| Title: | Uncertainty Estimation and Contribution Analysis |
|---|---|
| Description: | Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis. |
| Authors: | Hugo Gasca-Aragon |
| Maintainer: | Hugo Gasca-Aragon <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.2.0 |
| Built: | 2025-01-10 02:59:40 UTC |
| Source: | https://github.com/cran/uncertainty |
Uncertainty estimation and contribution analysis implemented by 4 methods: the Gaussian method of first, the Gaussian method of second order, the Kragten numerical method and the Monte Carlo simulation method
| Package: | uncertainty |
| Type: | Package |
| Version: | 0.1.1 |
| Date: | 2014-06-12 |
| License: | GPL (>=2) |
Define an "uncertainty budget" object, including all the involved variables. Then estimate the "uncertainty" object by defining a measurand model, using the "uncertainty budget" and applying an estimation method. Print or plot the measurand estimates or create a "summary uncertainty" object to print or plot the uncertainty contributions to the measurand model.
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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, print.uncertaintyBudget, uncertainty, print.uncertainty, plot.uncertainty, summary.uncertainty, print.summary.uncertainty, plot.summary.uncertainty
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), dof=c(10,10), label=c("x[0]", "x[1]"), distribution=c("normal","normal")), y=cor.mat) u.budget ## Gaussian first order estimates GFO.res<- uncertainty(x=u.budget, y=list(measurand_name="ratio.GFO", measurand_label=expression(ratio[GFO]), measurand_model="x0/x1", method="GFO", alpha=0.05)) contr.GFO<- summary.uncertainty(GFO.res) ## Monte Carlo estimates MC.res<- uncertainty(x=u.budget, y=list(measurand_name="ratio.MC", measurand_label=expression(ratio[MC]), measurand_model="x0/x1", method="MC", alpha=0.05, B=1e5)) contr.MC<- summary.uncertainty(MC.res) ## print the estimates MC.res GFO.res ## print the uncertainty summary contr.MC contr.GFO ## Displaying both estimated distributions ## Not run: plot(MC.res, col=4, xlab=MC.res$measurand_model) plot(GFO.res, lty=2, col=2, add=T) legend(0.7, 2.5, legend=c("Monte Carlo", "Gaussian First Order"), lty=c(1,2), col=c(4,2), lwd=2, bg="white") ## End(Not run) ## Display both uncertainty summaries ## Not run: barplot(cbind(contr.GFO$budget$contrib, contr.MC$budget$contrib), beside=TRUE, horiz=TRUE, main="Uncertainty contribution by method", xlab="percent Variance", names.arg=c(GFO.res$measurand_label, MC.res$measurand_label)) ## End(Not run) ########################## ## Example H.1 from GUM ## ########################## # define the uncertainty budget u.budget<- uncertaintyBudget( x=list( name=c("lambda.s", "alpha.s", "theta.bar", "Delta", "delta.alpha", "delta.theta", "d.bar", "d.cr", "d.cnr"), label=c("lambda[s]", "alpha[s]", "bar(theta)", "Delta", "delta[alpha]", "delta[theta]", "bar(d)", "d[cr]", "d[cnr]"), mean=c(50.000623,11.5e-6,-1e-1, 0, 0, 0, 2.15e-4, 0, 0), units=c("mm", "oC^-1","oC","oC", "oC^-1", "oC", "mm", "mm", "mm"), u=c(25e-6, 1.2e-6, 0.2, 0.35, 0.58e-6, 0.029, 5.8e-6, 3.9e-6, 6.7e-6), distribution=c("t","unif","unif","arcsine","unif","unif","t","t","t"), dof=c(18, 1, 1, 1, 50, 2, 24, 5, 8) ), y=diag(1, 9) ) # define the measurand measurand_name<- "lambda" measurand_label<- "lambda" measurand_model<- paste("(lambda.s*(1+alpha.s*(theta.bar+Delta+delta.theta))", "+d.bar+d.cr+d.cnr)/(1+(alpha.s+delta.alpha)*(theta.bar+Delta))", sep="") # estimate the measurand using the Gaussian First Order method (GUM) u.GFO<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="GFO" ) ) u.GFO # same result as reported in Table H.1 # estimate the measurand using the Gaussian Second Order method u.GSO<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="GSO" ) ) u.GSO # same results as reported