Software - Miquel De Cáceres Ainsa Indicspecies R package Indicator species are species that are used as ecological indicators of community or habitat types, environmental conditions, or environmental changes. In order to determine indicator species, the characteristic to be predicted is represented in the form of a classification of the sites, which is compared to the patterns of distribution of the species found in that set of sites. 'Indicspecies' is an R package that contains a set of functions to assess the strength of relationship between species and a classification of sites. As such, it includes the well-known IndVal method (Dufrêne & Legendre 1997) and extends it by allowing the user to study combinations of site groups (De Cáceres et al. 2010). Apart from the IndVal index, the package allows computing many other indices suitable for this kind of associations (De Cáceres & Legendre 2009), such as the phi coefficient of association. Download indicspecies (ver. 1.6.7) from CRAN. Resniche R package STI R package
R by example Basics Reading files Graphs Probability and statistics Regression Time-series analysis All these examples in one tarfile. Outright non-working code is unlikely, though occasionally my fingers fumble or code-rot occurs. Other useful materials Suggestions for learning R The R project is at : In particular, see the `other docs' there. Over and above the strong set of functions that you get in `off the shelf' R, there is a concept like CPAN (of the perl world) or CTAN (of the tex world), where there is a large, well-organised collection of 3rd party software, written by people all over the world. The dynamism of R and of the surrounding 3rd party packages has thrown up the need for a newsletter, R News. library(help=boot) library(boot) ? But you will learn a lot more by reading the article Resampling Methods in R: The boot package by Angelo J. Ajay Shah, 2005
knitr: Elegant, flexible and fast dynamic report generation with R | knitr Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ’s, see README. This website serves as the full documentation of knitr, and you can find the main manual, the graphics manual and other demos / examples here. For a more organized reference, see the knitr book. Motivation One of the difficulties with extending Sweave is we have to copy a large amount of code from the utils package (the file SweaveDrivers.R has more than 700 lines of R code), and this is what the two packages mentioned above have done. Features Acknowledgements Misc
R Programming - Wikibooks, collection of open-content textbooks Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN)[2] and many resources in specialized books, forums such as Stackoverflow[3] and personal blogs[4], but all of these resources are scattered and therefore difficult to find and to compare. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. How can you share your R experience ? Explain the syntax of a commandCompare the different ways of performing each task using R.Try to make unique examples based on fake data (ie simulated data sets).As with any Wikibook please feel free to make corrections, expand explanations, and make additions where necessary. Some rules : Prerequisites[edit] We assume that readers have a background in statistics. See also[edit]
Common Concepts in Statistics [M.Tevfik DORAK] Genetics Population Genetics Genetic Epidemiology Bias & Confounding Evolution HLA MHC Homepage M.Tevfik Dorak, MD, PhD Please use this address next time: See also Common Terms in Mathematics; Statistical Analysis in HLA & Disease Association Studies; Epidemiology (incl. For more LINKS, see the end of this page [Please note that the best way to find an entry is to use the Find option from the Edit menu, or CTRL + F] Absolute risk: Probability of an event over a period of time; expressed as a cumulative incidence like 10-year risk of 10% (meaning 10% of individuals in the group of interest will develop the condition in the next 10 year period). Accuracy: The degree to which a parameter (like the mean) is immune systematic error or bias. Addition rule: The probability of any of one of several mutually exclusive events occurring is equal to the sum of their individual probabilities. ANCOVA: See covariance models.
How to use R R is a powerful, free and open source, cross-platform, statistical and graphing software package;programming language;software environment for statistical computing. Downloading R[edit] Visit the R Project home page. Tutorials[edit] Books that are Helpful When Learning R[edit] See also[edit] External links[edit] Books[edit] Ecological Models and Data in R This is the web site for a book published by Princeton University Press (ISBN 0691125228). It is available from Princeton University Press and Amazon.com. Data and scripts for labs: Other data and scripts: Most of the data for the book are available in the emdbook package on CRAN. Most of the R code for doing things in the book is now in the two packages bbmle (also available in a development version) and emdbook, both available from R archive (CRAN) or via install.packages from inside R. Other miscellaneous R code: pdfhtmlxmlRnwR Warning: everything below here may be somewhat out of date ...If you want to see the existing notes for the course, start here. Old PDFs An old draft: 3 August 2007 (PDF, 6 MB). Individual chapters Last update: 27 December 2006 Formats: PDF, Rnw (Sweave -- original "source code"), R (R code only)
Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook This free ebook has been provided as a public service since 1995. Statistics: Methods and Applications textbook offers training in the understanding and application of statistics and data mining. It covers a wide variety of applications, including laboratory research (biomedical, agricultural, etc.), business statistics, credit scoring, forecasting, social science statistics and survey research, data mining, engineering and quality control applications, and many others. The Textbook begins with an overview of the relevant elementary (pivotal) concepts and continues with a more in depth exploration of specific areas of statistics, organized by "modules", representing classes of analytic techniques. A glossary of statistical terms and a list of references for further study are included. You have filtered out all documents.