R: a data analysis and statistical programming environment–an emerging tool for the geosciences

2002 ◽  
Vol 28 (10) ◽  
pp. 1219-1222 ◽  
Author(s):  
E.C. Grunsky
Keyword(s):  
Data Analysis ◽  
Programming Environment ◽  
Statistical Programming

scholarly journals Non-spatial and Spatial Linear Regression

2021 ◽  
Author(s):  
Wouter Steenbeek ◽  
Stijn Ruiter
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Spatial Dependence ◽  
Linear Regression Analysis ◽  
Predictor Variable ◽  
Programming Environment ◽  
Statistical Programming ◽  
Multiple Predictor ◽  
Applied Researcher ◽  
Broad Overview

This chapter gives an introduction to the workhorse of quantitative statistical analysis, linear regression analysis, assuming minimal background knowledge of the reader. We give a broad overview of linear regression analysis using one predictor variable and then turn to regression with multiple predictor variables and key assumptions, which segues into regression analysis of areal units that include spatial dependence. Throughout we use the statistical programming environment R, and we try to summarize the most important challenges that an applied researcher will face. As this is just an introduction to the topic, we provide references to sources that are highly recommended for any researcher who aims to understand or apply (spatial) linear regression analysis.

scholarly journals Data visualization with the programming language R

2021 ◽  
Author(s):  
Paul Brennan
Keyword(s):  
Data Analysis ◽  
Data Visualization ◽  
Programming Language ◽  
Education Finance ◽  
Employment Opportunities ◽  
Different Types ◽  
Statistical Programming

Data visualization is an extremely valuable skill in science, finance and journalism. Learning to program will help reproducible data analysis and will increase the different types of visualization that can be generated. The statistical programming language R is a very useful programming language. The R community is friendly, supportive and very diverse including students, academics, health scientists, journalists and professional data scientists. An experience of R or another programming language such as Python or JavaScript will improve your science and your employment opportunities in and outside of research. Programming is a useful skill in education, finance, journalism and other areas too.

SSD for R

2021 ◽  
Author(s):  
Charles Auerbach ◽  
Wendy Zeitlin
Keyword(s):  
Data Analysis ◽  
Visual Analysis ◽  
Statistical Significance ◽  
Single Subject ◽  
Single Subject Research ◽  
Level Data ◽  
Wide Range ◽  
Research Designs ◽  
Range Of Functions ◽  
Statistical Programming

Single-subject research designs have been used to build evidence to the effective treatment of problems across various disciplines, including social work, psychology, psychiatry, medicine, allied health fields, juvenile justice, and special education. This book serves as a guide for those desiring to conduct single-subject data analysis. The aim of this text is to introduce readers to the various functions available in SSD for R, a new, free, and innovative software package written in R, the robust open-source statistical programming language written by the book’s authors. SSD for R has the most comprehensive functionality specifically designed for the analysis of single-subject research data currently available. SSD for R has numerous graphing and charting functions to conduct robust visual analysis. Besides the ability to create simple line graphs, features are available to add mean, median, and standard deviation lines across phases to help better visualize change over time. Graphs can be annotated with text. SSD for R contains a wide variety of functions to conduct statistical analyses traditionally conducted with single-subject data. These include numerous descriptive statistics and effect size functions and tests of statistical significance, such as t tests, chi-squares, and the conservative dual criteria. Finally, SSD for R has the capability of analyzing group-level data. Readers are led step by step through the analytical process based on the characteristics of their data. Numerous examples and illustrations are provided to help readers understand the wide range of functions available in SSD for R and their application to data analysis and interpretation.

How to use this book.

2021 ◽  
pp. 1-2
Author(s):  
Donald Quicke ◽  
Buntika A. Butcher ◽  
Rachel Kruft Welton
Keyword(s):  
Statistical Analysis ◽  
Open Source ◽  
Programming Environment ◽  
Biology Students ◽  
Book Title ◽  
Analysis Tools ◽  
Simple Step ◽  
Statistical Programming

Abstract This book takes a simple step-by-step approach to give a good grounding in the use of R for undergraduate/beginning postgraduate biology students. R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. This chapter provides some steps on how to use the book, from setting up the computer to running the code as you go along. The chapter structure is also introduced.

scholarly journals - Solving the probability regression models by using R programming environment - Case Study For patients Epilepsy in Basra : حل نماذج الانحدار الاحتمالية باستخدام بيئة برمجة R - دراسة حالة لمرضى الصرع –

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Nada Badr Jarah
Keyword(s):  
Regression Models ◽  
Age Factors ◽  
Scientific Progress ◽  
Regression Function ◽  
Economic Effects ◽  
Programming Environment ◽  
Factors Affecting ◽  
Statistical Programming ◽  
R Programming ◽  
Patients With Epilepsy

The technology for dealing with accurate calculations progresses very quickly . and be a major impetus for scientific progress. and the perfect mastery of R in higher levels is necessary for many applications . The R statistical programming environment provides an ideal stage to conduct the study because of its powerful programming capability , graphics and a comprehensive set of statistical functions , it contains more than 11164 packages . The aim is to study the phenomenon of epilepsy and to identify the most important factors affecting patients with epilepsy in the province of Basra. And the cause of this disease of social and economic effects on the patient and his family. and also this study is to solve the probability regression models represented by probit, tobit and logit ,as well as identifying the variables of the study (sex and age) that affect or increase the incidence epilepsy for the data of 2296 patients in Basra. The study design , is the represent by estimating the three probable regression models by probit, tobit and logit for the effect of sex and age factors on the risk of developing epilepsy ,and the tools used is to make statistic program ,then we find the R programming language was give the good and correct answer, Then the statistical analysis using R language in which the written orders are implemented directly without the need to build a complete program to implement the programming statements using the regression function for each probability regression models (Probit, Tobit and Logit) and the results showed that the regression of the probabilistic functions is that both the age factor and the sex factor have a significant effect in the case of the disease in terms of being inpatient or outpatient. This is confirmed by the Wold test. and Also the conclusion shows that functions represented the best representation, which was confirmed by the coefficient of selection as it reached 0.96.

scholarly journals ADIFOR–Generating Derivative Codes from Fortran Programs

1992 ◽  
Vol 1 (1) ◽  
pp. 11-29 ◽  
Author(s):  
Christian Bischof ◽  
Alan Carle ◽  
George Corliss ◽  
Andreas Griewank ◽  
Paul Hovland
Keyword(s):  
Data Analysis ◽  
Automatic Differentiation ◽  
Divided Difference ◽  
Real Life ◽  
Programming Environment ◽  
Critical Importance ◽  
Difference Approximations ◽  
Source Transformation ◽  
Derivatives Of ◽  
Fortran 77

The numerical methods employed in the solution of many scientific computing problems require the computation of derivatives of a function f Rn→Rm. Both the accuracy and the computational requirements of the derivative computation are usually of critical importance for the robustness and speed of the numerical solution. Automatic Differentiation of FORtran (ADIFOR) is a source transformation tool that accepts Fortran 77 code for the computation of a function and writes portable Fortran 77 code for the computation of the derivatives. In contrast to previous approaches, ADIFOR views automatic differentiation as a source transformation problem. ADIFOR employs the data analysis capabilities of the ParaScope Parallel Programming Environment, which enable us to handle arbitrary Fortran 77 codes and to exploit the computational context in the computation of derivatives. Experimental results show that ADIFOR can handle real-life codes and that ADIFOR-generated codes are competitive with divided-difference approximations of derivatives. In addition, studies suggest that the source transformation approach to automatic differentiation may improve the time to compute derivatives by orders of magnitude.

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