in section H.1.6, U(99) = 93 nm, # the difference is due to rounding error. # u = 34 nm, but dof are updated to 21 instead of keeping 16. # estimate the measurand using the Monte Carlo method (GUM supplement 1) u.MC<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="MC", B=1e6 ) ) u.MC # this result is not reported in the GUM # estimate the measurand using the Kragten method u.Kragten<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="Kragten" ) ) u.Kragten # same as GFO resultsrequire(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), dof=c(10,10), label=c("x[0]", "x[1]"), distribution=c("normal","normal")), y=cor.mat) u.budget ## Gaussian first order estimates GFO.res<- uncertainty(x=u.budget, y=list(measurand_name="ratio.GFO", measurand_label=expression(ratio[GFO]), measurand_model="x0/x1", method="GFO", alpha=0.05)) contr.GFO<- summary.uncertainty(GFO.res) ## Monte Carlo estimates MC.res<- uncertainty(x=u.budget, y=list(measurand_name="ratio.MC", measurand_label=expression(ratio[MC]), measurand_model="x0/x1", method="MC", alpha=0.05, B=1e5)) contr.MC<- summary.uncertainty(MC.res) ## print the estimates MC.res GFO.res ## print the uncertainty summary contr.MC contr.GFO ## Displaying both estimated distributions ## Not run: plot(MC.res, col=4, xlab=MC.res$measurand_model) plot(GFO.res, lty=2, col=2, add=T) legend(0.7, 2.5, legend=c("Monte Carlo", "Gaussian First Order"), lty=c(1,2), col=c(4,2), lwd=2, bg="white") ## End(Not run) ## Display both uncertainty summaries ## Not run: barplot(cbind(contr.GFO$budget$contrib, contr.MC$budget$contrib), beside=TRUE, horiz=TRUE, main="Uncertainty contribution by method", xlab="percent Variance", names.arg=c(GFO.res$measurand_label, MC.res$measurand_label)) ## End(Not run) ########################## ## Example H.1 from GUM ## ########################## # define the uncertainty budget u.budget<- uncertaintyBudget( x=list( name=c("lambda.s", "alpha.s", "theta.bar", "Delta", "delta.alpha", "delta.theta", "d.bar", "d.cr", "d.cnr"), label=c("lambda[s]", "alpha[s]", "bar(theta)", "Delta", "delta[alpha]", "delta[theta]", "bar(d)", "d[cr]", "d[cnr]"), mean=c(50.000623,11.5e-6,-1e-1, 0, 0, 0, 2.15e-4, 0, 0), units=c("mm", "oC^-1","oC","oC", "oC^-1", "oC", "mm", "mm", "mm"), u=c(25e-6, 1.2e-6, 0.2, 0.35, 0.58e-6, 0.029, 5.8e-6, 3.9e-6, 6.7e-6), distribution=c("t","unif","unif","arcsine","unif","unif","t","t","t"), dof=c(18, 1, 1, 1, 50, 2, 24, 5, 8) ), y=diag(1, 9) ) # define the measurand measurand_name<- "lambda" measurand_label<- "lambda" measurand_model<- paste("(lambda.s*(1+alpha.s*(theta.bar+Delta+delta.theta))", "+d.bar+d.cr+d.cnr)/(1+(alpha.s+delta.alpha)*(theta.bar+Delta))", sep="") # estimate the measurand using the Gaussian First Order method (GUM) u.GFO<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="GFO" ) ) u.GFO # same result as reported in Table H.1 # estimate the measurand using the Gaussian Second Order method u.GSO<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="GSO" ) ) u.GSO # same results as reported in section H.1.6, U(99) = 93 nm, # the difference is due to rounding error. # u = 34 nm, but dof are updated to 21 instead of keeping 16. # estimate the measurand using the Monte Carlo method (GUM supplement 1) u.MC<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="MC", B=1e6 ) ) u.MC # this result is not reported in the GUM # estimate the measurand using the Kragten method u.Kragten<- uncertainty( x=u.budget, y=list(measurand_name=measurand_name, measurand_label=measurand_label, measurand_model=measurand_model, alpha=0.01, method="Kragten" ) ) u.Kragten # same as GFO results
Builds a barplot with a bar for each source of uncertainty. If correlation is present then an additional entry is added. The current metric used to display is When correlation is present its contribution may be negative.
