The R Commander: a Basic-Statistics Graphical User Interface to R
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
An Evaluation of Statistical Software for Research and Instruction
Behavior Research Methods, Instruments, & Computers 1985, 17(2),352-358 An evaluation of statistical software for research and instruction DARRELL L. BUTLER Ball State University, Muncie, Indiana and DOUGLAS B. EAMON University of Houston-Clear Lake, Houston, Texas A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruc tion and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages. Many psychologists use statistical programs to analyze ware packages available on the VAX computers at Ball research data or to teach statistics, and many are interested State University: BMDP, SPSSx, SCSS, and MINITAB. in using computer software for these purposes (e.g., Versions of these programs are widely available and are Butler, 1984; Butler & Kring, 1984). The present paper used here as points ofreference to judge the strengths and provides some program descriptions, benchmarks, and weaknesses of the microcomputer packages. Although evaluations of a wide variety of marketed statistical pro there are many programs distributed by individuals (e.g., grams that may be useful in research and/or instruction. see Academic Computer Center of Gettysburg College, This review differs in several respects from other re 1984, and Eamon, 1983), none are included in the present cent reviews of statistical programs (e. g., Carpenter, review. -
Rkward: a Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R
JSS Journal of Statistical Software June 2012, Volume 49, Issue 9. http://www.jstatsoft.org/ RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R Stefan R¨odiger Thomas Friedrichsmeier Charit´e-Universit¨atsmedizin Berlin Ruhr-University Bochum Prasenjit Kapat Meik Michalke The Ohio State University Heinrich Heine University Dusseldorf¨ Abstract R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated devel- opment environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself. Keywords: GUI, integrated development environment, plugin, R. -
Summation Reviewer Manual
| 1 | 2 AccessData Legal and Contact Information Document date: December 12, 2014 Legal Information ©2014 AccessData Group, Inc. All rights reserved. No part of this publication may be reproduced, photocopied, stored on a retrieval system, or transmitted without the express written consent of the publisher. AccessData Group, Inc. makes no representations or warranties with respect to the contents or use of this documentation, and specifically disclaims any express or implied warranties of merchantability or fitness for any particular purpose. Further, AccessData Group, Inc. reserves the right to revise this publication and to make changes to its content, at any time, without obligation to notify any person or entity of such revisions or changes. Further, AccessData Group, Inc. makes no representations or warranties with respect to any software, and specifically disclaims any express or implied warranties of merchantability or fitness for any particular purpose. Further, AccessData Group, Inc. reserves the right to make changes to any and all parts of AccessData software, at any time, without any obligation to notify any person or entity of such changes. You may not export or re-export this product in violation of any applicable laws or regulations including, without limitation, U.S. export regulations or the laws of the country in which you reside. AccessData Group, Inc. 1100 Alma Street Menlo Park, California 94025 USA www.accessdata.com AccessData Trademarks and Copyright Information AccessData® MPE+ Velocitor™ AccessData Certified Examiner® (ACE®) Password Recovery Toolkit® AD Summation® PRTK® Discovery Cracker® Registry Viewer® Distributed Network Attack® ResolutionOne™ DNA® SilentRunner® Forensic Toolkit® (FTK®) Summation® Mobile Phone Examiner Plus® ThreatBridge™ AccessData Legal and Contact Information | 3 A trademark symbol (®, ™, etc.) denotes an AccessData Group, Inc. -
Statistics with Free and Open-Source Software
Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source -
Statistical Software
Statistical Software A. Grant Schissler1;2;∗ Hung Nguyen1;3 Tin Nguyen1;3 Juli Petereit1;4 Vincent Gardeux5 Keywords: statistical software, open source, Big Data, visualization, parallel computing, machine learning, Bayesian modeling Abstract Abstract: This article discusses selected statistical software, aiming to help readers find the right tool for their needs. We categorize software into three classes: Statisti- cal Packages, Analysis Packages with Statistical Libraries, and General Programming Languages with Statistical Libraries. Statistical and analysis packages often provide interactive, easy-to-use workflows while general programming languages are built for speed and optimization. We emphasize each software's defining characteristics and discuss trends in popularity. The concluding sections muse on the future of statistical software including the impact of Big Data and the advantages of open-source languages. This article discusses selected statistical software, aiming to help readers find the right tool for their needs (not provide an exhaustive list). Also, we acknowledge our experiences bias the discussion towards software employed in scholarly work. Throughout, we emphasize the software's capacity to analyze large, complex data sets (\Big Data"). The concluding sections muse on the future of statistical software. To aid in the discussion, we classify software into three groups: (1) Statistical Packages, (2) Analysis Packages with Statistical Libraries, and (3) General Programming Languages with Statistical Libraries. This structure -
STAT 3304/5304 Introduction to Statistical Computing
