sensitivity (version 1.2)

morris: The Morris OAT Screening Method

Description

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 $\sigma$.

Usage

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)

Arguments

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.

Value

• 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.

Details

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: $$\Delta_i = \textrm{k.delta} \frac{\textrm{max}_i-\textrm{min}_i}{k-1}$$ 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: $$\Delta_i = (\textrm{max}_i-\textrm{min}_i) \frac{k}{2(k-1)}$$

min and max are boundaries of the region of experimentation. They can be single values (the same for each factor) or vectors.

References

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.

See Also

sensitivity compute

Examples

# Test case : the non-monotonic function of Morris

sa <- morris(model = morris.fun, factors = 20, levels = 4, r = 4)
print(sa)
plot(sa)