Dedicated to the 95th anniversary of the outstanding scientist Academician of the National Academy of Sciences of Ukraine V.L. RVACHOV

The article is dedicated to the outstanding scientist of the twentieth century, Academician of the National Academy of Sciences of Ukraine Volodymyr Logvynovych Rvachev, who would have turned 95 in 2021. At one time VL Rvachev was the first rector of HIRE, in 1970 he became head of the Department of Theoretical and Mathematical Physics of KhPI (now it is the Department of Applied Mathematics). The name of this famous scientist is included in the golden pages of the history of mathematics and mechanics. The R-functions theory, as the main scientific discovery of his life, forever glorified VL Rvachev as a great scientist. He created also scientific school which is world-famous due to the numerous implementations and uniqueness of the RFM method. The main biographical data of the famous scientist are presented in the work, the versatility of the talented person, the depth of his life views, the breadth of horizons are emphasized. Particular attention is paid to the development and further application of the R-functions theory to solve modern problems. The response of close people, students and followers who remember their great Teacher is presented in words of gratitude to Volodymyr Logvynovych.

Supervised Learning for Binary Classification on US Adult Income

In this project, various binary classification methods have been used to make predictions about US adult income level in relation to social factors including age, gender, education, and marital status. We first explore descriptive statistics for the dataset and deal with missing values. After that, we examine some widely used classification methods, including logistic regression, discriminant analysis, support vector machine, random forest, and boosting. Meanwhile, we also provide suitable R functions to demonstrate applications. Various metrics such as ROC curves, accuracy, recall and F-measure are calculated to compare the performance of these models. We find the boosting is the best method in our data analysis due to its highest AUC value and the highest prediction accuracy. In addition, among all predictor variables, we also find three variables that have the largest impact on the US adult income level.

Revticulate: An R framework for interaction with RevBayes

1: Phylogenetic methods are increasingly complex. Researchers need to make many choices about how to model different aspects of the data appropriately. It is increasingly common to deploy hierarchical Bayesian models in which different data types may be described by different processes. This necessitates tools to help users understand model assumptions more clearly.2: We describe the package \code{Revticulate}, which provides an R-based interface to the software RevBayes. RevBayes is a Bayesian phylogenetics program that implements an R-like computing language, but does not interface with R itself. Revticulate was designed to allow communication between an R session, and all of its associated capabilities, such as plotting and simulation, and a RevBayes session.3: Revticulate can be used to copy objects from RevBayes into R. We provide several usage examples demonstrating how objects, such as such as random variables drawn from probability distributions and phylogenetic trees, can be generated in RevBayes. We then show how these objects can be used with R's phylogenetic ecosystem to plot a phylogenetic tree, or with base R functions to simulate the behavior of a particular probability. 4: Revticulate is a broadly useful software. Revticulate can be used alongside popular document preparation packages, such as Knitr and pkgdown to generate attractive reports, tutorials, and websites. This means that researchers who are looking to communicate their work in RevBayes can do that very easily using Revticulate, enabling rapid generation of reproducible research outputs.

Equivalence Testing for Multiple Regression

Psychological research is rife with inappropriately concluding lack of association or no effect between a predictor and the outcome in regression models following statistically nonsignificant results. This approach is methodologically flawed, however, because failing to reject the null hypothesis using traditional, difference-based tests does not mean the null is true (i.e., no relationship). This flawed methodology leads to high rates of incorrect conclusions that flood the literature. This thesis introduces a novel, methodologically sound alternative. I demonstrate how equivalence testing can be applied to evaluate whether a predictor has negligible effects on the outcome variable in multiple regression. I constructed a simulation study to evaluate the performance (i.e., power and error rates) of two equivalence-based tests and compared it to the common, but inappropriate, method of concluding no effect by failing to reject the null hypothesis of the traditional test. I further propose two R functions to accompany this thesis and supply researchers with open-access and easy-to-use tools that they can flexibly adopt in their own research. The use of the proposed equivalence-based methods and R functions is then illustrated using examples from the literature, and recommendations for results reporting and interpretations are discussed. My results demonstrate that using tests of equivalence instead of the traditional test is the appropriate statistical choice: Tests of equivalence show high rates of correct conclusions, especially with larger sample sizes, and low rates of incorrect conclusions, whereas the traditional method demonstrates unacceptably high incorrect conclusion rates.

Easyreporting simplifies the implementation of Reproducible Research layers in R software

During last years “irreproducibility” became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools. We describe the main functionalities and illustrate the organization of an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we show how to use easyreporting in other projects to trace R functions automatically. This latter feature helps developers to implement procedures that automatically keep track of the analysis steps. Easyreporting can be useful in supporting the reproducibility of any data analysis project and shows great advantages for the implementation of R packages and GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study.

Very basic R syntax.

R is a programming language that has a huge range of inbuilt statistical and graphical functions. Firstly, this chapter shows how R works by talking you through a number of exercises, often producing graphical output, so you will get to know how to write simple code and become familiar with some of the most commonly used R functions for manipulating data and doing simple calculations. For ease, the chapter will firstly use a non-biological type of example. Thereafter, it will enter, display and analyse a number of real biological or medical datasets as might be obtained in student class experiments or fieldwork projects. Further on, it will present an outline of statistical tests appropriate to various types of data that you will come across.

Practical R for biologists: an introduction

R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.