Statistical modeling / R

R is a freely available software environment for statistical computing and graphics. This course encourages the use of R for extensive exploratory data analysis and the use of advanced statistical modeling tools for data analysis.

Who should attend?

Data analysists with experience in statistics will benefit from this course. The course is not intended as a first course in statistics or in R.

How you will benefit?

You will learn to use R for advanced statistical analysis and graphics. All practical exercises with solutions are included with the course material and can be followed after the course.

Language

Die Kurssprache und die Kursunterlagen sind Englisch; Diskussionen und Hilfestellungen sind in Deutsch, Englisch und Spanisch möglich.

Dieser 3-tägige Kurs ist Teil einer Reihe zu Statistical modeling / R.

Trainer und Dozenten

Dr. rer. nat. Pablo E. Verde is working in the range of statistical modelling in medical and clinical research, currently in synthesis of evidence (meta-analyses). He chairs the Biometrics Task Group in the Coordination Centre for Clinical Studies at Heinrich-Heine-Universität Düsseldorf and conducts research at the university's Institute for Medical Sociology. He has more than 20 years of international experience in statistical consultancy, research and teaching in the domains of medical science, agriculture, health research and risk analysis/financial econometrics.

Pablo is an expert on statistical software R and WinBUGS for MCMC calculations. Since 1998, he is an active member of the R community, being in charge of translation of R into spanish language.

Since 1990, he is teaching the application of S and R, at first for the financial sector and since 2000 in the academic realm.
Pablo is Visiting Lecturer of the Department of Statistics at Stanford University and a Stanford Community Member since 2007. In 2000, Pablo became a member of the Royal Statistical Society.

Voraussetzungen

You should be familiar with basic statistical concepts up to regression and preferably you have some experience with R.

Inhalt

  • Introductory concepts of R, data structures, objects, classes, and functions
  • Linear regression models: model building, variables selection, model checking, resistant regression models
  • Non-linear regression models
  • Modeling binomial data: Logistic regression and its extensions
  • Modeling count data: log-linear models for contingency tables and multinomial modeling
  • Introduction to survival regression with R
  • Dynamic linear models and time series
  • Machine learning regression tools: regression trees, generalized additive models (GAM) and random forest
  • Introduction to multi-level and hierarchical models with R: linear mixed models and generalized mixed models