A handbook of statistical analyses using R - download pdf or read online

By Torsten Hothorn

ISBN-10: 1420079336

ISBN-13: 9781420079333

Like the best-selling first variations, A guide of Statistical Analyses utilizing R, 3rd Edition offers an up to date consultant to information research utilizing the R process for statistical computing. The e-book explains the best way to behavior various statistical analyses, from uncomplicated inference to recursive partitioning to cluster analysis.

New to the 3rd Edition

  • Three new chapters on quantile regression, lacking values, and Bayesian inference
  • Extra fabric within the logistic regression bankruptcy that describes a regression version for ordered express reaction variables
  • Additional exercises
  • More exact motives of R code
  • New part in every one bankruptcy summarizing the result of the analyses
  • Updated model of the HSAUR package deal (HSAUR3), inclusive of a few slides that may be utilized in introductory information courses

Whether you’re a knowledge analyst, scientist, or pupil, this instruction manual indicates you the way to simply use R to successfully review your information. With various real-world examples, it emphasizes the sensible program and interpretation of results.

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Additional info for A handbook of statistical analyses using R

Example text

2 Raw scatterplot of the logarithms of market value and sales. 8 Organising an Analysis Although it is possible to perform an analysis typing all commands directly on the R prompt it is much more comfortable to maintain a separate text file collecting all steps necessary to perform a certain data analysis task. 4 Boxplots of the logarithms of the market value for four selected countries, the width of the boxes is proportional to the square roots of the number of companies. © 2010 by Taylor and Francis Group, LLC SUMMARY 23 examples of these functions and those that produce more interesting graphics in later chapters.

Simple graphical tools for the inspection of the empirical distributions are introduced later on and in Chapter 2. 2 Customising Analyses In the preceding sections we have done quite complex analyses on our data using functions available from R. However, the real power of the system comes © 2010 by Taylor and Francis Group, LLC 16 AN INTRODUCTION TO R to light when writing our own functions for our own analysis tasks. Although R is a full-featured programming language, writing small helper functions for our daily work is not too complicated.

66 extracts the variables name, sales, profits and assets for the three largest companies. frame by R> companies <- Forbes2000$name which is equivalent to the previously shown statement R> companies <- Forbes2000[,"name"] We might be interested in extracting the largest companies with respect to an alternative ordering. The three top selling companies can be computed along the following lines. First, we need to compute the ordering of the companies’ sales R> order_sales <- order(Forbes2000$sales) which returns the indices of the ordered elements of the numeric vector sales.

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A handbook of statistical analyses using R by Torsten Hothorn


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