gconsensus-package: Consensus Value Constructor

In gconsensus: Consensus Value Constructor

Consensus Value Constructor

Description

An implementation of the International Bureau of Weights and Measures (BIPM) generalized consensus estimators used to assign the reference value in a key comparison exercise. This can also be applied to any interlaboratory study. Given a set of different sources, primary laboratories or measurement methods this package provides an evaluation of the variance components according to the selected statistical method for consensus building. It also implements the comparison among different consensus builders and evaluates the participating method or sources against the consensus reference value. Based on a diverse set of references, DerSimonian-Laird (1986) <doi:10.1016/0197-2456(86)90046-2>, for a complete list of references look at the reference section in the package documentation. Partially based on the results published in Control clinical trials (1985). <https://doi.org/10.1016/0197-2456(86)90046-2> For a complete list of references see the reference section.

Details

The DESCRIPTION file: This package was not yet installed at build time.
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Author(s)

Hugo Gasca-Aragon

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

References

1. Graybill and Deal (1959), Combining Unbiased Estimators, Biometrics, 15, pp. 543-550.

2. DerSimonian and Laird (1986), Meta-analysis in Clinical Trials, Controlled Clinical Trials, 7, pp. 177-188.

3. R. A. Horn, S. A. Horn and D. B. Duncan (1975), Estimating heteroscedastic variance in linear models. Journ. Amer. Statist. Assoc. 70, 380

4. M. S. Levenson, D. L. Banks, K. R. Eberhardt, L. M. Gill, W. F. Guthrie, H. K. Liu, M. G. Vangel, J. H. Yen, and N. F. Zhang (2000), An ISO GUM Approach to Combining Results from Multiple Methods, Journal of Research of the National Institute of Standards and Technology, Volume 105, Number 4.

5. John Mandel and Robert Paule (1970), Interlaboratory Evaluation of a Material with Unequal Number of Replicates, Analytical Chemistry, 42, pp. 1194-1197.

6. Robert Paule and John Mandel (1982), Consensus Values and Weighting Factors, Journal of Research of the National Bureau of Standards, 87, pp. 377-385.

7. Andrew Rukhin (2009), Weighted Means Statistics in Interlaboratory Studies, Metrologia, Vol. 46, pp. 323-331.

8. Andrew Ruhkin (2003), Two Procedures of Meta-analysis in Clinical Trials and Interlaboratory Studies, Tatra Mountains Mathematical Publications, 26, pp. 155-168.

9. Andrew Ruhkin and Mark Vangel (1998), Estimation of a Common Mean and Weighted Means Statistics, Journal of the American Statistical Association, Vol. 93, No. 441.

10. Andrew Ruhkin, B. Biggerstaff, and Mark Vangel (2000), Restricted Maximum Likelihood Estimation of a Common Mean and Mandel-Paule Algorithm, Journal of Statistical Planning and Inference, 83, pp. 319-330.

11. Mark Vangel and Andrew Ruhkin (1999), Maximum Likelihood Analysis for Heteroscedastic One-Way Random Effects ANOVA in Interlaboratory Studies, Biometrics 55, 129-136.

12. Susannah Schiller and Keith Eberhardt (1991), Combining Data from Independent Analysis Methods, Spectrochimica, ACTA 46 (12).

13. Bimal Kumar Sinha (1985), Unbiased Estimation of the Variance of the Graybill-Deal Estimator of the Common Mean of Several Normal Populations, The Canadian Journal of Statistics, Vol. 13, No. 3, pp. 243-247.

14. Nien-Fan Zhang (2006), The Uncertainty Associated with The Weighted Mean of Measurement Data, Metrologia, 43, PP. 195-204.

15. CCQM (2013), CCQM Guidance note: Estimation of a Consensus KCRV and associated Degrees of Equivalence, Version 10.

16. Knapp G. and Hartung J. (2003), Improved tests for a random effects meta-regression with a single covariate, Statistics in Medicine, Vol 22, Issue 17, pp 2693-2710

See Also

gconsensus

Examples

ilab <- list(
  data = data.frame(
    participant = c("BAM", "IRMM", "LGC", "NARL", "NIST", "NMIJ", "NRC"),
    code = paste0("p", c(1:7)),
    method = rep(1, 7), 
    value = c(10.21, 10.9, 10.94, 10.58, 10.81, 9.62, 10.8), 
    unit = rep("\\micro \\kilogram/\\kilogram", 7),
    expandedUnc = c(0.381, 0.250, 0.130, 0.410, 0.445, 0.196, 0.093), 
    n = rep(1, 7), 
    coverageFactor = rep(2, 7),
    coverageProbability = rep(0.95, 7), 
	method.type = rep("p", 7),
    included = rep(1, 7)),
	symbol = rep(1, 7),
	symbol.Fillcolor = rep(1, 7),
	comments = rep("", 7),
	info = list(variable = c("Software", "Institute", "Exercise", "Author", "Date", 
      "Measurand", "Units"), 
    value = c("consensus", "BIPM", "CCQM-K25", "Michele Schantz & Stephen Wise",
      "12-12-2003", "PCB 105", "\\micro \\kilogram/\\kilogram"))
)

aConsensus <- gconsensus(ilab,
  method = "DL1",
  config = list(
               signif.digits = 2,
			   display.order = "location",
			   display.orientation = "horizontal",
			   display.shownames = TRUE,
			   tab.size = 12,
			   alpha = 0.05,
               expansion.factor.type = "small.sample",
			   tau = mad(ilab$data$value),
               unreliable.uncertainties = FALSE,
               MC_samples = 1e5,
               MC_seed = 12345,
               MC_use.HKSJ.adjustment = TRUE,
			   MC_interval.type = "symmetric",
			   MC_use.parallel.computing = FALSE,
		       filename = "hb_consensus_model.txt"
          )
)

print(aConsensus)

gconsensus documentation built on Nov. 10, 2022, 5:09 p.m.