf{x}_i)
is the response at the
location \mathbf{x}_i
,
\widehat{y}_{-i}(\mathbf{x}_i)
is the fitted
value when the i
-th observation is omitted (see
influence.gp
), and
\widehat{\sigma}_{-i}(\mathbf{x}_i)
is the
corresponding kriging standard deviation.
Value
A list composed of the following elements where n is the total number of observations.
mean |
A vector of length n. The |
sd |
A vector of length n. The |
Warning
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.
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.
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
and influence.gp
, the
predict
and influence
methods for "gp"
.