Buch, Englisch, 236 Seiten, Format (B × H): 164 mm x 252 mm, Gewicht: 552 g
Buch, Englisch, 236 Seiten, Format (B × H): 164 mm x 252 mm, Gewicht: 552 g
Reihe: Chapman & Hall/CRC The R Series
ISBN: 978-1-4398-3164-9
Verlag: Taylor & Francis Inc
With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times.
Features
Introduces parametric proportional hazards models with baseline distributions like the Weibull, Gompertz, Lognormal, and Piecewise constant hazard distributions, in addition to traditional Cox regression
Presents mathematical details as well as technical material in an appendix
Includes real examples with applications in demography, econometrics, and epidemiology
Provides a dedicated R package, eha, containing special treatments, including making cuts in the Lexis diagram, creating communal covariates, and creating period statistics
A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.
Zielgruppe
Researchers and graduate students in demography, sociology, economics, statistics, and epidemiology.
Autoren/Hrsg.
Fachgebiete
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung
- Sozialwissenschaften Psychologie Psychologie / Allgemeines & Theorie Psychologische Forschungsmethoden
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
Weitere Infos & Material
Event History and Survival Data. Single Sample Data. Cox Regression. Poisson Regression. More on Cox Regression. Parametric Models. Multivariate Survival Models. Competing Risks Models. Statistical Concepts. Survival Distributions. A Brief Introduction to R. Survival Packages in R.
