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
Unable to get R language system2 command result 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]
Data Analytics for Beginners: Part 1 | statsguys If you want more practice data projects, be sure to check out What You Will Learn: This post is meant for ANYONE interested in learning more about data analytics and is made so that you can follow along even with no prior experience in R. Some background in Statistics would be helpful (making the fancy words seem less fancy) but neither is it necessary. Why You Should Follow Along: Are you a professional looking to develop additional skills that will make you high demand in the job market? Hal Varian, Chief Economist at Google, said this about the field of Data Analytics and Data Science: If you are looking for a career where your services will be in high demand, you should find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. Who We Are: Table of Contents: Tips for following along Feel free to ask questions on our Kaggle Forum post and we will respond as soon as possible! Installing R and Rstudio Conclusion
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]