STAT 3304/5304 Introduction to Statistical Computing Statistical Packages Some Statistical Packages • BMDP • GLIM • HIL • JMP • LISREL • MATLAB • MINITAB 1 Some Statistical Packages • R • S-PLUS • SAS • SPSS • STATA • STATISTICA • STATXACT • . and many more 2 BMDP • BMDP is a comprehensive library of statistical routines from simple data description to advanced multivariate analysis, and is backed by extensive documentation. • Each individual BMDP sub-program is based on the most competitive algorithms available and has been rigorously field-tested. • BMDP has been known for the quality of it’s programs such as Survival Analysis, Logistic Regression, Time Series, ANOVA and many more. • The BMDP vendor was purchased by SPSS Inc. of Chicago in 1995. SPSS Inc. has stopped all develop- ment work on BMDP, choosing to incorporate some of its capabilities into other products, primarily SY- STAT, instead of providing further updates to the BMDP product. • BMDP is now developed by Statistical Solutions and the latest version (BMDP 2009) features a new mod- ern user-interface with all the statistical functionality of the classic program, running in the latest MS Win- dows environments. 3 LISREL • LISREL is software for confirmatory factor analysis and structural equation modeling. • LISREL is particularly designed to accommodate models that include latent variables, measurement errors in both dependent and independent variables, reciprocal causation, simultaneity, and interdependence. • Vendor information: Scientific Software International http://www.ssicentral.com/ 4 MATLAB • Matlab is an interactive, matrix-based language for technical computing, which allows easy implementation of statistical algorithms and numerical simulations. • Highlights of Matlab include the number of toolboxes (collections of programs to address specific sets of problems) available. -
Software Packages – Overview There Are Many Software Packages Available for Performing Statistical Analyses. the Ones Highlig
APPENDIX C SOFTWARE OPTIONS Software Packages – Overview There are many software packages available for performing statistical analyses. The ones highlighted here are selected because they provide comprehensive statistical design and analysis tools and a user friendly interface. SAS SAS is arguably one of the most comprehensive packages in terms of statistical analyses. However, the individual license cost is expensive. Primarily, SAS is a macro based scripting language, making it challenging for new users. However, it provides a comprehensive analysis ranging from the simple t-test to Generalized Linear mixed models. SAS also provides limited Design of Experiments capability. SAS supports factorial designs and optimal designs. However, it does not handle design constraints well. SAS also has a GUI available: SAS Enterprise, however, it has limited capability. SAS is a good program to purchase if your organization conducts advanced statistical analyses or works with very large datasets. R R, similar to SAS, provides a comprehensive analysis toolbox. R is an open source software package that has been approved for use on some DoD computer (contact AFFTC for details). Because R is open source its price is very attractive (free). However, the help guides are not user friendly. R is primarily a scripting software with notation similar to MatLab. R’s GUI: R Commander provides limited DOE capabilities. R is a good language to download if you can get it approved for use and you have analysts with a strong programming background. MatLab w/ Statistical Toolbox MatLab has a statistical toolbox that provides a wide variety of statistical analyses. The analyses range from simple hypotheses test to complex models and the ability to use likelihood maximization. -
Powerha Systemmirror Graphical User Interface (GUI)
IBM PowerHA SystemMirror for AIX Standard Edition Version 7.2 PowerHA SystemMirror Graphical User Interface IBM IBM PowerHA SystemMirror for AIX Standard Edition Version 7.2 PowerHA SystemMirror Graphical User Interface IBM Note Before using this information and the product it supports, read the information in “Notices” on page 17. This edition applies to IBM PowerHA SystemMirror 7.2 Standard Edition for AIX and to all subsequent releases and modifications until otherwise indicated in new editions. © Copyright IBM Corporation 2017, 2018. US Government Users Restricted Rights – Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents About this document ......... v Cluster zones .............. 9 Highlighting .............. v Troubleshooting PowerHA SystemMirror GUI ... 10 Case-sensitivity in AIX ........... v Configuring PowerHA SystemMirror GUI .... 11 ISO 9000................ v Changing ports ............ 11 Related information ............ v Roles and role-based access control ..... 12 Changing the default location of log files ... 13 PowerHA SystemMirror graphical user Configuring the PowerHA SystemMirror GUI to interface (GUI) ............ 1 be highly available ........... 14 Discovering a cluster as a non-root user .... 14 What's new in PowerHA SystemMirror Graphical User Interface .............. 1 Notices .............. 17 Planning for PowerHA SystemMirror GUI .... 2 Installing PowerHA SystemMirror GUI ..... 4 Privacy policy considerations ........ 19 Logging in to the PowerHA SystemMirror GUI -
On Reproducible Econometric Research
On Reproducible Econometric Research Roger Koenkera Achim Zeileisb∗ a Department of Economics, University of Illinois at Urbana-Champaign, United States of America b Department of Statistics and Mathematics, WU Wirtschaftsuniversit¨at Wien, Austria SUMMARY Recent software developments are reviewed from the vantage point of reproducible econo- metric research. We argue that the emergence of new tools, particularly in the open-source community, have greatly eased the burden of documenting and archiving both empirical and simulation work in econometrics. Some of these tools are highlighted in the discussion of two small replication exercises. Keywords: reproducibility, replication, software, literate programming. 1. INTRODUCTION The renowned dispute between Gauss and Legendre over priority for the invention of the method of least squares might have been resolved by Stigler(1981). A calculation of the earth's ellipticity reported by Gauss in 1799 alluded to the use of meine Methode; had Stigler been able to show that Gauss's estimate was consistent with the least squares solution using the four observations available to Gauss, his claim that he had been using the method since 1795 would have been strongly vindicated. Unfortunately, no such computation could be reproduced leaving the dispute in that limbo all too familiar in the computational sciences. The question that we would like to address here is this: 200 years after Gauss, can we do better? What can be done to improve our ability to reproduce computational results in econometrics and related fields? Our main contention is that recent software developments, notably in the open- source community, make it much easier to achieve and distribute reproducible research. -
