Advancing in R (ADVR06) http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr06/
This course will take place at FAFU university, No.15, Shangxiadian Road, Fuzhou, Fujian from 24th – 28th April 2017 Google map – https://www.google.co.uk/maps/place/%E7%A6%8F%E5%BB%BA%E5%86%9C%E6%9E%97%E5% A4%A7%E5%AD%A6/@26.084946,119.2375123,17z/data=!3m1!4b1!4m5!3m4! 1s0x3440509bcfcee35f:0xf9c63d19b7962d4!8m2!3d26.084946!4d119.239701 The course is aimed at biologists with a basic to moderate knowledge in R. The course content is designed to bridge the gap between basic R coding and more advanced statistical modelling. An introduction to working with real- life data: data ‘wrangling’, visualisation, graphing and analysis, including model selection and simplification, mixed effects models, generalised linear models and non-linear models. This five day course will consist of series of modules, each lasting roughly half a day and comprises of a mixture of lectures and practicals designed to either build required skills for future modules or to perform a family of analyses that is frequently encountered in the biological literature. THIS COURSE WILL BE TAUGHT IN ENGLISH Course Programme: Sunday 23rd Meet at Fuzhou airport or Fuzhou train station Monday 24th Module 1: Data manipulation using {tidyr} and {dplyr} Module 2: Data visualization using {ggplot2} Tuesday 25th Module 3: Categorical predictors and the general linear model Module 4: ANCOVA & selecting among non-nested models Wednesday 26th Module 5: Generalized linear models (Poisson and logistic regression) Module 6: Making predictions using model coefficients ({broom} and {visreg}) Thursday 27th Module 7: Introduction to mixed effects models: random intercepts Module 8: Random slope mixed effects models Friday 28th Module 9: Polynomial & mechanistic nonlinear models Module 10: Combining advanced methods (nonlinear mixed models, GLMMs) The course will consist of a mixture of lectures and hands-on practical’s. Data sets for computer practical’s will be provided by the instructors, but participants are welcome to bring their own data. Assumed quantitative knowledge A basic understanding of statistical concepts, including statistical significance and hypothesis testing Assumed computer background Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots. Relative newcomers to programming in R will be provided (by the instructors) with some introductory exercises to complete prior to the course. This will introduce some of the core features of R and RStudio before the course starts. Packages We offer two packages • COURSE ONLY – Includes lunch and refreshments. • ALL INCLUSIVE – Includes breakfast, lunch, dinner, refreshments and accommodation. Accommodation is either single or twin single sex en-suite rooms. Arrival Sunday 23rd April and departure Friday 28th April PM. If you have any questions please email oliverhoo...@prstatistics.com Our other courses 1. ADVANCING IN STATISTICAL MODELLING USING R (December 2016, April 2017, December 2017 http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr05/ http://www.prstatistics.com/course/advancing-statistical-modelling-using-r- advr06/ 2. SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (November 2016, July 2017) http://www.prstatistics.com/course/spatial-analysis-ecological-data-using-r- spae04/ 3. STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR USING R (February 2017) http://www.prstatistics.com/course/stable-isotope-mixing-models-using-r- simm03/ 4. GENETIC DATA ANALYSIS USING R (TBC) 5. BIOINFORMATICS FOR GENETICISTS AND BIOLOGISTS (July 2017) http://www.prstatistics.com/course/bioinformatics-for-geneticists-and- biologists-bigb02/ 6. APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (November 2017) 7. INTRODUCTION TO STATISTICS AND R FOR BIOLOGISTS (April 2017) http://www.prstatistics.com/course/introduction-to-statistics-and-r-for- biologists-irfb02/ 8. INTRODUCTION TO PYTHON FOR BIOLOGISTS (TBC) 9. TIME SERIES MODELS FOR ECOLOGISTS AND CLIMATOLOGISTS (TBC) 10. ADVANCES IN MULTIVAIRAITE ANALYSIS OF SPATIAL ECOLOGICAL DATA (April 2017) 11. ADVANCES IN DNA TAXONOMY (TBC) 12. INTRODUCTION TO BIOINFORMATICS USING LINUX (TBC) 13. INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical- modelling-using-r-ibhm02/ 14. LANDSCAPE (POPULATION) GENETIC DATA ANALYSIS USING R (TBC) 15. PHYLOGENETIC DATA ANALYSIS USING R (TBC) 16. MODEL BASED MULTIVARIATE ANALYSIS OF ECOLOGICAL DATA USING R (January 2017) http://www.prstatistics.com/course/model-base-multivariate-analysis-of- abundance-data-using-r-mbmv01/ 17. ADVANCED PYTHON FOR BIOLOGISTS (February 2017) http://www.prstatistics.com/course/advanced-python-biologists-apyb01/ 18. NETWORK ANAYLSIS FOR ECOLOGISTS USING R (March) http://www.prstatistics.com/course/network-analysis-ecologists-ntwa01/ 19. GEOMETRIC MORPHOMETRICS USING R (June) http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/ 20. INTRODUCTION TO METHODS FOR REMOTE SENSING (July 2017) 21. ECOLOGICAL NICHE MODELLING (TBC) 22. ANIMAL MOVEMENT ECOLOGY (TBC) Oliver Hooker PhD. www.prstatistics.com www.prstatistics.com/organiser/oliver-hooker/Oliver Hooker PR statistics 3/1 128 Brunswick Street Glasgow G1 1TF +44 (0) 7966500340
