The functions of this package generate the design of experiments (depending on the method of analysis) and compute the sensitivity indices based on the model inputs and outputs. All sensitivity indices can be estimated with the bootstrap technique which allows to estimate the bias, and basic bootstrap confidence intervals. Text and graphical outputs display the results of the analysis.
The
(step 1) The model can be internal or external to R. If internal, it
can be a function that takes an unique matrix or data.frame
parameter and returns a numeric vector. It can also be a
predictor, i.e. an object wich can be called with the predict
method. If the model is external it does not have to be
interfaced with R: the program will stop after generating the DOE, and
calculations will start again when one gives the corresponding
responses.
The four next steps depend upon the type of the model:
For internal models:
(step 2-5) sa <- method(model, parameters...)
For external models:
(step 2-3) sa <- method(model = NULL, parameters...)
(step 4) external to R, and the result is loaded in the y variable
(step 5) compute(sa, y)
method should be the name of a SA function, such as
linsa, morris, sobol,
sobol.sal02 or fast. These function create
the object sa of class "linsa", "morris",
"sobol", "sobol.sal02" or "fast". For further
information on these function, see the corresponding documentation.
Finally, for displaying the results of the analysis:
(step 6) print(sa); plot(sa)
linsa
morris
sobol
sobol.sal02
fast
compute
testmodels