dynsi: Dynamic Sensitivity Indices: DSI

In multisensi: Multivariate Sensitivity Analysis

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 "A+B+c+A:B" OR an integer equal to the maximum interaction order in the sensitivity model.
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 factor.example <- list(A=c(0,1),B=c(0,1,4)).
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 "exc.ssc".
...	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

gsi, multisensi

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)    #
  

multisensi documentation built on May 2, 2019, 2:14 p.m.