Google Summer of Code 2015 is coming to an end. During this summer, I have learned too many things to list here about statistical modeling, Ruby and software development in general, and I had a lot of fun in the process!
Posts Tagged “mixed_models”
(EDIT: I have also written a more theoretical blog post on the topic.)
The following shows an application of class
LMM from the Ruby gem
mixed_models to SNP data (single-nucleotide polymorphism) with known pedigree structures. The family information is prior knowledge that we can model in the random effects of a linear mixed effects model.
A few days ago I started working on hypotheses tests and confidence intervals for my project
mixed_models, and I got pretty surprised by certain things.
I made some more progress on my Google Summer of Code project MixedModels. The linear mixed models fitting method is now capable of handling non-numeric (i.e., categorical) predictor variables, as well as interaction effects. Moreover, I gave the method a user friendly R-formula-like interface. I will present these new capabilities of the Ruby gem with an example. Then I will briefly describe their implementation.
During the last two weeks I made some progress on my Google Summer of Code project.
The Ruby gem is now capable of fitting linear mixed models. In this short blog post I want to give an example, and compare the results I get in Ruby to those obtained by
lme4 in R.