uncertainty (version 0.3.0)

uncertaintyBudget.default: Generic function for calling an uncertainty budget object

Description

Creates an uncertainty budget.

Usage

# S3 method for default
uncertaintyBudget(x, y, ...)

Value

An uncertainty budget object with attributes:

name the name of each input quantity, this is the identifier used in the measurement model computation

mean the mean value of each input quantity

u the uncertainty of each input quantity

unit the measurement unit of each input quantity

dof the degrees of freedom of each input quantity

type the type of source "A"=experimental, "B"=other means

label the label of each input quantity, used for displaying and plotting

description the full description of the input quantity

distribution the distribution of each input quantity, valid values are (bernoulli, beta, binomial, cuachy, chisq, exp, f, gamma, lognormal, poission, normal, unif, t, traingular, weibull, arcsine)

cor the correlation matrix among the input quantities

Arguments

x

a list with the vector entries name, label, mean, unit, u(uncertainty), type, description, distribution and dof, one for each input quantity.

y

a correlation matrix of the input quantities, interpreted in the same order of input quantities as the vector name

...

additional parameters

Author

Hugo Gasca-Aragon

Maintainer: Hugo Gasca-Aragon <[e-mail]>

Details

Creates an uncertainty budget object

References

JCGM 200:2012. International vocabulary of metrology—Basic and general concepts and associated terms (VIM)

JCGM 100:2008. Guide to the expression of uncertainty of measurement

JCGM 100:2005. Supplement 1 Propagation of distributions usign a Monte Carlo method

EURACHEM/CITAC Guide CG 4. Quantifying Uncertainty in Analytical Measurement

Becker, R.A., Chambers, J.M. and Wilks, A.R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

uncertaintyBudget, uncertainty, print.uncertaintyBudget

Examples

require(mvtnorm)

cor.mat <- matrix(c(1, -0.7, -0.7, 1), 2, 2)

u.budget <- uncertaintyBudget(x = list(name = c("x0", "x1"), 
	mean = c(10, 20), u = c(1, 5), unit = c("kg", "kg"), dof = c(10, 10),
	label = c("x[0]", "x[1]"), type = c("A", "A"),
	description = c("measurand mass", "sample mass"),
	distribution = c("normal", "normal")),
  y = cor.mat)
u.budget