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An interactive website for analytical method comparison and bias estimation

CLINICAL BIOCHEMISTrY 2017年第18期50卷 1025-1029页

作者： Bahar, Burak Tuncel, Ayse F. Holmes, Earle W. Holmes, Daniel T. Yale Univ Sch Med Dept Lab Med 20 York StPS-329 New Haven CT 06519 USA Loyola Univ Med Ctr Dept Pathol Maywood IL 60153 USA Univ British Columbia Dept Pathol & Lab Med Vancouver BC Canada

Objectives: regulatory standards mandate laboratories to perform studies to ensure accuracy and reliability of their test results. Method comparison and bias estimation are important components of these studies. Design & methods: We developed an interactive website for evaluating the relative performance of two analytical methods using r programming language tools. The website can be accessed at https://***/method_compare/. results: The site has an easy-to-use interface that allows both copy-pasting and manual entry of data. It also allows selection of a regression model and creation of regression and difference plots. Available regression models include Ordinary Least Squares, Weighted-Ordinary Least Squares, Deming, Weighted-Deming, Passing-Bablok and Passing-Bablok for large datasets. The server processes the data and generates downloadable reports in PDF or HTML format. Conclusions: Our website provides clinical laboratories a practical way to assess the relative performance of two analytical methods.

关键词： Method comparison Clinical chemistry r programming Shiny Website

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Calculating risk and prevalence ratios and differences in r: Developing intuition with a hands-on tutorial and code (vol 86, pg 104, 2023)

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ANNALS OF EPIDEMIOLOGY 2024年 99卷 47-47页

作者： Yorlets, rachel r. Lee, Youjin Gantenberg, Jason r. Brown Univ Dept Epidemiol Sch Publ Hlth Providence RI 02903 USA Brown Univ Populat Studies & Training Ctr Providence RI USA Brown Univ Sch Publ Hlth Dept Biostat Providence RI USA Brown Univ Dept Hlth Serv Policy & Practice Sch Publ Hlth Providence RI USA

AbstractEpidemiologic research questions often focus on evaluating binary outcomes, yet curricula and scientific literature do not always provide clear guidance or examples on selecting and calculating an appropriate measure of association in these scenarios. reporting inappropriate measures may lead to misleading statistical conclusions. We present a hands-on tutorial that includes annotated code written in an open-source statistical programming language (r) showing readers how to apply, compare, and understand four methods used to estimate a risk or prevalence ratio (or difference), rather than presenting an odds ratio. We will provide guidance on when to use each method, discuss the strengths and limitations of each approach, and compare the results obtained across them. Ultimately, we aim to help trainees, public health researchers, and interdisciplinary professionals develop an intuition for these methods and empower them to implement and interpret these methods in their own research.

关键词： risk ratio risk difference r programming Log-binomial Modified Poisson Logistic regression

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Examining the enablers of sustainable entrepreneurship - an interpretive structural modelling technique

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JOUrNAL OF ENTrEPrENEUrSHIP IN EMErGING ECONOMIES 2022年第5期14卷 701-726页

作者： Gupta, Neha Matharu, Manita Amity Univ Amity Sch Business Noida India

Purpose The General Assembly recognized the critical contribution of entrepreneurship to sustainable development in its resolution 73/225 on entrepreneurship for sustainable development by accelerating economic growth and innovation and addressing social and environmental challenges in the context of the 2030 Agenda for Sustainable Development. Hence, it is important to understand the variables influencing entrepreneurs' aspirations for sustainable enterprises to promote sustainable entrepreneurial activity for sustainable development. Therefore, this study aims to analyse the enablers affecting the adoption of sustainable entrepreneurship practices by the entrepreneurs in India. Design/methodology/approach This study has been conducted in three steps. The first step includes the identification of enablers from the extensive review of the literature followed by the second step of finalization of enablers by experts' opinion. Finally, in the third step, enablers are analysed using interpretive structural modelling (ISM). Findings After the extensive literature review and opinion of 100 millennial experts, 11 enablers are identified. In the third step, ISM is applied to develop a hierarchical model for the enablers affecting the adoption of sustainable entrepreneurial practice and to establish the contextual relationships among those enablers. research limitations/implications This study can be used by practitioners and policymakers to further validate the driving enablers for developing sustainability-driven entrepreneurial intention and to increase the adoption of sustainable practices by entrepreneurs. Originality/value This study is based on the ISM providing significant insights related to enablers affecting the adoption of sustainable entrepreneurial practices. It provides valuable knowledge to entrepreneurial researchers and practitioners.

