R統計回歸模式於環境流行病學之應用
Regression Method in Environmental Epidemiology Using R Language
| 節 | 週三 |
|---|---|
5 13:20–14:10 | R統計回歸模式於環境流行病學之應用 YS105（陽明） 3 節連堂 |
6 14:20–15:10 | |
7 15:30–16:20 |
* 根據陽明交大上課時間表所列
For each session, a 2-hour lecture will be delivered followed by a 1-hour hands-on analysis using R statistical program. Students will be able to implement their own codes for statistical analysis taught in the class.
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1. Use R program for statistical analysis and apply frequently used statistical models on their own research, 2. Learn how to model continuous exposures and outcomes in an advanced fashion (e.g. splines, polynomial regression), 3. Handle with the correlated data in the setting of environmental epidemiology, 4. Deal with data when the underlying statistical assumptions do no hold, 5. Apply state-of-art models in epidemiological studies
60% Final project (a real-world dataset will be distributed for analysis) 20% Two homework assignments (10% for each) 20% Class Participation
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| 週次 | 主題 |
|---|---|
| 第 1 週 | University Holiday 2022/02/16 |
| 第 2 週 | Introduction to R Environment I (Intro to Final Project) 2022/02/23 |
| 第 3 週 | Introduction to R Environment II 2022/03/02 |
| 第 4 週 | 1st Progress Report 2022/03/09 |
| 第 5 週 | Statistical Packages in R 2022/03/16 |
| 第 6 週 | Generalized Linear Model 2022/03/23 |
| 第 7 週 | Technical Discussion I 2022/03/30 |
| 第 8 週 | National Holiday (HW1 due) 2022/04/06 |
| 第 9 週 | Concentration-response Relationship 2022/04/13 |
| 第 10 週 | Modeling Relationship (Linear and Categorical factors) 2022/04/20 |
| 第 11 週 | Modeling Relationship (Splines) 2022/04/27 |
| 第 12 週 | Technical Discussion II 2022/05/04 |
| 第 13 週 | T.B.A. (HW2 due) 2022/05/11 |
| 第 14 週 | 2nd Progress Report 2022/05/18 |
| 第 15 週 | T.B.A. 2022/05/25 |
| 第 16 週 | Presentation of Final Project 2022/06/01 |
| 第 17 週 | flexible topic 2022/06/08 |
| 第 18 週 | flexible topic 2022/06/15 |
Course Text Books: Peter Dalgaard. Introductory Statistics with R (Paperback) 1st Edition. Springer-Verlag New York, Inc. ISBN 0-387-95475-9 (download link) P. M. McCullagh and John A. Nelder. Generalized Linear Models. 2nd Edition. Chapman and Hall, London. ISBN 978-0412317606. (download link) T. J. Hastie and R. J. Tibshirani. Generalize Additive Models. Chapman and Hall, London. ISBN: 0-412-34390-8 David Rupert, M. P. Wand, and R. J. Carroll. Semiparametric Regression. Cambridge University Press. ISBN 978-0521785167 W. N. Venables and B. D. Ripley. 2002 Modern Applied Statistics with S. 4th Edition. Springer. ISBN 0-387-95457-0 Other Useful Reference: An Introduction to R. Online manual at R website at http://cran.r-project.org/manuals.html Andreas Krause, Lelvin Olson. 2005. The Basics of S-PLUS. 4th Edition. Springer-Berlag, New York. ISBN 0-387-26109-5 Jose Pinheiro and Douglas Bate. 2000. Mixed-effects models in S and S-PLUS. Springer-Verlag, Berlin. ISBN 0-387-98957-9
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