Generalised Linear (MIXED) (GLMM), Nonlinear (NLGLM) And General Additive Models (MIXED) (GAMM) (GNAM01)
https://www.psstatistics.com/course/generalised-linear-glm-nonlinear-nlglm-and-general-additive-models-gam-gnam01/
This course will be delivered by Dr. Mark Andrews form the 9th - 13th September 2019 in Glasgow City Centre.
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Course Overview:
This course provides a general introduction to nonlinear regression analysis, covering major topics including, but not limited to, general and generalized linear models, generalized additive models, spline and radial basis function regression, and Gaussian process regression. We approach the general topic of nonlinear regression by showing how the powerful and flexible statistical modelling framework of general and generalized linear models, and their multilevel counterparts, can be extended to handle nonlinear relationships between predictor and outcome variables. We begin by providing a comprehensive practical and theoretical overview of regression, including multilevel regression, using general and generalized linear models. Here, we pay particular attention to the many variants of general and generalized linear models, and how these provide a very widely applicable set of tools for statistical modeling. After this introduction, we then proceed to cover practically and conceptually simple extensions to the general and generalized linear models framework using parametric nonlinear models and
polynomial regression. We will then cover more powerful and flexible extensions of this modeling. framework by way of the general concept of basis functions. We’ll begin our coverage of basis function regression with the major topic of spline regression, and then proceed to cover radial basis functions and the multilayer perceptron, both of which are types of artificial neural networks. We then move on to the major topic of generalized additive models (GAMs) and generalized additive mixed models (GAMMs), which can be viewed as the generalization of all the basis function regression topics, but cover a wider range of topic including nonlinear spatial and temporal models and interaction models. Finally, we will cover the powerful Bayesian nonlinear regression method of Gaussian process regression.
Monday 9th – Classes from 09:30 to 17:30
Module 1: General and generalized linear models, including multilevel models. In order to provide a solid foundation for the remainder of the course, we begin by providing a comprehensive practical and theoretical overview of the principles of general and generalized linear models, also covering their multilevel (or hierarchical) counterparts. General and generalized linear models provide a powerful set of tools for statistical modeling., which are extremely widely used and widely applicable. Their underlying theoretical principles are quite simple and elegant, and once understood, it becomes clear how these models can be extended in many different ways to handle different statistical modeling. situations.
For this module, we will use the very commonly used R tools such as lm, glm, lme4::lmer, lme4::glmer. In addition, we will also use the R based brms package, which uses the Stan probabilistic programming language. This package allows us to perform all the same analyses that are provided by lm, glm, lmer, glmer, etc., using an almost identical syntax, but also us to perform a much wider range of general and generalized linear model analyses.
Tuesday 10th – Classes from 09:30 to 17:30
Having established a solid regression modeling. foundation, on the second day we may cover a range of nonlinear modeling. extensions to the general and generalized linear modeling. framework.
Module 2: Polynomial regression. Polynomial regression is both a conceptually and practically simple extension of linear modeling. It be easily accomplished using the poly function along with tools like lm, glmer, lme4::lmer, lme4::glmer. Here, we will use cover piecewise linear and polynomial regression, using R packages such as segmented.
Module 3: Parametric nonlinear regression. In some cases of nonlinear regression, a bespoke parametric function for the relationship between the predictors and outcome variable is used. These are often obtained from scientific knowledge of the problem at hand. In R, we can use the package nls to perform parametric nonlinear regression.
Module 4: Spline regression: Nonlinear regression using splines is a powerful and flexible non-parametric or semi-parametric nonlinear regression method. It is also an example of a basis function regression method. Here, we will cover spline regression using the splines::bs and splines::ns functions that can be used with lm, glm, lme4::lmer, lme4::glmer, brms, etc.
Module 5: Radial basis functions. Regression using radial basis functions is a set of methods that are closely related to spline regression. They have a long history of usage in machine learning and can also be viewed as a type of artificial neural network model. Here, we will explore radial basis function models using the Stan programming language, which will allow us to build powerful and flexible versions of the radial basis functions.
Module 6: Multilayer perceptron. Closely related to radial basis functions are multilayer perceptrons. These and their variants and extensions are major building block of deep learning (machine learning) methods. We will explore multilayer perceptron in Stan, but we will also use the powerful Keras library.