Deducer: a Data Analysis GUI for R
JSS Journal of Statistical Software June 2012, Volume 49, Issue 8. http://www.jstatsoft.org/ Deducer: A Data Analysis GUI for R Ian Fellows University of California, Los Angeles Abstract While R has proven itself to be a powerful and flexible tool for data exploration and analysis, it lacks the ease of use present in other software such as SPSS and Minitab. An easy to use graphical user interface (GUI) can help new users accomplish tasks that would otherwise be out of their reach, and improves the efficiency of expert users by replacing fifty key strokes with five mouse clicks. With this in mind, Deducer presents dialogs that are understandable for the beginner, and yet contain all (or most) of the options that an experienced statistician, performing the same task, would want. An Excel-like spreadsheet is included for easy data viewing and editing. Deducer is based on Java's Swing GUI library and can be used on any common operating system. The GUI is independent of the specific R console and can easily be used by calling a text-based menu system. Graphical menus are provided for the JGR console and the Windows R GUI. Keywords: GUI, R. 1. Introduction R (R Development Core Team 2012) is a powerful statistical programming language that places the latest statistical techniques at one's fingertips through thousands of add-on packages available on the Comprehensive R Archive Network (CRAN) download servers. The price for all of this power is complexity. Because R analyses must be called as text commands, the user is required to find out the name of the function that will accomplish their task, and then remember that name along with the names of the variables to feed it, and its argument options. -
Maxim Integrated Page 1 of 37 Table of Contents
OneWireViewer User’s Guide, Version 1.6 UG3358; Rev 3; 6/19 Abstract This user's guide explains the OneWireViewer software program and how it can be used to evaluate the unique features of 1-Wire® and iButton® devices. Maxim Integrated Page 1 of 37 Table of Contents Introduction ................................................................................................................................... 5 Installation ..................................................................................................................................... 5 Download Bundled OneWireViewer and 1-Wire Drivers ........................................................... 5 Install Bundled OneWireViewer and 1-Wire Drivers .................................................................. 6 Starting the OneWireViewer Program ....................................................................................... 8 Uninstalling the OneWireViewer Program ................................................................................. 8 OneWireViewer Features ............................................................................................................ 10 Program Main Window ............................................................................................................ 10 Viewer Menus ......................................................................................................................... 10 File ...................................................................................................................................... -
Introducing SPM® Infrastructure
Introducing SPM® Infrastructure © 2019 Minitab, LLC. All Rights Reserved. Minitab®, SPM®, SPM Salford Predictive Modeler®, Salford Predictive Modeler®, Random Forests®, CART®, TreeNet®, MARS®, RuleLearner®, and the Minitab logo are registered trademarks of Minitab, LLC. in the United States and other countries. Additional trademarks of Minitab, LLC. can be found at www.minitab.com. All other marks referenced remain the property of their respective owners. Salford Predictive Modeler® Introducing SPM® Infrastructure Introducing SPM® Infrastructure The SPM® application is structured around major predictive analysis scenarios. In general, the workflow of the application can be described as follows. Bring data for analysis to the application. Research the data, if needed. Configure and build a predictive analytics model. Review the results of the run. Discover the model that captures valuable insight about the data. Score the model. For example, you could simulate future events. Export the model to a format other systems can consume. This could be PMML or executable code in a mainstream or specialized programming language. Document the analysis. The nature of predictive analysis methods you use and the nature of the data itself could dictate particular unique steps in the scenario. Some actions and mechanisms, though, are common. For any analysis you need to bring the data in and get some understanding of it. When reviewing the results of the modeling and preparing documentation, you can make use of special features embedded into charts and grids. While we make sure to offer a display to visualize particular results, there’s always a Summary window that brings you a standard set of results represented the same familiar way throughout the application.