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R Statistical Software
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- Performing calculation for statistical analysis can be very tough as it involves a lot of precision and care. This can be done easily with the help of certain statistical software. These statistical analysis software, free download are free to use and can be used for doing the calculations. Some of the free statistical analysis software or SPSS statistical software, free download has a user.
- R Tutorial Obtaining R. R is available for Linux, MacOS, and Windows. Software can be downloaded from The Comprehensive R Archive Network (CRAN). After R is downloaded and installed, simply find and launch R from your Applications folder.
- Stata for Mac includes software and PDF documentation, which includes access to all the manuals. Stata for Mac comes in three flavors: Stata/MP (64-bit Intel-based Macs only), Stata/SE, and Stata/IC.
R is a free software for statistical analysis and graphics.
It runs on various UNIX platforms, Windows, and MacOS.
The latest version, 2.12.1, was released on December 16, 2010.
Since 1997 an international core team of about 15 people develops R.
It runs on various UNIX platforms, Windows, and MacOS.
The latest version, 2.12.1, was released on December 16, 2010.
Since 1997 an international core team of about 15 people develops R.
Download R 3.2.2 for Windows. Create statistical graphs and displays with R.
screenshot of R running on Unix
- 1What is R?
What is R?
R is widely used for statistical software development and data analysis, and has become a de-facto standard among statisticians for the development of statistical software. R's source code is freely available under the GNU General Public License, and pre-compiled binary versions are provided for Microsoft Windows, Mac OS X, and several Linux and other Unix-like operating systems. R uses a command line interface, though several graphical user interfaces are available. wikipedia entry on R
Features
'R has many functions for statistical analyses and graphics; the latter are visualized immediately in their own window and can be saved in various formats (jpg, png, bmp, ps, pdf, emf, pictex, xfig; the available formats may depend on the operating system). The results from a statistical analysis are displayed on the screen, some intermediate results (P-values, regression coefficients, residuals,..) can be saved, written in a file, or used in subsequent analyses.
The R language allows the user, for instance, to program loops to successively analyse several data sets. It is also possible to combine in a single program different statistical functions to perform more complex analyses. The R users may benefit from a large number of programs written for S and available on the internet, most of these programs can be used directly with R.
At first, R could seem too complex for a non-specialist. This may not be true actually. In fact, a prominent feature of R is its flexibility.'
History
R was originally created by Ross Ihaka and Robert Gentleman (hence the name R) at the University of Auckland, New Zealand, and is now developed by the R Development Core Team. R is considered by its developers to be an implementation of the S programming language, with semantics derived from Scheme.
Install R
- choose a download mirror: list of mirror sites for R download
- download the right package for you (Linux/Windows/Mac)
- install the package following the OS-specific instructions
Use R
To use R you will have to learn some R commands (see screenshot), i.e. it's not fully menu based like most Windows and Mac software. This might seem tedious but you will soon realise that while slowing you down initially it will speed up your work and make it better after an initial learning period.
There is a lot of free documentation available. Shorter manuals are first in the list:
- Friendly Beginners' R Course by Toby Marthews, ZIP archive containing examples & 12 page PDF
- short introduction to R by Jim Lemon, HTML v1.6
- R for beginners by Emmanuel Paradis, PDF 76 pages
- R tutorial from Biology Fac, University of Alaska Fairbanks, HTML
- R tutorial from Maths, Union College, NY - very clear layout, HTML
- R tutorial by Vincent Zoonekynd, Maths, Université P.M. Curie, France, HTML
- Official R project manual, also available as PDF
You can find the complete listings on the R project webpage: manuals, contributed manuals.
Publications
- PLoS: A Quick Guide to Teaching R Programming to Computational Biology Students by Stephen J. Eglen*, Cambridge Computational Biology Institute, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
Examples for commonly used statistics
Bioconductor & Microarray data Analysis
Links
- home of the R project: manuals, FAQs, download,.