关键词： Sustainability Entrepreneurial intention Education for sustainable entrepreneurship Interpretive structural modelling r programming

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Computing Topics on Multiple Imputation in Big Identifiable Data Using r: An Application to Educational research 19th

Computing Topics on Multiple Imputation in Big Identifiable ...

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19th International Conference on Computational Science and Its Applications (ICCSA)

作者： Ferrao, Maria Eugenia Prata, Paula Univ Beira Interior Covilha Portugal Ctr Appl Math & Econ CEMAPRE Lisbon Portugal Inst Telecomunicacoes IT Covilha Portugal

ISBN: (纸本)9783030243029

This article shows how to conduct multiple imputation in big identifiable data for educational research purposes. The r statistical package and procedures to handle missing data applied for the purpose of this study were "BaylorEdPsych" and "mi". Firstly, we checked that every dataset rejected the null hypothesis for Missing Completely At random (MCAr), using the function "LittleMCAr". Simulated and real data analyses were conducted. results suggest that the improvement of the quality of imputation requires alternative methods to be developed.

关键词： Multiple imputation r programming Big data Education research

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Business-Oriented Feature Selection for Hybrid Classification Model of Credit Scoring 2

Business-Oriented Feature Selection for Hybrid Classificatio...

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2nd IEEE International Conference on Data Stream Mining and Processing (DSMP)

作者： Chornous, Galyna Nikolskyi, Ihor Taras Shevchenko Natl Univ Kyiv Dept Econ Cybernet Kiev Ukraine

ISBN: (纸本)9781538628744

Application of predictive models on the basis of data mining confirmed its expediency in solving many economic problems. One of the crucial issues is the assessment of the borrower's creditworthiness on the basis of credit scoring models. This paper proposed an ensemble-based technique combining selected base classification models with business-specific feature selection add-on to increase the classification accuracy of real-life case of credit scoring. As the model limitations have been used easy-understandable algorithms on open-source software (r programming) The statistical results proved that hybrid approach for user-defined variables can be more than useful for ensemble binary classification model. It is shown that a great improvement can be reached by applying hybrid approach to feature selection process on additional variables (more descriptive ones that were built on initial features) for this real-life case with limited computational resources.

关键词： hybrid ensemble feature selection binary classification stacking major voting credit scoring r programming

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Agile release Planning Using Natural Language Processing Algorithm

Agile Release Planning Using Natural Language Processing Alg...

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Amity International Conference on Artificial Intelligence (AICAI)

作者： Sharma, Sarika Kumar, Deepak Amity Univ AIIT Noida India

ISBN: (纸本)9781538693469

once the requirement is gathered in agile, it is broken down into smaller pre-defined format called user stories. These user stories are then scoped in various sprint releases and delivered accordingly. release planning in Agile becomes challenging when the number of user stories goes up in hundreds. In such scenarios it is very difficult to manually identify similar user stories and package them together into a release. Hence, this paper suggests application of natural language processing algorithms for identifying similar user stories and then scoping them into a release This paper takes the approach to build a word corpus for every project release identified in the project and then to convert the provided user stories into a vector of string using Java utility for calculating top 3 most occurring words from the given project corpus in a user story. Once all the user stories are represented as vector array then by using rV coefficient NLP algorithm the user stories are clustered into various releases of the software project. Using the proposed approach, the release planning for large and complex software engineering projects can be simplified resulting into efficient planning in less time. The automated commercial tools like JIrA and rally can be enhanced to include suggested algorithms for managing release planning in Agile.

关键词： Natural Language Processing Algorithm Software Engineering Agile Methodology release Planning r programming

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A Comparison Study of Clustering Validity Indices

A Comparison Study of Clustering Validity Indices

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Global Summit on Computer and Information Technology (GSCIT)

作者： Chouikhi, Hasna Charrad, Malika Ghazzali, Nadia Univ Gabes Gabes Tunisia La Manouba Univ RIADI Lab La Manouba Tunisia CNAM MSDM Team Cedr Lab Paris France Univ Quebec Trois Rivieres Trois Rivieres PQ Canada

ISBN: (纸本)9781467365871

Most of the algorithms of clustering take in entry certain parameters such as the number and the density of clusters or, at least, the number of data in every cluster. The question that arises is how to determine the number of clusters in the case of an automatic classification. The purpose of our study is to compare cluster validity indices to select the optimal ones. An examination of 30 indices for determining the number of clusters is conducted on real and artificial data sets being generated according to various design factors.

