{"product_id":"extending-the-linear-model-with-r-9781498720960","title":"Extending the Linear Model with R","description":"\u003cp\u003e • Author(s): Julian J. Faraway\u003cbr\u003e • Publisher: Taylor \u0026amp; Francis Inc\u003cbr\u003e • Publisher Imprint: Taylor \u0026amp; Francis Inc\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cem\u003eStart Analyzing a Wide Range of Problems \u003c\/em\u003e\u003c\/p\u003e\u003cp\u003eSince the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. \u003cstrong\u003eExtending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition\u003c\/strong\u003e takes advantage of the greater functionality now available in R and substantially revises and adds several topics.\u003c\/p\u003e\u003cp\u003e\u003cem\u003eNew to the Second Edition\u003c\/em\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eExpanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models \u003c\/li\u003e \u003cli\u003eNew sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models (GLMs) \u003c\/li\u003e \u003cli\u003eRevised 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\u003c\/li\u003e \u003cli\u003eNew chapter on the Bayesian analysis of mixed effect models that illustrates the use of STAN and presents the approximation method of INLA \u003c\/li\u003e \u003cli\u003eRevised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available\u003c\/li\u003e \u003cli\u003eUpdated coverage of splines and confidence bands in the chapter on nonparametric regression\u003c\/li\u003e \u003cli\u003eNew material on random forests for regression and classification \u003c\/li\u003e \u003cli\u003eRevamped R code throughout, particularly the many plots using the ggplot2 package\u003c\/li\u003e \u003cli\u003eRevised and expanded exercises with solutions now included\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003e\u003cem\u003eDemonstrates the Interplay of Theory and Practice\u003c\/em\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Taylor \u0026 Francis Inc","offers":[{"title":"Hardcover","offer_id":45608330035351,"sku":"9781498720960","price":8127.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9781498720960.webp?v=1769294218","url":"https:\/\/atlanticbooks.com\/products\/extending-the-linear-model-with-r-9781498720960","provider":"Atlantic Books","version":"1.0","type":"link"}