Wednesday 11th – Classes from 09:30 to 17:30
Module 7: Generalized additive models. We now turn to the major module of generalized additive models (GAMs). GAMs generalize many of concepts and module covered so far and represent a powerful and flexible framework for nonlinear modeling. In R, the mgcv package provides a extensive set of tools for working with GAMs. Here, we will provide an in-depth coverage of mgcv including choosing smooth terms, controlling overfitting and complexity,
prediction, model evaluation, and so on.
Module 9: Generalized additive mixed models. GAMs can also be used in linear mixed effects models where they are known as generalized additive mixed mmodels (GAMMs). GAMMs can also be used with the mgcv package.
Thursday 12th – Classes from 09:30 to 17:30
Module 10: Interaction nonlinear regression: A powerful feature of GAMs and GAMMs is the ability to model nonlinear interactions, whether between two continuous variables, or between one continuous and one categorical variable. Amongst other things, interactions between continuous variables allow us to do spatial and spatio-temporal modeling. Interactions between categorical and continuous variables allow us to model how nonlinear relationships between a predictor and outcome change as a function of the value of different categorical variables.
Module 11: Nonlinear regression for time-series and forecasting. One major application of nonlinear regression is for modeling. time-series and forecasting. Here, we will explore the prophet library for time-series forecasting. This library, available for both Python and R, gives us a GAM-like framework for modeling. time-series and making forecasts.
Friday 13th – Classes from 09:30 to 16:00
Module 12: Gaussian process regression. Our final module deals with a type of Bayesian nonlinear regression known as Gaussian process regression. Gaussian process regression can be viewed as a kind of basis function regression, but with an infinite number of basis functions. In that sense, it generalizes spline, radial basis functions, multilayer perceptron, generalized additive models, and provides means to overcome some practically challenging problems in nonlinear regression such as selecting the number and type of smooth functions. Here, we will explore Gaussian process regression using Stan.
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http://www.prinformatics.com/course/introduction-to-python-for-biologists-ipyb06/
3. June 17th – 21st 2019
ADVANCED PYTHON FOR BIOLOGISTS (APYB03)
Glasgow, Scotland, Dr. Martin Jones
www.prinformatics.com/course/advanced-python-biologists-apyb03/
4. June 24th – 28th 2019
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Glasgow, Scotland, Dr. Yoshiki Vazquez Baeza, Dr. Antonio Gonzalez Pena
https://www.prinformatics.com/course/microbiome-data-analysis-using-qiime2-mbqm01/
5. July 1st – 5th 2019
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Glasgow, Scotland, Dr. Paul Howden-Leach
https://www.prstatistics.com/course/bioacoustics-for-ecologists-hardware-survey-design-and-data-analysis-biac01/
6. July 8th – 12th 2019
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Glasgow, Scotland, Dr. Andrew Parnell
https://www.psstatistics.com/course/introduction-to-bayesian-hierarchical-modelling-using-r-ibhm03/
7. July 15th – 19th 2019
ANALYSING ENVIRONMENTAL ADAPTATION USING LANDSCAPE GENETICS (EDAP01)
Glasgow, Dr. Matt Fitzpatrick
https://www.prstatistics.com/course/analysing-environmental-adaptation-using-landscape-genetics-edap01
8. July 29th – August 2nd 2019
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Glasgow, Scotland, Dr. Jakub Nowosad
https://www.prstatistics.com/course/introduction-to-spatial-analysis-of-ecological-data-using-r-ispe01/
9. September 2nd – 6th 2019
APPLIED METHODS FOR ANALYSING CAPTURE-RECAPTURE (MARK-RECAPTURE) DATA USING SPATIALLY EXPLICIT AND NON-SPATIAL TECHNIQUES (MARK01)
Glasgow, Scotland, Dr. Joanne Potts, Dr. David Borchers
