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, k.delta = "usual", min = 0, max = 1, scale = TRUE, nboot = 0, conf = 0.95, ...) ## S3 method for class 'morris': compute(sa, y = 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. |
k.delta |
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. |
nboot |
the number of bootstrap replicates. |
conf |
the confidence level for bootstrap confidence intervals. |
sa |
the sensitivity analysis object. |
y |
the response. |
... |
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.
The number of levels is the same for each space coordinate. Then
levels
must be a single integer.
k.delta
is such that:
Delta_i = k.delta
* ( max
_i - min
_i ) / ( k - 1 )
where k is the number of levels (levels
). If
k.delta
is given as "usual"
and k is even,
then Delta is the value recommended by Morris:
Delta_i = ( max
_i - min
_i ) * k / ( 2 * ( k - 1 ) )
min
and max
are boundaries of the region of
experimentation. They can be single values (the same for each
factor) or vectors.
morris
returns an object of class "morris"
.
An object of class "morris"
is a list containing the following
components:
model |
the model. |
levels |
the number of levels of the design grid. |
r |
the number of repetitions of the design. |
delta |
the value of Delta. |
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 before computing the elementary effects. |
nboot |
the number of bootstrap replicates. |
conf |
the confidence level for bootstrap confidence intervals. |
D |
the successive diagonal matrices composed of equiprobable +1 and -1. |
P |
the successive random permutation matrices. |
x |
the design of experiments (input sample). |
y |
the response. |
mu |
the estimations of the mu* index. |
sigma |
the estimations of the sigma index. |
call |
the matched call. |
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)