sb implements the Sequential Bifurcations screening method (Bettonvil and Kleijnen 1996).

sb is located in package sensitivity. Please install and load package sensitivity before use.

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, ...)

install.packages("sensitivity", repo="http://cran.r-project.org", dep=T)
library(sensitivity)
# 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)

Return 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.

Details: 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.