Quick-R: Home Page Seeing Theory - Basic Probability Chance Events Randomness is all around us. Probability theory is the mathematical framework that allows us to analyze chance events in a logically sound manner. The probability of an event is a number indicating how likely that event will occur. A classic example of a probabilistic experiment is a fair coin toss, in which the two possible outcomes are heads or tails. Flip the Coin Flip 100 times For an unfair or weighted coin, the two outcomes are not equally likely.
alyssa frazee Thu 02 January 2014 | -- (permalink) My sister is a senior undergraduate majoring in sociology. She just landed an awesome analyst job for next semester and was told she'll be using some R in the course of her work. She asked me to show her the ropes during winter vacation, and of course I said yes! What better way to while away the days of a Minnesota winter?!1 One catch: the day we planned to work, it turned out we only had an hour of overlapping free time. Challenge accepted. (1) download R and RStudio I'm impressed that RStudio is both accessible/helpful for beginners and useful for experts. (2) console and script The first thing we did after getting set up was type two lines into the console: It wasn't exactly "hello world", but it illustrated some concepts like "assignment" and "variables" and "evaluation"2. The next thing I had my sister do was save those two lines of code in an R script. (3) comments # COMMENTS ARE SUPER IMPORTANT so we learned about them (4) graphics (5) getting help
R Programming 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]
Guide Hub Welcome to the Guide Hub! If you're looking for answers or advice, you should be able to find either of them below. If you have an idea for a Guide that you'd like to see added to this page, contact a member of Senior Staff and show them a draft. Site Rules: You will be expected to know and understand the rules of the wiki. Guide for Newbies: A guide that covers site applications, general site behavior, tips for being a good member, tips on writing, and a list of senior staff. How to Contribute: A general guide to the kinds of fiction on the SCP Wiki and how to contribute to them.How to Write an SCP: Exactly what it says on the tin. Writing the Foundation Universe Specifics of the SCP-verse Formatting Your Pages Advanced Formatting and You: Instructions for how to do fancy things with wikidot coding! Developing Your Style Rules of Thumb: A collection of rules of thumb, compiled and collected from experienced members of the site.Conservation of WTF or, ‘Why does the rabbit need two brains?’
· R Tools for Visual Studio Welcome to R Tools for Visual Studio Preview! About this release THANK YOU for trying out this second preview release of R Tools for Visual Studio (RTVS)! Of course, we remind you that this release is meant for evaluation purposes only and not for production use. If you already have VS2015 with Update 1 (or higher) installed and R installed, you can download RTVS from the link below - but we highly recommend following the Installation guide: Download R Tools for Visual Studio Meet the RTVS Engineering Team in New York City on May 12! Are you in the NYC area on May 12th? If interested, please sign up here. Key features in Version 0.3 For an overview of what is new in 0.3, please see our What’s New in 0.3 page. Completely New Features for 0.3 Package Manager - Graphical package manager Code Snippets - Insert code snippets with simple keystrokes to write code faster Code Navigation - Quickly navigate to different functions within your project Existing and Improved Features A quick video overview Q.
Model visualisation. had.co.nz This page lists my published software for model visualisation. This work forms the basis for the third chapter of my thesis. classifly: Explore classification boundaries in high dimensions. Given p-dimensional training data containing d groups (the design space), a classification algorithm (classifier) predicts which group new data belongs to. Generally the input to these algorithms is high dimensional, and the boundaries between groups will be high dimensional and perhaps curvilinear or multi-facted. This R package provides methods for visualising the division of space between the groups. clusterfly: Explore clustering results in high dimensions. Typically, there is somewhat of a divide between statistics and visualisation software. There are also some custom methods for certain types of clustering, mostly inspired by the work of Dr Dianne Cook: Self organising maps (aka Kohonen neural networks), ? meifly: Models explored interactively. Installation Presentations/publications
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R Moves Up From #9 to #6, But What Does It Mean to Really be Proficient in a Language? There's been a lot of noise in the data science community this past week about IEEE Spectrum's 2015 language rankings, where R moved up three notches from #9 in 2014 to #6 in 2015. The Spectrum post gives some lip service to needing to know a domain in addition to just the language itself. But here I drill down into what it means to really know a language. API for the standard library. I was first introduced to this concept about 18 years ago in my transition from C++ when I was interviewing for jobs that used Java. In one interview, I said Java was easy step from C++, and the interviewer responded, "But it's know all of the API that's the bigger step." So, do you really know R?
Highland Statistics Ltd Jump straight to Price and Order the book Outline Keywords Table of Contents Data sets and R code used Video files Support chapters Discussion board Outline This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts. The book uses the functions glm, lmer, glmer, glmmADMB, and also JAGS from within R. R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage. Readers of this book have free access to: Chapter 1 of Zero Inflated Models and Generalized Linear Mixed Models with R. (2012a) Zuur, Saveliev, Ieno. See the Preface (and the text below) how to access the pdfs of these chapters. Keywords Table of contents Click for Table of contents Price and Order the book The paperback is priced at 49 GBP. Copyright statement This book is copyright material from Highland Statistics Ltd. Data sets and R code used in the book. Video file with general comments Alain Zuur Support chapters
I saw someone post a few of these earlier, and decided someone might as well upload the full set.