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Faraway, Julian James 作者

CRC Pr I Llc

译者

2005.12 出版日期

300 页数

785.00元价格

HRD

丛书系列

9781584884248 图书编码

Extending the Linear Model With R 在线电子书图书标签: R Statistics 统计 Textbook Stat R语言数据处理教材

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Extending the Linear Model With R 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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7.6 Faraway, Julian J (2006), Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC.

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7.6 Faraway, Julian J (2006), Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Chapman & Hall/CRC.

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STAT 425

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Extending the Linear Model With R 在线电子书著者简介

Julian J. Faraway is a professor of statistics in the Department of Mathematical Sciences at the University of Bath. His research focuses on the analysis of functional and shape data with particular application to the modeling of human motion. He earned a PhD in statistics from the University of California, Berkeley.

Extending the Linear Model With R 在线电子书图书目录

Extending the Linear Model With R 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Extending the Linear Model With R 在线电子书图书描述

Start Analyzing a Wide Range of Problems

Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics.

New to the Second Edition

Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models

New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs)

Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods

New chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA

Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available

Updated coverage of splines and confidence bands in the chapter on nonparametric regression

New material on random forests for regression and classification

Revamped R code throughout, particularly the many plots using the ggplot2 package

Revised and expanded exercises with solutions now included

Demonstrates the Interplay of Theory and Practice

This textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: GLMs, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.

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