background preloader

R by example

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

Software Resources for R Software Resources for R Below is a list of resource pages for using R to do statistics. On each page a set of data are explored with the software. Getting Started Entering data Simple summary statistics Stem and leaf plots Histograms Boxplots Dotplots Logarithmic transformations Saving your data Summarizing Quantitative Data Simple summary statistics Boxplots comparing two groups Transformations Summarizing a Single Categorical Variable Mode Tables Bar charts Pie charts Making and Interpreting Tables for Two Categorical Variables One- and Two-Way Tables Probability and two-way tables Inference for One Proportion Confidence interval Hypothesis test Inference for Two Proportions Chi-Squared Tests One-way (Goodness of Fit) Two-way (Contingency Tables) Inference for a Single Mean Inference for Two Means (Independent Samples) Inference for Paired Differences Scatterplots and Correlation Transformations in R Straightening a curved relationship by transforming a variable Simple Linear Regression

Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Multivariate analysis of variance Multivariate analysis of variance or multiple analysis of variance (MANOVA) is a statistical test procedure for comparing multivariate (population) means of several groups. Unlike univariate ANOVA, it uses the variance-covariance between variables in testing the statistical significance of the mean differences. It is a generalized form of univariate analysis of variance (ANOVA). It is used when there are two or more dependent variables. It helps to answer: 1. do changes in the independent variable(s) have significant effects on the dependent variables?; 2. what are the interactions among the dependent variables? Where sums of squares appear in univariate analysis of variance, in multivariate analysis of variance certain positive-definite matrices appear. Analogous to ANOVA, MANOVA is based on the product of model variance matrix, and inverse of the error variance matrix, , or . implies that the product The most common[3][4] statistics are summaries based on the roots (or eigenvalues) of the

Learning R 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. 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. We also assume that readers are familiar with computers and that they know how to use software with a command-line interface. See also[edit] Larry Wasserman's book All of Statistics[6]The Statistics and the Econometric Theory wikibooks.The Econometrics and Statistics pages on wikipedia. References[edit]

StatNotes: Topics in Multivariate Analysis, from North Carolina State University Looking for Statnotes? StatNotes, viewed by millions of visitors for the last decade, has now been converted to e-books in Adobe Reader and Kindle Reader format, under the auspices of Statistical Associates Publishers. The e-book format serves many purposes: readers may cite sources by title, publisher, year, and (in Adobe Reader format) page number; e-books may be downloaded to PCs, Ipads, smartphones, and other devices for reference convenience; and intellectual property is protected against piracy, which had become epidemic. Click here to go to the new Statnotes website at . Or you may use the Google search box below to search the website, which contains free e-books and web pages with overview summaries and tables of contents. Or you may click on a specific topic below to view the specific overview/table of contents page.

start [R Wiki] * R is a free software environment for statistical computing and graphics. It runs on a wide variety of UNIX platforms, Windows and MacOS. This R Wiki is dedicated to the collaborative writing of R documentation. For information on browsers, RSS syndication, copyright, ... read usage. R comes with several official manuals and FAQs. These should be your primary source of information. Personal note: I have some difficulties with the statement that the official manuals should be your primary source of information. Books I found ordinary books to be the most helpful source of information for novice programmers and I can particularly recommend: Adler, J. (2010) R in a nutshell. Help forum for programmers The R-Help mailing list can give excellent answers, but due to the high calibre of respondents it can be intimidating, (and off-putting). In addition I can’t but feel that many questions are a waste of time for most of those who respond. Stackoverflow

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] The R Project for Statistical Computing Quantitative Methods In Linguistics (9781405144254): Keith Johnson 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. You have filtered out all documents.

Basic Widget Methods Widget (class) [#] Widget implementation class. This class is used as a mixin by all widget classes. after(delay_ms, callback=None, *args) [#] Registers an alarm callback that is called after a given time. This method registers a callback function that will be called after a given number of milliseconds. The callback is only called once for each call to this method. class App: def __init__(self, master): self.master = master self.poll() def poll(self): ... do something ... self.master.after(100, self.poll) after_cancel to cancel the callback. You can also omit the callback. delay_ms Delay, in milliseconds. callback The callback. *args Optional arguments that are passed to the callback. Returns: An alarm identifier. after_cancel(id) [#] Cancels an alarm callback. id Alarm identifier. after_idle(callback, *args) [#] Registers a callback that is called when the system is idle. bbox(column=None, row=None, col2=None, row2=None) [#] The bbox method. column row col2 row2 bell(displayof=0) [#] displayof cget(key) [#]

Analyzing Linguistic Data: A Practical Introduction to Statistics using R (9780521709187): R. H. Baayen Library Tcl8.6.1/Tk8.6.1 Documentation > Tcl C API, version 8.6.1 Tcl/Tk Applications | Tcl Commands | Tk Commands | [incr Tcl] Package Commands | SQLite Package Commands | TDBC Package Commands | tdbc::mysql Package Commands | tdbc::odbc Package Commands | tdbc::sqlite3 Package Commands | Thread Package Commands | Tcl C API | Tk C API | [incr Tcl] Package C API | TDBC Package C API Copyright © 1989-1994 The Regents of the University of California Copyright © 1992-1999 Karl Lehenbauer and Mark Diekhans Copyright © 1994-1998 Sun Microsystems, Inc Copyright © 1997-2000 Ajuba Solutions Copyright © 1998-2000 Scriptics Corporation Copyright © 2001 ActiveState Corporation Copyright © 2001 ActiveState Tool Corp Copyright © 2001 Vincent Darley Copyright © 2001-2002 Kevin B.