Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools
A new software package, <b>ISEtools</b>, is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner. Incorporating all collected data simultaneously, this Bayesian app...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/20/4544 |
| _version_ | 1811305965275840512 |
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| author | Peter W. Dillingham Basim S.O. Alsaedi Aleksandar Radu Christina M. McGraw |
| author_facet | Peter W. Dillingham Basim S.O. Alsaedi Aleksandar Radu Christina M. McGraw |
| author_sort | Peter W. Dillingham |
| collection | DOAJ |
| description | A new software package, <b>ISEtools</b>, is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner. Incorporating all collected data simultaneously, this Bayesian approach naturally accommodates sensor arrays and provides improved limit of detection estimates, including providing appropriate uncertainty estimates. Utilising >1500 lines of code, <b>ISEtools</b> provides a set of three core functions—loadISEdata, describeISE, and analyseISE— for analysing ion-selective electrode data using the Nikolskii−Eisenman equation. The functions call, fit, and extract results from Bayesian models, automatically determining data structures, applying appropriate models, and returning results in an easily interpretable manner and with publication-ready figures. Importantly, while advanced statistical and computationally intensive methods are employed, the functions are designed to be accessible to non-specialists. Here we describe basic features of the package, demonstrated through a worked environmental application. |
| first_indexed | 2024-04-13T08:36:00Z |
| format | Article |
| id | doaj.art-ce9b558f158a483c8e3b6cc8ab4137b8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:36:00Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-ce9b558f158a483c8e3b6cc8ab4137b82022-12-22T02:54:05ZengMDPI AGSensors1424-82202019-10-011920454410.3390/s19204544s19204544Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtoolsPeter W. Dillingham0Basim S.O. Alsaedi1Aleksandar Radu2Christina M. McGraw3Department of Mathematics and Statistics, University of Otago, Dunedin 9054, New ZealandSchool of Science and Technology, University of New England, Armidale, NSW 2351, AustraliaLennard-Jones Laboratories, Birchall Centre, Keele University, Keele, Staffordshire ST5 5BG, UKDepartment of Chemistry, University of Otago, Dunedin 9054, New ZealandA new software package, <b>ISEtools</b>, is introduced for use within the popular open-source programming language R that allows Bayesian statistical data analysis techniques to be implemented in a straightforward manner. Incorporating all collected data simultaneously, this Bayesian approach naturally accommodates sensor arrays and provides improved limit of detection estimates, including providing appropriate uncertainty estimates. Utilising >1500 lines of code, <b>ISEtools</b> provides a set of three core functions—loadISEdata, describeISE, and analyseISE— for analysing ion-selective electrode data using the Nikolskii−Eisenman equation. The functions call, fit, and extract results from Bayesian models, automatically determining data structures, applying appropriate models, and returning results in an easily interpretable manner and with publication-ready figures. Importantly, while advanced statistical and computationally intensive methods are employed, the functions are designed to be accessible to non-specialists. Here we describe basic features of the package, demonstrated through a worked environmental application.https://www.mdpi.com/1424-8220/19/20/4544analytical methodsbayesian methodscalibrationelectrochemistrylimit of detection |
| spellingShingle | Peter W. Dillingham Basim S.O. Alsaedi Aleksandar Radu Christina M. McGraw Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools Sensors analytical methods bayesian methods calibration electrochemistry limit of detection |
| title | Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools |
| title_full | Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools |
| title_fullStr | Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools |
| title_full_unstemmed | Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools |
| title_short | Semi-Automated Data Analysis for Ion-Selective Electrodes and Arrays Using the R Package ISEtools |
| title_sort | semi automated data analysis for ion selective electrodes and arrays using the r package isetools |
| topic | analytical methods bayesian methods calibration electrochemistry limit of detection |
| url | https://www.mdpi.com/1424-8220/19/20/4544 |
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