funGp (version 1.0.0)

plot,Xfgpm-method: Plot method for the class "Xfgpm"

Description

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.

Usage

# S4 method for Xfgpm
plot(
  x,
  y = NULL,
  which = c("diag", "evol"),
  calib = TRUE,
  fitp = TRUE,
  horiz = FALSE,
  ...
)

Arguments

x

The Xfgpm object to plot.

y

Not used.

which

Character giving the type of plot wanted. Can take the value "diag" or "evol". See Examples.

calib

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".

fitp

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".

horiz

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.

See Also

* fgpm_factory for structural optimization of funGp models.

Examples

# 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))

}