mbrdsim: A function to set up a design for a given set of factors with...
In simrel: Simulation of Multivariate Linear Model Data

Description Usage Arguments Value Author(s) References Examples

The multi-level binary replacement (MBR) design approach is used here in order to facilitate the investigation of the effects of the data properties on the performance of estimation/prediction methods. The mbrdsim function takes as input a list containing a set of factors with their levels. The output is an MBR-design with the combinations of the factor levels to be run.

1	mbrdsim(simlist, fraction, gen = NULL)

`simlist`	A named list containing the levels of a set of (multi-level) factors.
`fraction`	Design fraction at bit-level. Full design: fraction=0, half-fraction: fraction=1, and so on.
`gen`	Generators for the fractioning at the bit level. Default is `NULL` for which the generators are chosen automatically by the `FrF2` function. See documentation of `FrF2` for details on how to set the generators.

`BitDesign`	The design at bit-factor level. The object is of class design, as output from FrF2. Function design.info() can be used to get extra design info of the bit-design. The bit-factors are named.numbered if the input factor list is named.
`Design`	The design at original factor level, non-randomized. The factors are named if the input factor list is named.

Solve Sæbø

Martens, H., Måge, I., Tøndel, K., Isaeva, J., Høy, M. and Sæbø¸, S., 2010, Multi-level binary replacement (MBR) design for computer experiments in high-dimensional nonlinear systems, J, Chemom, 24, 748–756.

# Input: A list of factors with their levels (number of levels must be a multiple of 2).
## Simrel Parameters ----
sim_list <- list(
  p = c(20, 150),
  gamma = seq(0.2, 1.1, length.out = 4),
  relpos = list(list(c(1, 2, 3), c(4, 5, 6)), list(c(1, 5, 6), c(2, 3, 4))),
  R2 = list(c(0.4, 0.8), c(0.8, 0.8)),
  ypos = list(list(1, c(2, 3)), list(c(1, 3), 2))
)
## 1/8 fractional Design ----
dgn <- mbrdsim(sim_list, fraction = 3)
design <- cbind(
  dgn[["Design"]],
  q = lapply(dgn[["Design"]][, "p"], function(x) rep(x/2, 2)),
  type = "multivariate",
  n = 100,
  ntest = 200,
  m = 3,
  eta = 0.6
)
## Simulation ----
sobj <- apply(design, 1, function(x) do.call(simrel, x))
names(sobj) <- paste0("Design", seq.int(sobj))

# Info about the bit-design including bit-level aliasing (and resolution if \code{gen = NULL})
if (requireNamespace("DoE.base", quietly = TRUE)) {
  dgn <- mbrdsim(sim_list, fraction = 3)
  DoE.base::design.info(dgn$BitDesign)
}