| 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)