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Outputs the global sensitivity scores SI and SIT, calculated by the Sobol-Homma-Saltelli method

gsa_saltelli.Rd

M1, M2, and N are matrices prepared by uqsa::saltelli_prior(). The parameters (rows) from these matrices need to be simulated (using any method), to obtain fM1, fM2 and fN.

Usage

gsa_saltelli(fM1, fM2, fN, subtractMean = TRUE)

Arguments

fM1

output (f)unction values for M1, nSamples × nOuts

fM2

output (f)unction values for M2, nSamples × nOuts

fN

output (f)unction values for N, nSamples × nOuts × nPars

Value

a list with sensitivity indices $SI and total sensitivities $SIT

Details

These matrices are shaped similarly to M1, M2 and N respectively, but now the parameters are replaced by the effects they have on a observable of interest (the output). It can be the vector valued output at a specific (single) time-point or a scalar output at different time-points.

See Geir Halnes et al. (Halnes, Geir, et al. J. comp. neuroscience 27.3 (2009): 471.

Examples

m <- model_from_tsv(uqsa_example("AKAR4"))
o <- write_and_compile(as_ode(m))
ex <- experiments(m,o)
s <- simulator.c(ex[1],o)
p0 <- values(m$Parameter)
rprior <- rUniformPrior(p0/2,p0*2)
SP <- saltelli_prior(1000,rprior)
fM1 <- t(s(t(SP$M1))[[1]]$func[1,,])
fM2 <- t(s(t(SP$M2))[[1]]$func[1,,])
fN <- lapply(asplit(SP$N,3),\(N) t(s(t(N))[[1]]$func[1,,]))
fN <- simplify2array(fN)
GSA <- gsa_saltelli(fM1,fM2,fN)
print(names(GSA))
#> [1] "SI"  "SIT"
cat(
  "average relative senitivity S(p1) / S(p2): ",
  mean(abs(GSA$SI[,1]/GSA$SI[,2]),na.rm=TRUE)
)
#> average relative senitivity S(p1) / S(p2):  3091.02