Missing Value Imputation

Novice and advanced R users from all professional groups.

• CHF 600.- für Angehörige der UZH/ETH und assoziierter Institute
• CHF 800.- für Alumni der UZH/ETH, Angehörige anderer Universtitäten, Forschungseinrichtungen und Ämter des Bundes oder der Kantone, non-profit Organisationen
• CHF 1200.- für Firmen

Nearly all data analytic procedures in R are designed for complete data and fail if the data contain NA's. Most procedures simply ignore any incomplete rows in the data, or use ad-hoc procedures like replacing NA with the "best value". However, such procedures for fixing NA's may introduce serious biases in the ensuing statistical analysis.

Multiple imputation is a principled solution for this problem. The aim of this course to enhance participants’ knowledge in imputation methodology using R. The course will explain the principles of missing data theory, outline a step-by-step approach toward creating high quality imputations, and provide guidelines on how the results can be reported.

The course will be based on the popular R package MICE. Familiarity is required to basic statistical concepts and techniques (such as regression) and the concept of statistical inference. This course will emphasize computational techniques, but no prior programming experience with R is needed.

For all Zurich R Courses participants should bring their own laptops to the course and will be informed by email in advance which packages they need to install.

Februar 23-24, 2017