vgSA: Variance Gradient Sensitivity Analysis

In ppernot/rgumlib: Uncertainty Propagation (GUM & GUM-Supp1) and Sensitivity Analysis

Description

Uses Monte Carlo sample to generate Variance Gradients estimates. Only for independent input parameters.

Usage

vgSA(fExpr, x.mu, x.u, X, Y, budgetTable = TRUE, silent = FALSE)

Arguments

fExpr	An expression or a function object.
x.mu	Named vector of mean values with names compatible with fExpr.
x.u	Named vector of standard uncertainty values.
X	A MxN matrix of M values for N input variables.
Y	A vector of values corresponding to X.
budgetTable	Flag to compute the budget table.
silent	Flag to run without printout.

Value

A list containing:

vg	(vector) variance gradients
budget	(dataframe) VG budget table, mostly to be printed

References

Mark Campanelli, Raghu Kacker and R\"udiger Kessel (2013) Variance gradients and uncertainty budgets for nonlinear measurement functions with independent inputs. Meas. Sci. Technol. 24:25002.

Examples

fExpr = expression(x1+x2)
x.mu = c(1,1); names(x.mu)=c('x1','x2')
x.u = c(0.1,0.1); names(x.u)=c('x1','x2')
x.pdf = c('unif','triangle'); names(x.pdf)=c('x1','x2')
S=gumS1(fExpr,x.mu,x.u,x.pdf,x.df=NULL,nrunMax=1000)
vgSA(fExpr,x.mu,x.u,S$X,S$Y)

ppernot/rgumlib documentation built on May 25, 2019, 11:24 a.m.