sb
implements the Sequential Bifurcations screening
method (Bettonvil and Kleijnen 1996).
1 2 3 4 5 6 7 8 9 
p 
number of factors. 
sign 
a vector fo length 
interaction 
a boolean, 
x 
a list of class 
y 
a vector of model responses. 
i 
an integer, used to force a wanted bifurcation instead of that proposed by the algorithm. 
... 
not used. 
The model without interaction is
Y = beta_0 + sum_{i=1}^p beta_i X_i
while the model with interactions is
Y = beta_0 + sum_{i=1}^p beta_i X_i + sum_{1 <= i < j <= p} gamma_{ij} X_i X_j
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.
sb
returns a list of class "sb"
, containing all
the input arguments detailed before, plus the following components:
i 
the vector of bifurcations. 
y 
the vector of observations. 
ym 
the vector of mirror observations (model with interactions only). 
The groups effects can be displayed with the print
method.
Gilles Pujol
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  # 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)

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