View source: R/uncertaintyBudget.default.R
| uncertaintyBudget.default | R Documentation |
Creates an uncertainty budget.
## Default S3 method:
uncertaintyBudget(x, y, ...)
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 |
Creates an uncertainty budget object
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
none
Hugo Gasca-Aragon
Maintainer: Hugo Gasca-Aragon <hugo_gasca_aragon@hotmail.com>
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.
uncertaintyBudget, uncertainty, print.uncertaintyBudget
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
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