关键词： Clustering validity indices NbClust package r programming Clustering

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On the Developers' Attitude Towards CrAN Checks 30

On the Developers' Attitude Towards CRAN Checks

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30th IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Kumar, Pranj Ay Ie, Davin Vidoni, Melina RMIT Univ Melbourne Vic Australia Australian Natl Univ Canberra ACT Australia

ISBN: (纸本)9781450392983

r is a package-based, multi-paradigm programming language for scientific software. It provides an easy way to install third-party code, datasets, tests, documentation and examples through CrAN (Comprehensive r Archive Network). Prior works indicated developers tend to code workarounds to bypass CrAN's automated checks (performed when submitting a package) instead of fixing the code-doing so reduces packages' quality. It may become a threat to those analyses written in r that rely on miss-checked code. This preliminary study card-sorted source code comments and analysed StackOverflow (SO) conversations discussing CrAN checks to understand developers' attitudes. We determined that about a quarter of SO posts aim to bypass a check with a workaround;the most affected are code-related problems, package dependencies, installation and feasibility. We analyse these checks and outline future steps to improve similar automated analyses.

关键词： Software Ecosystems Package-Based Environment r programming CrAN Checks

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Speech production and perception data collection in r: A tutorial for web-based methods using speechcollectr

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BEHAVIOr rESEArCH METHODS 2024年第7期56卷 6915-6950页

作者： Thomas, Abbey L. Assmann, Peter F. Univ Texas Dallas Sch Behav & Brain Sci Richardson TX 75080 USA

This tutorial is designed for speech scientists familiar with the r programming language who wish to construct experiment interfaces in r. We begin by discussing some of the benefits of building experiment interfaces in r-including r's existing tools for speech data analysis, platform independence, suitability for web-based testing, and the fact that r is open source. We explain basic concepts of reactive programming in r, and we apply these principles by detailing the development of two sample experiments. The first of these experiments comprises a speech production task in which participants are asked to read words with different emotions. The second sample experiment involves a speech perception task, in which participants listen to recorded speech and identify the emotion the talker expressed with forced-choice questions and confidence ratings. Throughout this tutorial, we introduce the new r package speechcollectr, which provides functions uniquely suited to web-based speech data collection. The package streamlines the code required for speech experiments by providing functions for common tasks like documenting participant consent, collecting participant demographic information, recording audio, checking the adequacy of a participant's microphone or headphones, and presenting audio stimuli. Finally, we describe some of the difficulties of remote speech data collection, along with the solutions we have incorporated into speechcollectr to meet these challenges.

关键词： r programming Web-based Audio recording Experimentation Speech production Speech perception

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Leveraging GenAI for an Intelligent Tutoring System for r: A Quantitative Evaluation of Large Language Models 15

Leveraging GenAI for an Intelligent Tutoring System for R: A...

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15th IEEE Global Engineering Education Conference (IEEE EDUCON)

作者： Frank, Lukas Herth, Fabian Stuwe, Paul Klaiber, Marco Gerschner, Felix Theissler, Andreas Aalen Univ Appl Sci Aalen Germany

ISBN: (纸本)9798350394023;9798350394030

The tremendous advances in Artificial Intelligence (AI) open new opportunities for education, with Intelligent Tutoring Systems (ITS) powered by Generative Artificial Intelligence (GenAI) proving to be a promising prospect. Because of this, our work explores state-of-the-art (SOTA) ITS approaches with the integration of Large Language Models (LLMs) to improve programming education. We investigate whether and how a GenAI-based ITS can effectively support students in learning r programming skills. We measured the performance of three current pairings of LLMs and user interfaces: GPT-3.5 via ChatGPT, PaLM 2 via Google Bard, and GPT-4 via Bing. Therefore, we evaluated the LLMs on four types of problem settings when learning/teaching programming. Our experimental results show that the use of generative AI, specifically LLMs for r programming, is promising, where GPT-3.5 yielded the most satisfactory results. Furthermore, the advantages and limitations of our approach are addressed and revealed. Finally, open research directions towards explainable AI (XAI) and integrated self-assessment are pointed out.

关键词： Generative AI AI in Education Intelligent Tutoring Systems r programming Student Support

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