Plot an object with class "Xfgpm" representing
a collection of functional GP models corresponding to
different structural parameters.
Two types of graphics can be shown depending on the choice of
which. The choice which = "diag" is used to display
diagnostics of the quality of the optimized model. Two types of
diagnostic plots are shown as sub-plots by default, but each can be
discarded if wanted. The choice which = "evol" is used to
assess the quality of the fitted fgpm models on the basis of
Leave-One-Out cross-validation.
The choice which = "diag" (default) provides two plots for
assessing the quality of the output delivered by the model selection
algorithm in the fgpm_factory function. The first
one is a calibration plot similar to the one offered for
fgpm objects by plot,fgpm-method.
This plot allows to validate the absolute quality of the selected
model. The second one displays the performance statistic of all
the models successfully evaluated by the model selection
algorithm. This provides a notion of the relative quality of the
selected model with respect to the other models that can be made
using the same data.
The choice which = "evol" displays the evolution of the
quality of the configurations evaluated along the iterations, by
the model selection algorithm in the fgpm_factory function. For
each iteration, the performance statistic of all the evaluated
models is printed, along with the corresponding median of the
group. The plot also includes the global maximum, which
corresponds to the best performance statistic obtained up to the
current iteration. In this plot, it is typical to have some points
falling relatively far from the maximum, even after multiple
iterations. This happens mainly because we have multiple
categorical features, whose alteration might change the
performance statistic in a nonsmooth way. On the other hand, the
points that fall below zero usually correspond to models whose
hyperparameters were hard to optimize. This occurs sporadically
during the log-likelihood optimization for Gaussian processes, due
to the non-linearity of the objective function. As long as the
maximum keeps improving and the median remains close to it, none
of the two aforementioned phenomena is matter for worries. Both
of them respond to the mechanism of exploration implemented in the
algorithm, which makes it able to progressively move towards
better model configurations.
The Xfgpm object to plot.
Not used.
Character giving the type of plot wanted. Can take the value
"diag" or "evol". See Examples.
Logical. If TRUE the calibration plot of the
selected model will be included in the display in its
"diagnostic" part if which is set to "diag".
Logical. If TRUE a scatter plot of the quality
of all explored models will be included in the display in its
"diagnostic" part if which is set to "diag".
Logical. Used only when which is "diag"
and when both calib and fitp are TRUE. If
horiz is TRUE the two subplots are displayed
horizontally (on a same row) rather than vertically which is
the default.
Other graphical parameters such as main of
xlab. When which is "diag" and both
calib and fitp are TRUE, the graphical parameters
should be enclosed into a list and passed with the formal name
calib.gpars or fitp.gpars.
* fgpm_factory for structural optimization of funGp models.
# generating input and output data
set.seed(100)
n.tr <- 2^5
x1 <- x2 <- x3 <- x4 <- x5 <- seq(0, 1, length = n.tr^(1/5))
sIn <- expand.grid(x1 = x1, x2 = x2, x3 = x3, x4 = x4, x5 = x5)
fIn <- list(f1 = matrix(runif(n.tr * 10), ncol = 10),
f2 = matrix(runif(n.tr * 22), ncol = 22))
sOut <- fgp_BB7(sIn, fIn, n.tr)
if (FALSE) {
# optimizing the model structure with 'fgpm_factory' (~10 seconds)
xm <- fgpm_factory(sIn = sIn, fIn = fIn, sOut = sOut)
# assessing the quality of the model - absolute and w.r.t. the other
# explored models
plot(xm, which = "evol")
# diagnostics (two subplots)
plot(xm, which = "diag")
plot(xm, which = "diag", horiz = TRUE)
# diagnostics (one plot)
plot(xm, which = "diag", fitp = FALSE)
plot(xm, which = "diag", calib = FALSE)
# customizing some graphical parameters
plot(xm, calib.gpars = list(xlim = c(800,1000), ylim = c(600,1200)),
fitp.gpars = list(main = "Relative quality", legends = FALSE))
}