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Introduction to Frequentist and Bayesian mixed (Hierarchical) models (IFBM01)
8th October 2018 - 12th October 2018£275.00 - £500
This course will cover introductory mixed or hierarchical modelling (fixed and random effects models) for real-world data sets from both a Frequentist and Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in mixed modelling. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using both the standard lme4 mixed effects models library in R, together with the Bayesian modelling framework via rstanarm. The course covers the full gamut from simple regression models through to full generalised multivariate mixed structures. The relevant advantages and disadvantages of both the Frequentist and Bayesian approaches will be presented.. Participants are encouraged to bring their own data sets for discussion with the course tutors.
Research postgraduates, practicing academics and professionals in government and industry.
Venue – PS statistics head office, 53 Morrison Street, Glasgow, G5 8LB – Google map
Availability – 30 places
Duration – 5 days
Contact hours – Approx. 28 hours
ECT’s – Equal to 3 ECT’s
Language – English
We offer COURSE ONLY and ACCOMMODATION PACKAGES;
• COURSE ONLY – Includes lunch and refreshments.
• ACCOMMODATION PACKAGE (to be purchased in addition to the course only option) – Includes breakfast, lunch, welcome dinner Monday evening, farewell dinner Fridayevening, refreshments and accommodation. Self-catering facilities are available in the accommodation. Accommodation is approximately a 6-minute walk from the PR statistics head office. Accommodation is multiple occupancy (max 3-4 people) single sex en-suite rooms. Arrival Sunday 23rd September (after 5pm) and departure Friday 28th September (accommodation must be vacated by 9am). An additional nights accommodation can be purchased, departure 9am Saturday morning email for details.
Other payment options are available please email firstname.lastname@example.org
Cancellation policy: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com Failure to attend will result in the full cost of the course being charged. In the unfortunate event that PS statistics must cancel this course due to unforeseen circumstances a full refund for the course will be credited. However PS statistics cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.
There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be guided practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of regression methods and preferably generalised linear models such as logistic regression.
Assumed computer background
Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links.
It is essential that you come with all necessary software and packages already installed. You will be sent a complete list of instructions prior to the course as internet access may not always be available.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Meet at 43 Cook Street, Glasgow G5 8JN at approx. 17:00 onwards
Monday 8th – Classes from 09:30 to 17:30
Class 1: Introduction; some example datasets; overview of course
Class 2: Revision: probability distributions and likelihood
Class 3: Maximum likelihood and bootstrap uncertainties
Practical: revision on using R to load data, create plots and fit statistical models.
Tuesday 9th – Classes from 09:30 to 17:30
Intro to mixed models
Class 1: Linear and generalised linear models (GLMs)
Class 2: Simple mixed regression models
Class 3: Generalised Linear Mixed Models (GLMMs)
Practical: introduction to lme4
Wednesday 10th – Classes from 09:00 to 17:00
Bayesian hierarchical models
Class 1: Introduction to Bayesian inference
Class 2: Bayesian computation and Markov chain Monte Carlo
Class 3: Bayesian Hierarchical Models (BHMs)
Practical: Introduction to rstanarm
Thursday 11th – Classes from 09:00 to 17:00
Extending mixed models
Class 1: Multivariate and multi-layer hierarchical models
Class 2: Shrinkage and variable selection
Class 3: Non-Linear mixed models
Friday 12th – Classes from 09:30 to 16:00
Advanced topics and bring your own data
Class 1: Extending Bayesian models
Class 2: Using stan (instead of rstanarm) for richer inference
Practical: analyse and get help with your data.