fast (version 0.64)

sensitivity: Calculate sensitivity according to the FAST algorithm

Description

sensitivity calculates the sensitivity from a series of model outputs (x) according to the FAST alogrithm.

Usage

sensitivity(x, numberf, order = 4, make.plot = FALSE, show.legend  = TRUE, plot.max = max(ff[-1]), include.total.variance  = FALSE, cukier = TRUE, names = paste(sep = "", "P", 1:numberf), main = "", xlab = "frequency", ylab = "Fourier Coef", pch = rep(0, numberf), col = (1:numberf) + 1, reorder = 1:numberf, ...)

Arguments

x
A vector of model outputs where parameters vary between runs according to the fast algorithm.
numberf
Number of parameters varied.
order
Order of parameter frequency independence (see Cukier)
make.plot
plot the Fourier spectrum?
plot.max
xmax in the spectrum
include.total.variance
include the sum of all variances in the result list.
pch
see par
col
see par
...
Additional parameters passed to plot
xlab
see par
ylab
see par
main
Title for the plot
show.legend
Boolean indicating whether to plot the legend
names
A vector of parameter names.
cukier
boolean: Calculate FAST-parameters according to Cukier 1975 or McRae 1982
reorder
A vector of indices that allows to use a different order for the parameters. This is important to check effects of the sampling scheme on results. Use the same as for fast_parameters

Value

A list of the partial variance accounted for by each parameter.

References

Reusser, Dominik E., Wouter Buytaert, and Erwin Zehe. "Temporal dynamics of model parameter sensitivity for computationally expensive models with FAST (Fourier Amplitude Sensitivity Test)." Water Resources Research 47 (2011): W07551.

CUKIER, R. I.; SCHAIBLY, J. H. & SHULER, K. E. Study Of Sensitivity Of Coupled Reaction Systems To Uncertainties In Rate Coefficients .3. Analysis Of Approximations Journal Of Chemical Physics, 1975 , 63 , 1140-1149

McRae, G.; Tilden, J. & Seinfeld, J. Global sensitivity analysis - a computational implementation of the Fourier amplitude sensitivity test (FAST) Comput. Chem. Eng., 1982 , 6 , 15-25

See Also

S, fast

Examples

Run this code
example_model1<-function(p,x){
   return(p[1]*x+p[2]*(1-x))
}
paras<-fast_parameters(min=c(0,0,0),max=c(1,2,2))
paras
model_results <- apply(paras, 1, example_model1, x=0.5)
model_results
sensitivity <- sensitivity(x=model_results, numberf=3, make.plot=TRUE)
sensitivity

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