## S3 method for class 'summary.uncertainty' plot(x, y = NULL, ...)## S3 method for class 'summary.uncertainty' plot(x, y = NULL, ...)
x |
an uncertainty summary object |
y |
not used. |
... |
additional parameters to customize the plot |
none
None (invisible NULL)
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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
# 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) # 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)) # create an uncertainty summary object GFO.sum<- summary(GFO.res) # display the chart ## Not run: plot(GFO.sum)# 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) # 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)) # create an uncertainty summary object GFO.sum<- summary(GFO.res) # display the chart ## Not run: plot(GFO.sum)
Plot a probability density function attributed to the measurand, depending on the selected method to estimate the uncertainty.
## S3 method for class 'uncertainty' plot(x, y = NULL, xlab = parse(text = x$measurand_label), main = "", ylab = "Probability density", from = x$mean - 4 * x$u, to = x$mean + 4 * x$u, lwd = 2, add = FALSE, ...)## S3 method for class 'uncertainty' plot(x, y = NULL, xlab = parse(text = x$measurand_label), main = "", ylab = "Probability density", from = x$mean - 4 * x$u, to = x$mean + 4 * x$u, lwd = 2, add = FALSE, ...)
x |
An uncertainty object |
y |
not used, exists only for compatibility with the S3 generic function. |
xlab |
string or expression, label for the x-axis. |
main |
string or expression, label for the plot. |
ylab |
string or expression, label for the y-axis. |
from |
numeric, lower value of the x-axis to display. |
to |
numeric, upper valur of the x-axis to display. |
lwd |
numeric, line width. |
add |
logic, decides to add the curve into an existing plot or to create a new plot. |
... |
additional parameters. |
none
None (invisible NULL)
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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
# 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) # 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)) # plot the estimated pdf of the measurand ## Not run: plot(GFO.res)# 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) # 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)) # plot the estimated pdf of the measurand ## Not run: plot(GFO.res)
For each input quantity (source of uncertainty) it shows the uncertainty contribution, measured in percent of variance of the measurand model.
## S3 method for class 'summary.uncertainty' print(x, ...)## S3 method for class 'summary.uncertainty' print(x, ...)
x |
An uncertainty summary object |
... |
Additional parameters |
none
None (invisible NULL)
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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.
# 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 # create an uncertainty summary object GFO.sum<- summary(GFO.res) # implicit call to the print method GFO.sum # same as print(GFO.sum) # uncertainty summary structure attributes(GFO.sum)# 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 # create an uncertainty summary object GFO.sum<- summary(GFO.res) # implicit call to the print method GFO.sum # same as print(GFO.sum) # uncertainty summary structure attributes(GFO.sum)
Displays the estimated value of the measurand, its standard deviation, its standard uncertainty, the degrees of freedom and the significance level and an CI with that significance level.
## S3 method for class 'uncertainty' print(x, ...)## S3 method for class 'uncertainty' print(x, ...)
x |
an uncertainty object |
... |
additional parameters |
none
None (invisible NULL)
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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.
