GUM.validate: Monte Carlo Check on the Statistical Performance of GUM...
In metRology: Support for Metrological Applications

Description Usage Arguments Details Value Author(s) References See Also Examples

A function for assessing the statistical performance of measurement uncertainty intervals for particular metrology applications computed using the methods from the Joint Committee on Guides in Metrology (JCGM) Guide to the Expression of Uncertainty in Measurement (GUM). The validation is carried out using the input values as true values in a simulation that directly checks the attained coverage probability of the uncertainty intervals produced using the GUM function.

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GUM.validate(var.name, x.i, u.i, nu.i, type, distribution, measurement.fnc, 
             correlation = diag(length(var.name)), shared.u.i = var.name, cl = 0.95, 
             cov.factor = "Student's t", sig.digits.U = 2)

`var.name`	Character vector of input variable names.
`x.i`	Vector of input variable values.
`u.i`	Vector of standard uncertainties (i.e. standard errors) for each input variable value.
`nu.i`	Degrees of freedom associated with each standard uncertainty.
`type`	Character vector of values "A" and "B" indicating the methods used to evaluate the standard uncertainty of each input value. Standard uncertainties evaluated using statistical methods are denoted Type A in the GUM, while standard uncertainties evaluated using other means are denoted Type B.
`distribution`	Character vector of probability distributions associated with the potential values taken on by each input variable. The current possible choices are "Normal" (i.e. Gaussian), "Triangular", or "Rectangular" (i.e. Uniform).
`measurement.fnc`	Character string specifying the functional relationship between input variables that defines the output measurement result.
`correlation`	Matrix giving the correlation between the different input variable values. Default is to assume no correlation between input variable values.
`shared.u.i`	Character vector giving the relative relationship between the standard uncertainties for each variable value. Groups of variables based on a common shared standard uncertainty share will all share the same variable name. The default is to assume all standard uncertainties are assessed independently, resulting a value of shared.u.i that is identical to var.name.
`cl`	Nominal confidence level to be used to compute the expanded uncertainty of the output measurement result. Default value is 0.95.
`cov.factor`	Type of coverage factor to be used. The default is to use the value from the Student's t distribution with confidence level specified above and nu.eff effective degrees of freedom.
`sig.digits.U`	Number of significant digits to be reported in the expanded uncertainty of the measurement result. The measurement result will be rounded to the same number of decimal places.

Currently 1000 simulated sets of uncertainty data are used for the computation of the attained confidence level.

A Monte Carlo assessment of the attained coverage of expanded uncertainty intervals like those produced using the GUM function for the application of interest.

Hung-kung Liu hung-kung.liu@nist.gov and Will Guthrie will.guthrie@nist.gov

Joint Committee on Guides in Metrology (JCGM), Evaluation of Measurement Data Guide to the Expression of Uncertainty in Measurement, http://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf, 2008.

GUM a function to compute GUM uncertainty intervals for general metrological applications.

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## a simple uncertainty analysis for the product of two quantities
GUM.validate(c("x1","x2"), c(2.3,1.1), c(0.030,0.015), c(5,9999),
               c("A","B"),c("Normal","Rectangular"),"x1*x2")