morris {sensitivity} | R Documentation |
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, ...)
model |
the model |
factors |
the number of factors, or their names |
levels |
the number of levels of the design grid |
R |
the number of repetitions of the design, i.e. the number of elementary effect computed per factor |
jump |
the ‘grid jump’ coefficient |
min |
the minimum values for the factors |
max |
the maximum values for the factors |
scale |
logical. If TRUE , the input and output data are
scaled |
optim |
optimization of the design for better coverage of the space (cf Campolongo 2005), not documented yet (for informations feel free to ask the maintainer) |
... |
any other arguments for model which are passed
unchanged each time it is called |
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] = jump[i] * ( max[i] - min[i] ) / ( 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:
jump = levels / 2.
If jump
is a single value, then it is taken the same for each
coordinate.
morris
returns an object of class "morris"
.
An object of class "morris"
is a list containing the following
components:
x |
the design of experiments (input sample) |
y |
the response |
ee |
the matrix of the elementary effects |
mu |
the estimations of the mu* index |
sigma |
the estimations of the sigma index |
The number of model evaluations is (p + 1) * R where p is the number of factors.
Saltelli, A., Chan, K. and Scott, E. M., 2000, Sensitivity analysis. Wiley.
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) plot(sa)