Day 14: random effects, choosing models

5 / 29 / 2015

Reading:

• random versus fixed effect in neuroimaging literature ftp://193.62.66.20/spm/data/face_rfx/RFXabstract.pdf http://www.fil.ion.ucl.ac.uk/spm/doc/books/hbf2/pdfs/Ch12.pdf
• a simple tutorial on cross validation http://www.autonlab.org/tutorials/overfit.html

Day:

• Recall t and F tests: what are they doing exactly. Where does my variability come from?

  • example within run (from previous course)
  • example random effect (between subjects).

• Wait a second: What is a model exactly ?
• I am choosing a very wrong model: Consequences on the results of t/F tests.

• How do I know my model is - is not very wrong? the good, the bad, the ugly (reverse order)

  • the ugly: p-hacking. Let’s try it.
  • the bad: use R2.
  • the good: model validation

• Model validation: Principle. Example of “random effect” model testing the effect of “grumpiness”.