|
|
Product Description
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.
Customers Who Bought This Item Also Bought
- Linear Models with R (Chapman & Hall/CRC Texts in Statistical Science)
- Forecasting: principles and practice
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
- A Modern Approach to Regression with R (Springer Texts in Statistics)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- ggplot2: Elegant Graphics for Data Analysis (Use R!)
- Generalized Linear Models (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
- An Introduction to Generalized Linear Models (Chapman & Hall/CRC Texts in Statistical Science)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
- Statistical Inference
*If this is not the "Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Mo" product you were looking for, you can check the other results by clicking this link
.
Details were last updated on Oct 22, 2024 12:53 +08.
