sb {sensitivity}

Sequential Bifurcations

Package:

sensitivity

Version:

1.7

Description

sb implements the Sequential Bifurcations screening method (Bettonvil and Kleijnen 1996). This is an alpha version that might strongly evolve in the future.

Usage

sb(p, sign = rep("+", p), interaction = FALSE)
 
## S3 method for class 'sb':
ask((x, i = NULL, ...))

## S3 method for class 'sb':
tell((x, y, ...))

## S3 method for class 'sb':
print((x, ...))

## S3 method for class 'sb':
plot((x, ...))

Arguments

p: number of factors.
sign: a vector fo length p filled with "+" and "-", giving the (assumed) signs of the factors effects.
interaction: a boolean, TRUE if the model is supposed to be with interactions, FALSE otherwise.
x: a list of class "sb" storing the state of the screening study at the current iteration.
y: a vector of model responses.
i: an integer, used to force a wanted bifurcation instead of that proposed by the algorithm.
...: not used.

Details

The model without interaction is while the model with interactions is In both cases, the factors are assumed to be uniformly distributed on [-1,1]. This is a difference with Bettonvil et al. where the factors vary across [0,1] in the former case, while [-1,1] in the latter.

Another difference with Bettonvil et al. is that in the current implementation, the groups are splitted right in the middle.

Values

sb returns a list of class "sb", containing all the input arguments detailed before, plus the following components: The groups effects can be displayed with the print method.

i: the vector of bifurcations.
y: the vector of observations.
ym: the vector of mirror observations (model with interactions only).

References

B. Bettonvil and J. P. C. Kleijnen, 1996, Searching for important factors in simulation models with many factors: sequential bifurcations, European Journal of Operational Research, 96, 180--194.

Examples

# a model with interactions
p <- 50
beta <- numeric(length = p)
beta[1:5] <- runif(n = 5, min = 10, max = 50)
beta[6:p] <- runif(n = p - 5, min = 0, max = 0.3)
beta <- sample(beta)
gamma <- matrix(data = runif(n = p^2, min = 0, max = 0.1), nrow = p, ncol = p)
gamma[lower.tri(gamma, diag = TRUE)] <- 0
gamma[1,2] <- 5
gamma[5,9] <- 12
f <- function(x) { return(sum(x * beta) + (x %*% gamma %*% x))}
 
# 10 iterations of SB
sa <- sb(p, interaction = TRUE)
for (i in 1 : 10) {
  x <- ask(sa)
  y <- list()
  for (i in names(x)) {
    y[[i]] <- f(x[[i]])
  }
  tell(sa, y)
}
print(sa)
plot(sa)

Documentation reproduced from package sensitivity, version 1.7. License: GPL-2