[R-sig-ME] Generalised Linear (MIXED) (GLMM), Nonlinear (NLGLM) And General Additive Models (MIXED) (GAMM) (GNAM01)

[Oliver Hooker]

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 from the 9th - 13th
September 2019 in Glasgow City Centre

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.

Email oliverhookerpsstatistics.com

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1. June 10th – 14th 2019
STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04)
Glasgow, Scotland, Dr. Andrew Parnell, Dr. Andrew Jackson
www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm04/

2. June 10th – 14th 2019
INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB06)
Glasgow, Scotland, Dr. Martin Jones
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
MICROBIOME DATA ANALYSIS USING QIIME2 (MBQM01)
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
BIOACOUSTICS FOR ECOLOGISTS: HARDWARE, SURVEY DESIGN AND DATA ANALYSIS
(BIAC01)
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
INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING USING R (IBHM03)
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
INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISPE01)
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
Glasgow, Scotland, Dr. Mark Andrews
INTRODUCTION TO BAYESIAN DATA ANALYSIS FOR SOCIAL AND BEHAVIOURAL SCIENCES
USING R AND STAN (BDRS02)
https://www.psstatistics.com/course/introduction-to-bayesian-data-analysis-for-social-and-behavioural-sciences-using-r-and-stan-bdrs02/

19. November 4th – 8th 2019
BEHAVIOURAL DATA ANALYSIS USING MAXIMUM LIKELIHOOD (BDML02)
Glasgow, Scotland, Dr Will Hoppitt
https://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-likelihood-bdml02/

20. November 11th – 15th 2019
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME05)
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
INTRODUCTION TO STRUCTURED POPULATION MODELS AND DEMOGRAPHIC DISTRIBUTION
MODELS (IIPM01)
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
ECOLOGICAL NICHE MODELLING USING R (ENMR04)
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/

-- 
Oliver Hooker PhD.
PR statistics

2019 publications;

A way forward with eco evo devo: an extended theory of resource
polymorphism with postglacial fishes as model systems. Biological Reviews
(2019).

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