Download An Introduction to R by William N. Venables, David M. Smith PDF

By William N. Venables, David M. Smith

This educational guide presents a complete advent to R, a software program package deal for statistical computing and photographs. R helps quite a lot of statistical concepts and is well extensible through user-defined features. certainly one of R's strengths is the convenience with which publication-quality plots could be produced in a wide selection of codecs. this can be a published version of the educational documentation from the R distribution, with extra examples, notes and corrections. it's in keeping with R model 2.9.0, published April 2009. R is unfastened software program, dispensed less than the phrases of the GNU normal Public License (GPL). it may be used with GNU/Linux, Unix and Microsoft home windows. all of the funds raised from the sale of this booklet helps the advance of loose software program and documentation.

Show description

Read or Download An Introduction to R PDF

Similar mathematical & statistical books

Doing Data Analysis with SPSS: Version 18.0

Now up to date for SPSS information model 18, DOING information research WITH SPSS is a wonderful complement to any introductory information direction. It offers a realistic and precious creation to SPSS and allows scholars to paintings independently to benefit priceless software program talents outdoors of sophistication. through the use of SPSS to address advanced computations, scholars can concentrate on and achieve an figuring out of the underlying statistical options and methods within the introductory records path.

The Minimum Description Length Principle

The minimal description size (MDL) precept is a robust approach to inductive inference, the foundation of statistical modeling, development popularity, and laptop studying. It holds that the simplest rationalization, given a constrained set of saw info, is the one who allows the best compression of the information.

Analysis of Integrated and Cointegrated Time Series with R

The research of built-in and co-integrated time sequence will be regarded as the most method hired in utilized econometrics. This booklet not just introduces the reader to this subject yet allows him to behavior a number of the unit root assessments and co-integration tools on his personal by using the loose statistical programming setting R.

EnvStats: An R Package for Environmental Statistics

This ebook describes EnvStats, a brand new complete R package deal for environmental data and the successor to the S-PLUS module EnvironmentalStats for S-PLUS (first published in 1997). EnvStats and R supply an open-source set of strong features for acting graphical and statistical analyses of environmental facts, bringing significant environmental statistical equipment present in the literature and regulatory information files into one statistical package deal, besides an in depth hypertext aid approach that explains what those tools do, the right way to use those tools, and the place to discover them within the environmental statistics literature.

Additional info for An Introduction to R

Example text

The result of the function is a list giving not only the efficiency factors as the first component, but also the block and variety canonical contrasts, since sometimes these give additional useful qualitative information. 2 Dropping all names in a printed array For printing purposes with large matrices or arrays, it is often useful to print them in close block form without the array names or numbers. Removing the dimnames attribute will not achieve this effect, but rather the array must be given a dimnames attribute consisting of empty strings.

Are factors. The following formulae on the left side below specify statistical models as described on the right. y~x y~1+x Both imply the same simple linear regression model of y on x. The first has an implicit intercept term, and the second an explicit one. y~0+x y ~ -1 + x y ~ x - 1 Simple linear regression of y on x through the origin (that is, without an intercept term). log(y) ~ x1 + x2 Multiple regression of the transformed variable, log(y), on x1 and x2 (with an implicit intercept term).

Chapter 11: Statistical models in R 58 deviance(object ) Residual sum of squares, weighted if appropriate. formula(object ) Extract the model formula. plot(object ) Produce four plots, showing residuals, fitted values and some diagnostics. frame ) The data frame supplied must have variables specified with the same labels as the original.

Download PDF sample

Rated 4.30 of 5 – based on 49 votes