Retrieved from 'https://openwetware.org/mediawiki/index.php?title=R_Statistics&oldid=478260'
R/qtl: A QTL mapping environment
Software for mapping quantitative trait loci inexperimental crosses
Free Statistical Software Online
Current version: 1.46-2 (2020-02-28)
[ Download | FAQ | News | Bugs | Samplegraphics |Sample data |Tutorials |Book |Manual | Citation ]
Try theR/qtlcharts package:interactive graphics for QTL data.
Check out our book:A Guide to QTL Mapping with R/qtl, by Karl W. Broman andŚaunak Sen.
About R/qtl | About R |
R/qtl is an extensible, interactive environment for mapping quantitative trait loci (QTL) in experimental crosses. It is implemented as an add-on package for the freely available and widely used statistical language/software R (see the R project homepage). The development of this software as an add-on to R allows us to take advantage of the basic mathematical and statistical functions, and powerful graphics capabilities, that are provided with R. Further, the user will benefit by the seamless integration of the QTL mapping software into a general statistical analysis program. Our goal is to make complex QTL mapping methods widely accessible and allow users to focus on modeling rather than computing. A key component of computational methods for QTL mapping is the hidden Markov model (HMM) technology for dealing with missing genotype data. We have implemented the main HMM algorithms, with allowance for the presence of genotyping errors, for backcrosses, intercrosses, and phase-known four-way crosses. The current version of R/qtl includes facilities for estimating genetic maps, identifying genotyping errors, and performing single-QTL genome scans and two-QTL, two-dimensional genome scans, by interval mapping (with the EM algorithm), Haley-Knott regression, and multiple imputation. All of this may be done in the presence of covariates (such as sex, age or treatment). One may also fit higher-order QTL models by multiple imputation and Haley-Knott regression. R/qtl is distributed as source code for unix or compiled code for Windows or Mac. R/qtl is released under the GNU General Public License. To download the software, you must agree to the terms in that download. | R is an open-source implementation of the S language. As described onthe R projecthomepage: 'R is a system for statistical computation andgraphics. It consists of a language plus a run-time environment withgraphics, a debugger, access to certain system functions, and theability to run programs stored in script files. 'The core of R is an interpreted computer languagewhich allows branching and looping as well as modular programmingusing functions. Most of the user-visible functions in R are writtenin R. It is possible for the user to interface to procedures writtenin the C, C++, or FORTRAN languages for efficiency. The R distributioncontains functionality for a large number of statisticalprocedures. Among these are: linear and generalized linear models,nonlinear regression models, time series analysis, classicalparametric and nonparametric tests, clustering and smoothing. There isalso a large set of functions which provide a flexible graphicalenvironment for creating various kinds of datapresentations. Additional modules are available for a variety ofspecific purposes.' R is freely available for Windows, unix and MacOS, and may be downloaded from the Comprehensive R Archive Network (CRAN). Learning R may require a formidable investment of time, but it will definitely be worth the effort. Numerous free documents on getting started with R are available on CRAN. In addition, several books are available on R, S and S-PLUS; for example, see WN Venables, BD Ripley (2002) Modern Applied Statistics with S (4thed, Springer) or P Dalgaard (2008) Introductory statistics with R (2nd ed, Springer). See my Introduction to R page for further links. |
Contact forproblems/questions/suggestions: Karl W Broman
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Authors: Karl W Broman and Hao Wu, with ideas from Gary Churchill and Śaunak Sen and contributions from Danny Arends, Timothée Flutre, Ritsert Jansen, Pjotr Prins, Lars Rönnegård, Rohan Shah, Laura Shannon, Quoc Tran, Aaron Wolen, and Brian Yandell
Google Groups: We've created two GoogleGroups for email announcements regarding software updates (R/qtl announcements)and for discussion about the use of the software (R/qtl discussion). Note that youshould join just one of these two groups; all announcements will alsobe sent to the discussion group.
Free Download R Statistical Software For Mac
Other QTL mapping software
MapMaker/QTL | GeneNetwork |
QTL Cartographer | MultiQTL |
R/qtlDesign | The QTL Cafe |
HAPPY | Kajsa Ljungberg's software |
MapQTL | QTLMap |
Multimapper |
[ Download | FAQ | News | Bugs | Samplegraphics |Sample data |Tutorials |Book |Manual | Citation ]