black-boxes | R Documentation |

Set of analytic functions that take functional variables as inputs. Since they run quickly, they can be used for testing of funGp functionalities as if they were black box computer models. They cover different situations (number of scalar inputs and complexity of the inputs-output mathematical relationship).

```
fgp_BB1(sIn, fIn, n.tr)
fgp_BB2(sIn, fIn, n.tr)
fgp_BB3(sIn, fIn, n.tr)
fgp_BB4(sIn, fIn, n.tr)
fgp_BB5(sIn, fIn, n.tr)
fgp_BB6(sIn, fIn, n.tr)
fgp_BB7(sIn, fIn, n.tr)
```

`sIn` |
Object with class |

`fIn` |
Object with class |

`n.tr` |
Object with class |

For all the functions, the `d_s`

scalar inputs
`x_i`

are in the real interval `[0,\,1]`

and
the `d_f`

functional inputs
`f_i(t_i)`

are defined on the interval
`[0,\,1]`

. Expressions for the values are as follows.

With`fgp_BB1`

`d_s = 2`

`d_f = 2`

x1 * sin(x2) + x1 * mean(f1) - x2^2 * diff(range(f2))

With`fgp_BB2`

`d_s = 2`

and`d_f = 2`

x1 * sin(x2) + mean(exp(x1 * t1) * f1) - x2^2 * mean(f2^2 * t2)

With`fgp_BB3`

`d_s = 2`

and`d_f = 2`

is the first analytical example in Muehlenstaedt et al (2017)x1 + 2 * x2 + 4 * mean(t1 * f1) + mean(f2)

With`fgp_BB4`

`d_s = 2`

and`d_f = 2`

is the second analytical example in*preprint*of Muehlenstaedt et al (2017)(x2 - (5 / (4 * pi^2)) * x1^2 + (5 / pi) * x1 - 6)^2 + 10 * (1 - (1 / (8 * pi))) * cos(x1) + 10 + (4 / 3) * pi * (42 * mean(f1 * (1 - t1)) + pi * ((x1 + 5) / 5) + 15) * mean(t2 * f2))

With`fgp_BB5`

`d_s=2`

and`d_f=2`

is inspired by the second analytical example in*final version*of Muehlenstaedt et al (2017)(x2 - (5 / (4 * pi^2)) * x1^2 + (5 / pi) * x1 - 6)^2 + 10 * (1 - (1 / (8 * pi))) * cos(x1) + 10 + (4 / 3) * pi * (42 * mean(15 * f1 * (1 - t1) - 5) + pi * ((x1 + 5) / 5) + 15) * mean(15 * t2 * f2))

With`fgp_BB6`

`d_s = 2`

and`d_f = 2`

is inspired by the analytical example in Nanty et al (2016)2 * x1^2 + 2 * mean(f1 + t1) + 2 * mean(f2 + t2) + max(f2) + x2

With`fgp_BB7`

`d_s = 5`

and`d_f = 2`

is inspired by the second analytical example in*final version*of Muehlenstaedt et al (2017)(x2 + 4 * x3 - (5 / (4 * pi^2)) * x1^2 + (5 / pi) * x1 - 6)^2 + 10 * (1 - (1 / (8 * pi))) * cos(x1) * x2^2 * x5^3 + 10 + (4 / 3) * pi * (42 * sin(x4) * mean(15 * f1 * (1 - t1) - 5) + pi * (((x1 * x5 + 5) / 5) + 15) * mean(15 * t2 * f2))

An object of class `"matrix"`

with the values of the output at the specified input coordinates.

The functions listed above were used to validate the functionality and stability of this package. Several tests involving all main functions, plotters and getters were run for scalar-input, functional-input and hybrid-input models. In all cases, the output of the functions were correct from the statistical and programmatic perspectives. For an example on the kind of tests performed, the interested user is referred to the introductory funGp manual (\Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.18637/jss.v109.i05")}).

Muehlenstaedt, T., Fruth, J., and Roustant, O. (2017),
"Computer experiments with functional inputs and scalar outputs by a norm-based approach".
*Statistics and Computing*, **27**, 1083-1097.
[SC]

Nanty, S., Helbert, C., Marrel, A., Pérot, N., and Prieur, C. (2016),
"Sampling, metamodeling, and sensitivity analysis of numerical simulators with functional stochastic inputs".
*SIAM/ASA Journal on Uncertainty Quantification*, **4**(1), 636-659.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.1137/15M1033319")}

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