morris is the implementation of the Morris OAT Screening
method. This function generates the Morris design of experiments and
computes the measures of sensitivity $\mu^*$ and
$\sigma$.morris(model = NULL, factors, levels, R, jump = NULL,
min = 0, max = 1, scale = TRUE, optim = NULL, ...)TRUE, the input and output data are
scaledmodel which are passed
unchanged each time it is calledmorris returns an object of class "morris".
An object of class "morris" is a list containing the following
components:model is a function or a predictor (a class with a
predict method) computing the response y based on the
sample given by x. If no model is specified, the indices will be
computed when one gives the response. factors could either be a single number or a vector of
character strings.
The number of levels is not necessary the same for each space
coordinate. It is the case when levels is a single integer.
min and max are boundaries of the region of
experimentation. They can be single values (the same for each
factor) or vectors.
jump is such that:
$$\Delta_i = \texttt{jump}_i \times \frac{\texttt{max}_i-\texttt{min}_i}{\texttt{levels}_i-1}$$
If jump is given as NULL and the number of levels is
even (for each component), then jump has the value recommended
by Morris:
$\texttt{jump} = \texttt{levels} / 2$.
If jump is a single value, then it is taken the same for each
coordinate.
Morris, M. D., 1991, Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 161--174.
# Test case : the non-monotonic function of Morris
sa <- morris(model = morris.fun, factors = 20, levels = 4, R = 4)
print(sa)
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