mlmc (version 2.0.2)

plot.mlmc.test: Plot an mlmc.test object

Description

Produces diagnostic plots on the result of an mlmc.test function call.

Usage

# S3 method for mlmc.test
plot(x, which = "all", cols = NA, ...)

Value

No return value, called for side effects.

Arguments

x

an mlmc.test object as produced by a call to the mlmc.test function.

which

a vector of strings specifying which plots to produce, or "all" to do all diagnostic plots The options are:

"var" = \(\log_2\) of variance against level;

"mean" = \(\log_2\) of the absolute value of the mean against level;

"consis" = consistency against level;

"kurt" = kurtosis against level;

"Nl" = \(\log_2\) of number of samples against level;

"cost" = \(\log_{10}\) of cost against \(\log_{10}\) of epsilon (accuracy).

cols

the number of columns across to plot to override the default value.

...

additional arguments which are passed on to plotting functions.

Author

Louis Aslett <[e-mail]>

Details

Most of the plots produced are relatively self-explanatory. However, the consistency and kurtosis plots in particular may require some background. It is highly recommended to refer to Section 3.3 of Giles (2015), where the rationale for these diagnostic plots is addressed in full detail.

References

Giles, M.B. (2015) 'Multilevel Monte Carlo methods', Acta Numerica, 24, pp. 259–328. Available at: tools:::Rd_expr_doi("10.1017/S096249291500001X").

Examples

# \donttest{
tst <- mlmc.test(opre_l, N = 2000000,
                 L = 5, N0 = 1000,
                 eps.v = c(0.005, 0.01, 0.02, 0.05, 0.1),
                 Lmin = 2, Lmax = 6,
                 option = 1)
tst
plot(tst)
# }