Krause / Olson The Basics of S-PLUS

4th Auflage 2005
ISBN: 978-0-387-28390-6
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 444 Seiten, eBook

Reihe: Statistics and Computing

Thisisnowthefourtheditionof“TheBasicsofS-Plus”since1997.S-Plus saw a steady growth in popularity, and it established itself in many edu- tional and business places as a major data analysis tool.S-Plus is valued for its modern, interactive data analysis environment, whether it is the p- mary system or a complement to other standards like SAS (the latter is in particular true for the industry we work in, pharmaceuticals). We have followed the various releases with new editions of our book, introducing over time major changes like the incorporation of S Version 4 (the underlying language), Trellis graphs, a graphical user interface, in particular for the Windows operating system, and a chapter on R and its di?erencestoS-Plus(thatareminorforthematerialcoveredinthisbook). Thiseditionisanupdatefromedition3tocovernewfunctionsandfeatures ofS-Plus Version 7.0 (working from the beta release for MS Windows and Linux), adding more practical tips and examples, and correcting a few mistakes. We are very grateful to all our readers, in particular those sending us suggestions, comments, and any other kind of feedback. You will see some of these re?ected in the book.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Krause, Andreas

Olson, Melvin

Inhaltsverzeichnis

Graphical User Interface.- A First Session.- A Second Session.- Graphics.- Trellis Graphics.- Exploring Data.- Statistical Modeling.- Programming.- Object-Oriented Programming.- Input and Output.- Tips and Tricks.- S-Plus Internals.- Information Sources on and Around S-Plus.- R.

Leseproben

7 Exploring Data  (p. 193)

In the preceding chapters, we have laid the foundation for understanding the concepts and ideas of the S-Plus system. We explored basic ideas and how to use S-Plus for performing calculations, and we have seen how data can be generated, stored, and accessed. Furthermore, we also looked at how data can be displayed graphically. All this will be useful as we explore real data sets in this chapter. We will explore data sets that come with S-Plus, speci.cally the Barley and Geyser data sets.

Rather than presenting a list of available statistical functions, we will go through a typical data analysis as a way of introducing the more useful and common commands and the kind of output we’ll encounter. We chose to use S-Plus data sets so you can follow along with the analysis we present and complete the exercises at the end of this chapter. We divide the data analysis into two categories: "descriptive" and "graphical" exploration. Further sections cover distributions and related functions, con.rmatory statistics and hypothesis testing, and missing and in.nite values.

7.1 Descriptive Data Exploration

We will now explore the di.erent variables contained in the Barley data set. We will first analyze the variables in one dimension, or, in other words, we will take a univariate approach. The analysis of the dependence between the variables and the exploration of higher-dimensional structure follows later.


The Barley Data Set

The Barley data are measurements of yield in bushels per acre at di.erent sites. The analysis comprises 6 sites planting 10 di.erent varieties of barley in 2 successive years, 1931 and 1932. The data set therefore contains 120 measurements of barley yield. Our main goal will be to investigate di.erences in barley yields given by the di.erent variable constellations, such as the 1931 harvest of the .fth variety on site 4 and the 1932 harvest of the seventh variety at the same site.
Just enter
              > barley
to see the data.Exploratory data analysis (EDA) is an approach to investigating data that stresses the need to know more about the structure and information inherent in the data. The methods used with this approach are referred to as descriptive, as opposed to con.rmatory. Descriptive simply means that simple summaries are used to describe the data: their shapes, sizes, relationships, and the like. Examples of descriptive statistics are means, medians, standard deviations, ranges, and so on.

Given the basic information about the Barley data, the following analysis is intended to gain more information and structural knowledge about the numbers we have.

A typical place to begin is, of course, looking at the data. If the data set is small, we can easily look at it simply by printing it out. We check the data size by entering
               > dim(barley)
                     120 4