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Lecturer(s)
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Course content
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1-2. Introduction to a syntax, libraries and packages of linear models in the R program. 3-4. Formulation of the regression models in the R program. 5-6. Options of the generalized linear models in R. 7-8. Advanced application of the generalized linear models. 9-10. Introduction to the model validation diagnostic. 11-12. Possibilities of using more comlex and flexible statistical methods. 13-14. Autocorrelation problems in model construction.
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Learning activities and teaching methods
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Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Demonstration
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Learning outcomes
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Goal of the subject will be to extend the knowledge gained from previous courses about statistis in R. The main topic of the courses will be to teach students how to construct linear, general linear and generalized linear models. I will try to explain advantages and limitations of the generalized linear models, lessons will focus on the flexibility in the specification of sytematic and random component of the model. Common mistakes linked with the construction and application of the generalized linear models will be explained. Students will also learn more complex statistical methods which can be used in the R program (generalized additive models, multivariate analyses, mixed and marginal models). Practical exercises will be used to practise the correct data analysis process. Residual diagnostic methods, autocorrelation analysis, model and factor selection methods will be practiced.
- orientation and knowledge of syntax in R program - ability to use statistical analysis in R program - using of modern statistical method in R program - ability to individual study of new satistical methods
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Prerequisites
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- knowledge of basic statistical methods - ability to work with tables in program Microsoft Excel - awareness of general statistical principes - willingness to study new programming language
EKO/RSTA1
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Assessment methods and criteria
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Written exam, Seminar Work
- active participation on the seminars - work out individual assignment
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Recommended literature
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Crawley M.J., Hoboken N.J. (2007). The R book. Wiley..
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Drozd P. (2007). Cvičení z biostatistiky. Základy práce ze softwarem R. Universitas Ostraviensis. ISBN 978-80-7368-433-4.
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Faraway J.J. (2004). Linear Models with R. Chapman & Hall, New York..
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Faraway J.J. (2005). Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (Texts in Statistical Science). Chapman & Hall, New York..
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Pekár S., Brabec M. (2009). Moderní analýza biologických dat. Scientia, Praha. ISBN 978-80-86960-44-9.
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Wood S. (2006). Generalized Additive Models: An Introduction with R (Texts in Statistical Scence). Chapman & Hall, New York..
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