This guide is aimed at users who have this facility. The most convenient way to use R is at a graphics workstation running a windowing system. Thus whereas SAS and SPSS will give copious output from a regression or discriminant analysis, R will give minimal output and store the results in a fit object for subsequent interrogation by further R functions. In S a statistical analysis is normally done as a series of steps, with intermediate results being stored in objects. ![]() There is an important difference in philosophy between S (and hence R) and the other main statistical systems. Most classical statistics and much of the latest methodology is available for use with R, but users may need to be prepared to do a little work to find it. ![]() More details on packages are given later (see Packages). There are about 25 packages supplied with R (called “standard” and “recommended” packages) and many more are available through the CRAN family of Internet sites (via ) and elsewhere. A few of these are built into the base R environment, but many are supplied as packages. We prefer to think of it of an environment within which many classical and modern statistical techniques have been implemented. Our introduction to the R environment did not mention statistics, yet many people use R as a statistics system. See section ‘What documentation exists for R?’ in the ‘R FAQ’ manual for more information. There are now a number of books which describe how to use R for data analysis and statistics, and documentation for S/ S-PLUS can typically be used with R, keeping the differences between the S implementations in mind. The formal methods and classes of the methods package are based on those described in Programming with Data by John M. The new features of the 1991 release of S are covered in Statistical Models in S edited by John M. For R, the basic reference is The New S Language: A Programming Environment for Data Analysis and Graphics by Richard A. The evolution of the S language is characterized by four books by John Chambers and coauthors. R can be regarded as an implementation of the S language which was developed at Bell Laboratories by Rick Becker, John Chambers and Allan Wilks, and also forms the basis of the S-PLUS systems. ![]() However, most programs written in R are essentially ephemeral, written for a single piece of data analysis. It has developed rapidly, and has been extended by a large collection of packages. ![]() R is very much a vehicle for newly developing methods of interactive data analysis. The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software. (Indeed most of the system supplied functions are themselves written in the S language.)
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |