# "Testing Statistical Hypotheses" and "Theory of Point Estimation" impressions

I spent much of the last two months reading Lehmann & Romano “Testing Statistical Hypotheses” (3rd ed.) and Lehmann & Casella “Theory of Point Estimation” (2nd ed.), abbr. TSH and TPE. The following is a collection of random facts observations I made while reading TSH and TPE. The choice of topics is biased towards application in regression models.

# Statistical linear mixed models in Ruby with mixed_models (GSoC2015)

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!

# A (naive) application of linear mixed models to genetics

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.

# P-values and confidence intervals

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.

# MixedModels Formula Interface and Categorical Variables

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.