sensitivity-package {sensitivity} | R Documentation |
Methods and functions for global sensitivity analysis.
The sensitivity package implements some global sensitivity analysis methods:
src
), PCC and PRCC (pcc
).
morris
).
sb
).
sobol
), and Saltelli's
scheme (2002) to compute first order and total indices
with a reduced cost (sobol2002
).
fast99
).
Moreover, some utilities are provided: standard test-cases
(testmodels
) and template file generation
(template.replace
).
The sensitivity package works either on R models than on external models (such as executables).
R models must be functions or objects that have a predict
method, such as lm
objects. Models defined as functions will
be called once with an expression of the form y <- f(X)
where
X
is the design of experiments, i.e. a data.frame
with
p
columns (the input factors) and n
lines (each, an
experiment), and y
is the vector of length n
of the
model responses (we say that such functions are vectorized).
If the model is external to R, for instance a computational code, it
must be analyzed with the decoupled approach, see
decoupling
. This approach can also be used on R models
that doesn't fit the specifications.
A. Saltelli, K. Chan and E. M. Scott eds, 2000, Sensitivity Analysis, Wiley.