https://www.prstatistics.com/course/applied-methods-for-analysing-capture-recapture-mark-recapture-data-using-spatially-explicit-and-non-spatial-techniques-mark01/
10. September 9th – 13th 2019
GENERALISED LINEAR (MIXED) (GLMM), NONLINEAR (NLGLM) AND GENERAL ADDITIVE MODELS (MIXED) (GAMM) (GNAM01)
Glasgow, Scotland, Dr. Mark Andrews
https://www.psstatistics.com/course/generalised-linear-glm-nonlinear-nlglm-and-general-additive-models-gam-gnam01/
11. September 16th – 20th 2019
R PACKAGE DESIGN AND DEVELOPMENT AND REPRODUCIBLE DATA SCIENCE FOR BIOLOGISTS (RPKG01)
Glasgow, Scotland, Dr. Cory Merow, Dr. Andy Rominger
https://www.prstatistics.com/course/r-package-design-and-development-and-reproducible-data-science-for-biologists-rpkg01/
12. September 16th – 20th 2019
STRUCTURAL EQUATION MODELLING AND PATH ANALYSIS (SMPA01)
Glasgow, Scotland, Dr. Mark Andrews
https://www.psstatistics.com/course/structural-equation-modelling-and-path-analysis-smpa01/
13. September 23rd – 27th 2019
DATA SCIENCE/ANALYTICS USING PYTHON (DSAP01)
Glasgow, Scotland, Dr. Mark Andrews
https://www.psstatistics.com/course/data-science-analytics-using-python-dsap01/
14. September 30th – October 4th 2019
GEOMETRIC MORPHOMETRICS USING R (GMMR02)
Glasgow, Scotland, Prof. Dean Adams, Prof. Michael Collyer, Dr. Antigoni Kaliontzopoulou
http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr02/
15. October 7th – 11th 2019
CONSERVATION PLANNING USING PRIORITIZR : FROM THEORY TO PRACTICE (PRTZ01)
Athens, GREECE, Dr Richard Schuster and Nina Morell
https://www.prstatistics.com/course/conservation-planning-using-prioritizr-from-theory-to-practice-prtz01/
16. October 14th – 18th 2019
INTRODUCTION TO BEHAVIOURAL DATA ANALYSIS USINR R (IBDA01)
Glasgow, Scotland, Dr Will Hoppitt
https://www.psstatistics.com/course/introduction-to-behavioural-data-analysis-using-r-ibda01/
17. October 21st – 25th 2019
MULTIVARIATE ANALYSIS OF ECOLOGICAL COMMUNITIES USING THE VEGAN PACKAGE (VGNR01)
Glasgow, Scotland, Dr. Guillaume Blanchet
www.prstatistics.com/course/multivariate-analysis-of-ecological-communities-in-r-with-the-vegan-package-vgnr01/
18. November 4th – 8th 2019
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Glasgow, Scotland, Dr Will Hoppitt
https://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-likelihood-bdml02/
20. November 11th – 15th 2019
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Glasgow, Scotland, Dr Matt Denwood, Emma Howard
https://www.prstatistics.com/course/applied-bayesian-modelling-for-ecologists-and-epidemiologists-abme05/
21. November 18th – 22nd 2019
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Athens, GREECE, Dr Cory Merow
https://www.prstatistics.com/course/introduction-to-structured-population-models-and-demographic-distribution-models-iipm01/
22. November 25th – 29th 2019
ADVANCED RANGE, NICHE, AND DISTRIBUTION MODELING (ASDM01)
Athens, GREECE, Dr Cory Merow
https://www.prstatistics.com/course/advanced-range-niche-and-distribution-modeling-asdm01/
23. May 11th – 15th 2020
FORMALIZING UNCERTAINTY: FUZZY LOGIC IN SPECIES DISTRIBUTION AND DIVERSITY PATTERNS (FLDM01)
Glasgow, Scotland, Dr. Marcia Barbosa
https://www.prstatistics.com/course/formalizing-uncertainty-fuzzy-logic-in-species-distribution-and-diversity-patterns-fldm01/
24. May 18th – 22nd 2020
STRUCTUAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS (SEMR02)
Glasgow, Scotland, Dr. Jonathan Lefcheck, Dr. Jim (james) Grace
https://www.prstatistics.com/course/structural-equation-modelling-for-ecologists-and-evolutionary-biologists-semr02/
25. October 5th – 9th 2020
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Glasgow, Scotland, Dr. Neftali Sillero
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-enmr04/
26. October 11th – 16th 2020
ADVANCED ECOLOGICAL NICHE MODELLING USING R (ABNMR01)
Glasgow, Scotland, Dr. Neftali Sillero
http://www.prstatistics.com/course/advanced-ecological-niche-modelling-using-r-anmr01/
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