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. One should note that all the responses must be computed by a
single call to the model function (then, the model can be vectorized).
If the model is external it does not have to be interfaced with R: the
user won't have to give a model to the function. Then, it will
stop just after generating the DOE. The responses have to be computed
by the user, whithin Ror not. Calculations will start again when the
user gives the corresponding responses (via the tell function).
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 (or not), and the result is loaded by the user
in the y variable
(step 5) tell(sa, y)
method should be the name of a SA function, such as
srcpcc, morris, sobol,
or fast. These function create the object sa of
class "srcpcc", "morris", "sobol", 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)
srcpcc
morris
sobol
fast
tell
testmodels