Bøger i Use R! serien

- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.

This environment should be such that one can: write short scripts to de?ne a Bayesian model use or write functions to summarize a posterior distribution use functions to simulate from the posterior distribution construct graphs to illustrate the posterior inference An environment that meets these requirements is the R system.

R-code for examples in the book

Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes.

Once the model has been introduced it is used to generate synthetic data, using R code, and these generated data are then used to estimate its parameters. This sequence enhances understanding of both the time series model and the R function used to fit the model to data.

This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables.

Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. (6) gene set enrichment analysis. Each chapter of this book describes an analysis of real data using hands-on example driven approaches.

Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data.

RExcel seamlessly integrates the entire set of R's statistical and graphical methods into Excel, allowing students to focus on statistical methods and concepts and minimizing the distraction of learning a new programming language.

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics.

Written by the author of the lattice system, this book describes lattice in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed.

Scientists often collect samples of curves and other functional observations, and develop models where parameters are also functions. This volume is especially aimed toward those wanting to apply these techniques to their research problems.

Our aim is to give a practical and theoretical account of how to solve a large variety of differential equations, comprising ordinary differential equations, initial value problems and boundary value problems, differential algebraic equations, partial differential equations and delay differential equations.

Six Sigma has arisen in the last two decades as a breakthrough Quality Management Methodology. The aim of this book is to show how R can be used as the software tool in the development of Six Sigma projects. The book includes a gentle introduction to Six Sigma and a variety of examples showing how to use R within real situations.