Description Usage Arguments Details Value Note References See Also Examples
The gsi function implements the calculation of Generalised Sensitivity Indices. This method allows to compute a synthetic Sensitivity Index for the dynamic or multivariate models by using factorial designs and the MANOVA decomposition of inertia. It computes also the Sensitivity Indices on principal components
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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 :
|
inertia |
cumulated proportion of inertia (a scalar < 1) to be explained by the selected Principal components OR number of PCs to be used (e.g 3) |
normalized |
logical value. TRUE (default) computes a normalized Principal Component analysis. |
cumul |
logical value. If TRUE the PCA will be done on the cumulative outputs |
simulonly |
logical value. If TRUE the program stops after calculating the design and the model outputs |
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 |
If factors is a list of factors, the gsi function generates a complete factorial design. If it is a data.frame, gsi expects that each column is associated with an input factor.
gsi returns a list of class "gsi", containing all the input arguments detailed before, plus the following components:
X |
a data.frame containing the experimental design (input samples) |
Y |
a data.frame containing the output matrix (response) |
H |
a data.frame containing the principal components |
L |
a data.frame whose columns contain the basis eigenvectors (the variable loadings) |
lambda |
the variances of the principal components |
inertia |
vector of inertia percentages per PCs and global criterion |
cor |
a data.frame of correlation between PCs and outputs |
SI |
a data.frame containing the Sensitivity Indices (SI) on PCs and the Generalised SI (GSI) |
mSI |
a data.frame of first order SI on PCs and first order GSI |
tSI |
a data.frame containing the total SI on PCs and the total GSI |
iSI |
a data.frame of interaction SI on PCs and interaction GSI |
pred |
a data.frame containing the output predicted by the metamodel arising from the PCA and anova decompositions |
residuals |
a data.frame containing the residuals between actual and predicted outputs |
Rsquare |
vector of dynamic coefficient of determination |
Att |
0-1 matrix of association between input factors and factorial terms in the anovas |
scale |
logical value, see the arguments |
normalized |
logical value, see the arguments |
cumul |
logical value, see the arguments |
call.info |
a list containing informations on the process (reduction, analysis, fct, call) |
inputdesign |
either the input data.frame or the sensitivity object used |
outputs |
a list of results on each output variable |
...
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.
M. Lamboni, D. Makowski and H. Monod, 2009. Multivariate global sensitivity analysis for dynamic crop models. Field Crops Research, volume 113. pp. 312-320
M. Lamboni, D. Makowski and H. Monod, 2009. Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models. Submitted to Reliability Engineering and System Safety.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | # Test case : the Winter Wheat Dynamic Models (WWDM)
# input factors design
data(biomasseX)
# input climate variable
data(Climat)
# output variables (precalculated to speed up the example)
data(biomasseY)
#
GSI <- gsi(2, biomasseY, biomasseX, inertia=3, normalized=TRUE, cumul=FALSE,
climdata=Climat)
summary(GSI)
print(GSI)
plot(x=GSI, beside=FALSE)
#plot(GSI, nb.plot=4) # the 'nb.plot' most influent factors
# are represented in the plots
#plot(GSI,nb.comp=2, xmax=1) # nb.comp = number of principal components
#plot(GSI,nb.comp=3, graph=1) # graph=1 for first figure; 2 for 2nd one
# and 3 for 3rd one; or 1:3 etc.
#graph.bar(GSI,col=1, beside=F) # sensitivity bar plot on the first PC
#graph.bar(GSI,col=2, xmax=1) #
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