This method 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 the plotLOO function. 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.

# S4 method for Xfgpm
plotX(x.model, calib = TRUE, fitp = TRUE, ...)

Arguments

x.model

an object of class Xfgpm containing the output of the model selection algorithm in fgpm_factory.

calib

a boolean indicating whether the calibration plot of the selected model should be included in the display. Default is TRUE.

fitp

a boolean indicating whether scatter plot of the quality of all explored models should be included in the display. Default is TRUE.

...

additional arguments affecting the display. Since this method allows to generate two plots from a single function call, the extra arguments for each plot should be included in a list. For the calibration plot, the list should be called calib.gpars. For the plot of the fitness of explored models, the list should be called fitp.gpars. The following typical graphics parameters are valid entries of both lists: xlim, ylim, xlab, ylab, main. The boolean argument legends can also be included in any of the two lists in order to control the display of legends in the corresponding plot.

Value

None.

References

Betancourt, J., Bachoc, F., Klein, T., and Gamboa, F. (2020), Technical Report: "Ant Colony Based Model Selection for Functional-Input Gaussian Process Regression. Ref. B3D-WP3.2". RISCOPE project. [HAL]

Betancourt, J., Bachoc, F., and Klein, T. (2020), R Package Manual: "Gaussian Process Regression for Scalar and Functional Inputs with funGp - The in-depth tour". RISCOPE project. [HAL]

See also

* fgpm_factory for structural optimization of funGp models;

* plotEvol for a plot on the evolution of the model selection algorithm in fgpm_factory.

Examples

# generating input and output data
set.seed(100)
n.tr <- 2^5
sIn <- expand.grid(x1 = seq(0,1,length = n.tr^(1/5)), x2 = seq(0,1,length = n.tr^(1/5)),
                   x3 = seq(0,1,length = n.tr^(1/5)), x4 = seq(0,1,length = n.tr^(1/5)),
                   x5 = seq(0,1,length = n.tr^(1/5)))
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)
# \donttest{
# optimizing the model structure with fgpm_factory (~5 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
plotX(xm)

# customizing some graphical parameters
plotX(xm, calib.gpars = list(xlim = c(800,1000), ylim = c(600,1200)),
          fitp.gpars = list(main = "Relative quality", legends = FALSE))
# }