Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects, Hodges James S.

Автор: Wood, Simon
Название: Generalized Additive Models: An Introduction with R
ISBN: 1584884746 ISBN-13(EAN): 9781584884743
Издательство: Taylor&Francis
Рейтинг:
Цена: 71450.00 T
Наличие на складе: Нет в наличии.
Описание: An Introduction to Generalized Additive Models with R provides readers with a thorough understanding of the theory and practical applications of GAMs to enable informed use of these very flexible tools and other advanced related models. The author's approach is based on a framework of penalized regression splines, and he provides a gentle introduction through motivating chapters on linear and generalized linear models. The author uses the freely available R software throughout to explain the underlying theory and illustrate the practicalities of linear, generalized linear, and generalized additive models. The text is accompanied by a supporting Web site that contains R code and the datasets used in the book.

Автор: Darlington Richard B., Hayes Andrew F.
Название: Regression Analysis and Linear Models: Concepts, Applications, and Implementation
ISBN: 1462521134 ISBN-13(EAN): 9781462521135
Издательство: Taylor&Francis
Рейтинг:
Цена: 83690.00 T
Наличие на складе: Невозможна поставка.
Описание: Ephasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book`s examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable`s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.