ILS: An R package for statistical analysis in Interlaboratory Studies

• Published in: Chemometrics and intelligent laboratory systems 2018-10, Vol.181, p.11-20
• Main Authors: Flores, Miguel, Fernández-Casal, Rubén, Naya, Salvador, Tarrío-Saavedra, Javier, Bossano, Roberto
• Format: Article
• Language: English
• Subjects: Bootstrap; Data depth; Functional data analysis; Interlaboratory studies; Outlier detection; R software
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2018.07.013

In this paper we present an R package with routines to perform Interlaboratory Studies (ILS). The aim of the ILS package is to detect laboratories that provide not consistent results, working simultaneously with different test materials, from the perspective of the Univariate Data Analysis and the Functional Data Analysis (FDA). The ILS package estimates the Mandel's h and k scalar statistics, based on the ASTM E691 and ISO 5725-2 standards, to identify laboratories that provide significantly different results. Cochran and Grubbs tests to evaluate the presence of outliers are also available. In addition, Analysis of Variance (ANOVA) techniques are provided, both for the cases of fixed and random effects, including confidence intervals for the parameters. One of the novelties of this package is the incorporation of tools to perform an ILS from a functional data analysis approach. Accordingly, the functional nature of the data obtained by experimental techniques corresponding to analytical chemistry, applied physics and engineering applications (spectra, thermograms, and sensor signals, among others) is taking into account by implementing the functional extensions of Mandel's h and k statistics. For this purpose, the ILS package also estimates the functional statistics H(t) and K(t), as well as the dH and dK test statistic, which are used to evaluate the repeatability and reproducibility hypotheses where the critical ch and ck values are estimated by using a bootstrap algorithm.