uncertainty.default {uncertainty} | R Documentation |
Generic function for calling an uncertainty object
Description
Creates an uncertainty estimation object using a measurand model and an uncertainty budget object
Usage
## Default S3 method:
uncertainty(x, y, ...)
Arguments
x |
an uncertainty budget object |
y |
a list with the measurand description and selected estimation method, the measurand description includes: measurand_name, measurand_model, measurand_label, alpha (significance level), method and method parameters. the valid methods are: GFO, GSO, MC, Kragten. currently the only method parameter implemented is the number of simulated samples (B) for the method MC. |
... |
additional parameters |
Details
Creates an uncertainty estimation object. Uses an uncertainty budget object to estimate the expected value and uncertainty of a measurand by applying a selected estimation method.
Value
An uncertainty estimation object with the structure:
method
selected estimating method,
call
current call invocation,
uncertaintyBudget
an uncertainty budget object,
measurand
name, label, model describing the measurand,
mean
the estimated mean,
sd
the estimated standard deviation,
u
the estimated standard uncertainty,
alpha
the significante level used in the estimation,
dof
the estimated degrees of freedom,
U
the estimated expanded uncertainty,
lcl
the lower confidence interval,
ucl
the upper confidence interval,
variables
a vector with the input quantities,
contribution
a vector with the uncertainty contributions,
cor.contribution
the uncertainty contribution due to overall correlation,
partial
a vector of the partial derivatives of the measurand.model with respect to each input quantity,
coeff
a vector of the sensibility coefficients for each input quantity.
Note
none
Author(s)
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <hugo_gasca_aragon@hotmail.com>
References
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
See Also
uncertainty
, uncertaintyBudget.default
, print.uncertainty
, plot.uncertainty
, summary.uncertainty
Examples
# create an uncertainty budget
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), dof=c(10,10),
label=c("x[0]", "x[1]"), distribution=c("normal","normal")),
y=cor.mat)
u.budget
# estimate the measurand uncertainty using an uncertainty budget,
# a measurand definition and a selected estimating method.
GFO.res<- uncertainty(x=u.budget,
y=list(measurand_name="ratio.GFO", measurand_label="ratio[GFO]",
measurand_model="x0/x1", method="GFO", alpha=0.05))
GFO.res