individual_errors | R Documentation |
Plots the predictive error with respect to a variety of quantities.
individual_errors(
em,
validation,
errtype = "normal",
xtype = "index",
plottype = "normal"
)
em |
The emulator to perform diagnostics on |
validation |
The validation set of points with output(s) |
errtype |
The type of individual error to be plotted. |
xtype |
The value to plot against |
plottype |
Whether to plot a standard or Q-Q plot. |
The choice of errors to plot is controlled by errtype
, and can be one
of four things: normal, corresponding to the regular standardised errors; eigen,
corresponding to the errors after reordering given by the eigendecomposition
of the emulator covariance matrix; chol, similarly deriving errors after Cholesky
decomposition; and cholpivot, deriving the errors after pivoted Cholesky decomposition.
What the errors are plotted with respect to is controlled by xtype
. The options
are index, which plots them in their order in the validation set; em, which plots errors
with respect to the emulator prediction at that point; and any named parameter of the
model, which plots with respect to the values of that parameter.
Finally, the plot type is controlled by plottype
: this can be one of normal,
which plots the errors; or qq, which produces a Q-Q plot of the errors.
The default output is to plot the standardised errors (with no decomposition)
against the ordering in the validation set; i.e. errtype = "normal"
,
xtype = "index"
, plottype = "normal"
.
Some combinations are not permitted, as the output would not be meaningful. Errors arising from an eigendecomposition cannot be plotted against either emulator prediction or a particular parameter (due to the transformation induced by the eigendecomposition); Q-Q plots are not plotted for a non-decomposed set of errors, as the correlation between errors makes it much harder to interpret.
The relevant plot.
Bastos & O'Hagan (2009) <doi:10.1198/TECH.2009.08019>
Other diagnostic functions:
analyze_diagnostic()
,
classification_diag()
,
comparison_diag()
,
get_diagnostic()
,
residual_diag()
,
standard_errors()
,
summary_diag()
,
validation_diagnostics()
i1 <- individual_errors(SIREmulators$ems$nS, SIRSample$validation)
i2 <- individual_errors(SIREmulators$ems$nS, SIRSample$validation, "chol", "em")
i3 <- individual_errors(SIREmulators$ems$nS, SIRSample$validation, "eigen", plottype = "qq")
i4 <- individual_errors(SIREmulators$ems$nS, SIRSample$validation, "cholpivot", xtype = "aSI")
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