xSample: Generate Sample

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

Description

Generate Monte Carlo sample from prescribed PDF. All PDFs are parameterized by their mean and standard deviation (if needed).

Usage

xSample(M = 1000, x.mu, x.u, x.pdf, x.df, x.cor = diag(length(x.mu)),
  x.cov = NULL, tol = sqrt(.Machine$double.eps))

Arguments

M	Sample size.
x.mu	Named vector of mean values with names compatible with fExpr.
x.u	Named vector of standard uncertainty values (one of {x.u, x.cov} mandatory).
x.pdf	Named vector of pdf types (see below).
x.df	Named vector of degrees of freedom for x.pdf.
x.cor	Named correlation matrix between model parameters.
x.cov	Named variance/covariance matrix between model parameters (one of {x.u, x.cov} mandatory).
tol	Numeric tolerance level to check positive-definiteness of x.cor or x.cov.

Details

Available distributions:

• 'delta' Dirac delta distribution (for constants); args = x.mu

• 'norm' Normal; args = x.mu, x.u

• 'tnorm' Truncated normal (positive values); args = x.mu, x.u

• 'lnorm' Lognormal; args = x.mu, x.u

• 'stud' Student's T; args = x.mu, x.u, x.df

• 'unif' Uniform; args = x.mu, x.u

• 'triangle' Symmetric triangular; args = x.mu, x.u

• 'arcsine' Arcsine derivative; args = x.mu, x.u

• 'pois' Poisson; args = x.mu

Correlation between variables is described by matrices x.cov or x.cor, and enforced by Gaussian copula.

Value

X A M*N matrix of M values for N variables.

Examples

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')
X=xSample(M=1000,x.mu,x.u,x.pdf)
SAPlot(X)

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