See help family for other allowable link functions for each family.
Three subtypes of generalized linear models will be covered here: logistic regression, poisson regression, and survival analysis. Logistic regression is useful when you are predicting a binary outcome from a set of continuous predictor variables.
Logit model, logistic regression, and log-linear model A comparison. - ppt download
It is frequently preferred over discriminant function analysis because of its less restrictive assumptions. Poisson regression is useful when predicting an outcome variable representing counts from a set of continuous predictor variables. Survival analysis also called event history analysis or reliability analysis covers a set of techniques for modeling the time to an event.
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Data may be right censored - the event may not have occured by the end of the study or we may have incomplete information on an observation but know that up to a certain time the event had not occured e. While generalized linear models are typically analyzed using the glm function, survival analyis is typically carried out using functions from the survival package. The survival package can handle one and two sample problems, parametric accelerated failure models, and the Cox proportional hazards model.
Data are bundled into a Surv object via the Surv function prior to further analyses. See Thomas Lumley's R news article on the survival package for more information.
It begins with an extensive discussion of odds and odds ratios as well as concrete illustrations of the basic independence models for contingency tables. Visit Seller's Storefront. Falls das von Ihnen bestellte Buch besonders schwer oder sperrig sein sollte, werden Sie ggf. List this Seller's Books. Payment Methods accepted by seller.
[Logistic regression vs other generalized linear models to estimate prevalence rate ratios].
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Publisher: U. About this title Synopsis: The primary focus here is on log-linear models for contingency tables, but in this second edition, greater emphasis has been placed on logistic regression.
After developing a sound applied and theoretical basis for the models considered,the book presents detailed discussions of the use of graphical models and of models selection procedures. It then explores models with quantitative factors and generalized linear models,after which the fundamental results are reexamined using powerful matrix methods.
Lesson 10: Log-Linear Models
Finally, the book gives an extensive treatment of Bayesian procedures for analyzing logistic regression and other regression models for binomial data. Bayesian methods are simple and,unlike "About this title" may belong to another edition of this title. More Information.