# 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)) # implicit call to print method GFO.res # same as print(GFO.res) # structure of an uncertainty estimation object attributes(GFO.res)# 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)) # implicit call to print method GFO.res # same as print(GFO.res) # structure of an uncertainty estimation object attributes(GFO.res)
Print the description of each uncertainty source
## S3 method for class 'uncertaintyBudget' print(x, ...)## S3 method for class 'uncertaintyBudget' print(x, ...)
x |
an uncertainty budget object |
... |
additional parameters |
none
None (invisible NULL)
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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.default, print
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) # implicitly calls the print method u.budget # same as print(u.budget) # uncertainty budget structure attributes(u.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) # implicitly calls the print method u.budget # same as print(u.budget) # uncertainty budget structure attributes(u.budget)
Performs an uncertainty contribution estimation for the uncertainty object. The metric used to measure the contribution is percent of variance. If correlation is present an additional entry is shown with the whole contribution due to correlated input quantities.
## S3 method for class 'uncertainty' summary(object, ndigits = 3, ...)## S3 method for class 'uncertainty' summary(object, ndigits = 3, ...)
object |
an uncerainty object |
ndigits |
numeric, the number of digits for displaying. |
... |
additional parameters |
none
An uncertainty summary object:
call |
the call invocation |
measurand.name |
name of the measurand |
measurand.label |
label of the measurand for displaying purposes |
budget |
a list with the name, mean, label, u(uncertainty), dof and uncertainty contribution for each input quantity plus a correlation entry if any |
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
uncertainty.default, print.summary.uncertainty, summary
# 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 # create an uncertainty summary object GFO.sum<- summary(GFO.res) # implicit call to the print method GFO.sum # same as print(GFO.sum) # uncertainty summary structure attributes(GFO.sum)# 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 # create an uncertainty summary object GFO.sum<- summary(GFO.res) # implicit call to the print method GFO.sum # same as print(GFO.sum) # uncertainty summary structure attributes(GFO.sum)
Builds an uncertainty estimation object using a measurand model and an uncertainty budget object
uncertainty(x, ...)uncertainty(x, ...)
x |
an uncertainty budget object |
... |
additional parameters |
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.
An uncertainty estimation object
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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.
Creates an uncertainty estimation object using a measurand model and an uncertainty budget object
## Default S3 method: uncertainty(x, y, ...)## Default S3 method: uncertainty(x, y, ...)
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 |
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.
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.
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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
uncertainty, uncertaintyBudget.default, print.uncertainty, plot.uncertainty, summary.uncertainty
# 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# 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
Generic function for creating an uncertainty budget object
uncertaintyBudget(x, ...)uncertaintyBudget(x, ...)
x |
a list with the vector entries name, label, mean, u(uncertainty), distribution and dof, one for each quantity. |
... |
additional parameters |
uncertaintyBudget is a generic function (under S3 protocol) for searching the default method.
An uncertainty budget object with attributes:
name the name of each input quantity
mean the mean value of each input quantity
u the uncertainty of each input quantity
dof the degrees of freedom of each input quantity
label the label of each input quantity
distribution the distribution of each input quantity, valid values are (normal, unif, t, chisq, f, triangle, binomial, bernoulli, beta, gamma)
cor the correlation matrix among the input quantities
none
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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.
Creates an uncertainty budget.
## Default S3 method: uncertaintyBudget(x, y, ...)## Default S3 method: uncertaintyBudget(x, y, ...)
x |
a list with the vector entries name, label, mean, u(uncertainty), 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
mean the mean value of each input quantity
u the uncertainty of each input quantity
dofthe degrees of freedom of each input quantity
labelthe label of each 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
H. Gasca-Aragon
Maintainer: H. Gasca-Aragon <[email protected]>
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), dof=c(10,10), label=c("x[0]", "x[1]"), distribution=c("normal","normal")), y=cor.mat) u.budgetrequire(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), dof=c(10,10), label=c("x[0]", "x[1]"), distribution=c("normal","normal")), y=cor.mat) u.budget