Course: Advanced regression methods

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Course title Advanced regression methods
Course code KBE/051
Organizational form of instruction Lecture + Lesson
Level of course Doctoral
Year of study not specified
Frequency of the course In academic years starting with an odd year (e.g. 2017/2018), in the winter semester.
Semester -
Number of ECTS credits 5
Language of instruction Czech
Status of course Compulsory-optional
Form of instruction Face-to-face
Work placements This is not an internship
Recommended optional programme components None
Lecturer(s)
  • Šmilauer Petr, doc. RNDr. Ph.D.
Course content
Content of lectures: LM, GLM and GAM, linear mixed-effect models with nested and crossed random effects, GLMM, GAMM, survival analysis with random effects, zero-inflated and zero-truncated models, regression trees and random forests, analyzing phylogenetical data, point pattern analysis, model selection and model averaging, bootstrap and jacknife methods. Content of practicals: Practicals complement the lecture, they are not temporally separated, but rather interleaved with it

Learning activities and teaching methods
Monologic (reading, lecture, briefing), E-learning
  • Preparation for credit - 28 hours per semester
  • Class attendance - 42 hours per semester
  • Preparation for classes - 28 hours per semester
  • Preparation for exam - 28 hours per semester
Learning outcomes
Students will learn to use the advanced methods of modelling the data from the field of natural sciences, with an accent on combining fixed and random effects in the models and properly choosing model complexity. An important component of this course are the individually solved homeworks, scored by the lecturer.
Students will be able to choose correctly - for datasets produced in the field of natural sciences - the type of regression models: coding of explanatory variables, choice of fixed and random effects, scale transformation for explanatory and response variables, testing significances of individual effects, methods of selection of explanatory variables. They will be also able to effectively apply advanced types of tree models (e.g. boosted regression trees, regression forests). They will manage the use of methods modelling phylogenetic correlations among taxa in the field of comparative ecology and to correctly use the non-parametric generalized additive models (GAM and GAMM).
Prerequisites
The students should already know not only the basic types of linear models (linear regression and ANOVA], but also the more advanced generalized linear models (GLM) and the application of these models within the R software package. The ability of work with the R software is verified at the start of this course with a simple written test.
KBE/050
----- or -----
KBE/785E

Assessment methods and criteria
Written examination, Interim evaluation

Students should study the materials provided by lecturer before the corresponding topic is discussed in the lecture-practicals. Students are provided with homeworks (5-7 times) during the term, and their solutions are scored by the lecturer.
Recommended literature
  • A. Zuur et al. (2009): Mixed effect models and extensions in ecology with R. Springer.
  • K.P. Burnham & D.R. Anderson (1998): Model selection and multimodel inference. 2nd edition, Springer.


Study plans that include the course
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