Bi7921 Advanced statistical methods for analysis of biological data

Faculty of Science
Spring 2017
Extent and Intensity
0/2/0. 2 credit(s) (plus 2 credits for an exam). Type of Completion: zk (examination).
Teacher(s)
prof. Mgr. Stanislav Pekár, Ph.D. (lecturer)
Ing. Marek Brabec, PhD. (lecturer), prof. Mgr. Stanislav Pekár, Ph.D. (deputy)
Mgr. Petr Šmarda, Ph.D. (lecturer)
Guaranteed by
prof. Mgr. Stanislav Pekár, Ph.D.
Department of Botany and Zoology - Biology Section - Faculty of Science
Contact Person: prof. Mgr. Stanislav Pekár, Ph.D.
Supplier department: Department of Botany and Zoology - Biology Section - Faculty of Science
Prerequisites
Bi5040 Biostatistics - basic course && Bi8190 Visualization of biolog. data && Bi7920 Data analysis
Basic biostatistic methods, General Linear Model, Generalised Linear Models.
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
The capacity limit for the course is 20 student(s).
Current registration and enrolment status: enrolled: 0/20, only registered: 0/20, only registered with preference (fields directly associated with the programme): 0/20
fields of study / plans the course is directly associated with
there are 8 fields of study the course is directly associated with, display
Course objectives
Aim of this lecture is to show how to analyse data with correlated structure by means of selected advanced methods, univariate or multivariate. Specifically data with temporal, spatial or phzlogeentic dependence (LME, GLS, GEE), as well as models with nonlinear predictor(NLS, GNLS, NLME, GAM).
Learning outcomes
Student will know: - how to prepare an exparimental design - use advanced statistical methods
Syllabus
  • 1) Correlated data. 2) Experimental design: Nested, Split-plot. Pseudoreplication. 3)Random effects. 4) Models with temporal dependence. 5) Models with spatial dependence. 6) Models with phylogenetic dependence. 7) Mixed-effect models like GLS, LME and GEE. 8) Models with non-linear predictor (NLS, GNLS). 9) Models with random effects and non-linar predictor (NLME). 10) Additive models (GAM).
Literature
  • Pekár S. & Brabec M. 2012. Moderní analýza biologických dat. 2. Lineární modely s korelacemi v prostředí R. Masaryk University Press, Brno.
  • Pinheiro J.C. & Bates D.M. 2000. Mixed-Effects Models in S and S-PLUS. New York: Springer Verlag.
Teaching methods
Beside theory, lectures are based on conctere data and ther analysis.
Assessment methods
Continuous examination by means of homework. Final exam is oral.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: in blocks.
The course is also listed under the following terms Spring 2011 - only for the accreditation, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013, Spring 2014, Spring 2015, Spring 2019, Spring 2021.
  • Enrolment Statistics (Spring 2017, recent)
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