dynsi {multisensi} | R Documentation |
Dynamic Sensitivity Indices: DSI
Description
dynsi implements the Dynamic Sensitivity Indices. This method allows to compute classical Sensitivity Indices on each output variable of a dynamic or multivariate model by using the ANOVA decomposition
Usage
dynsi(formula, model, factors, cumul = FALSE, simulonly=FALSE,
nb.outp = NULL, Name.File=NULL, ...)
Arguments
formula |
ANOVA formula like |
model |
output data.frame OR the name of the R-function which calculates the model output. The only argument of this function must be a vector containing the input factors values. |
factors |
input data.frame (the design) if model is a data.frame
OR a list of factors levels such as
|
cumul |
logical value. If TRUE the sensitivity analysis will be done on the cumalative outputs. |
simulonly |
logical value. If TRUE the program stops after calculating the design and the model outputs. |
nb.outp |
The first nb.outp number of model outputs to be considered. If NULL all the outputs are considered. |
Name.File |
optional name of a R script file containing the
R-function that calculates the simulation model. e.g |
... |
possible fixed parameters of the model function. |
Details
If factors
is a list of factors, the dynsi function generates a complete
factorial design. If it is a data.frame, dynsi expects that each column is
associated with an input factor.
Value
dynsi returns a list of class "dynsi" containing the following components:
X |
a data.frame containing the experimental design (input samples) |
Y |
a data.frame containing the output (response) |
SI |
a data.frame containing the Sensitivity Indices (SI) on each output variable of the model and the Generalised SI (GSI) |
mSI |
a data.frame of first order SI on each output variable and first order GSI |
tSI |
a data.frame containing the total SI on each output variable and the total GSI |
iSI |
a data.frame of interaction SI on each output variable and interaction GSI |
Att |
0-1 matrix of association between input factors and factorial terms in the anovas |
call.info |
a list containing informations on the process (reduction=NULL, analysis, fct, call) |
inputdesign |
either the input data.frame or the sensitivity object used |
outputs |
a list of results on each output variable |
...
Note
This function can now be replaced by a call to the multisensi
function. It is kept for compatibility with Version 1 of the multisensi package.
References
M. Lamboni, D. Makowski and H. Monod, 2009. Multivariate global sensitivity analysis for dynamic crop models. Field Crops Research, 113, 312-320.
A. Saltelli, K. Chan and E. M. Scott eds, 2000. Sensitivity Analysis. Wiley, New York.
See Also
Examples
# Test case : the Winter Wheat Dynamic Models (WWDM)
# input factors design,
data(biomasseX)
# input Climate variables
data(Climat)
# output variables (precalculated to speed up the example)
data(biomasseY)
#
DYNSI <- dynsi(2, biomasseY, biomasseX)
summary(DYNSI)
print(DYNSI)
plot(DYNSI, color=heat.colors)
#graph.bar(DYNSI,col=1, beside=F) # sensitivity bar plot
# for the first output (col=1)
#graph.bar(DYNSI,col=2, xmax=1) #