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Cookbook for R

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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. · 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)! We welcome your feedback and comments; we’re actively monitoring our Github issue tracker and triage new incoming issues every Friday. 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:

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. Writing R Extensions Table of Contents This is a guide to extending R, describing the process of creating R add-on packages, writing R documentation, R’s system and foreign language interfaces, and the R API. This manual is for R, version 3.1.0 (2014-04-10). Copyright © 1999–2013 R Core Team

Using R for statistical analyses - Basic Stats Chi-squared tests Tests for association are easily performed in R. The basc function is chisq.test() The first stage is to arrange your data in a .CSV file. Use row and column names. Don't forget that variable names in R can contain letters and numbers but the only punctuation allowed is a period. 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. Being ignorant of a rule does not excuse you if you break it. Read these before reading anything else. 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.

Model visualisation. 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. Epistemic, ontological and aleatory risk « Critical Uncertainties What do an eighteenth century mathematician and a twentieth century US Secretary of Defence have to do with engineering and risk? The answer is that both thought about uncertainty and risk, and the differing definitions that they arrived at neatly illustrate that there is more to the concept of risk than just likelihood multiplied by consequence. Which in turn has significant implications for engineering risk management. Editorial note. I’ve pretty much completely revised this post since the original, hope you like it The concept of risk allows us to make decisions in an uncertain world where we cannot perfectly predict future outcomes.

Pearson's Chi-squared Test for Count Data Description chisq.test performs chi-squared contingency table tests and goodness-of-fit tests. Usage chisq.test(x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)), rescale.p = FALSE, simulate.p.value = FALSE, B = 2000) Arguments free-programming-books Index Graphics Programming Graphical User Interfaces