Maximum-Likelihood parameter estimation in R: A brief tutorial

September 9, 2015
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(This article was first published on R – Nathaniel D. Phillips, PhD, and kindly contributed to R-bloggers)

A brief tutorial on ML estimation in R

Recently, a colleague asked me to demonstrate how one can calculate maximum-likelihood (ML) parameter estimates in R. As I had been meaning to do this for my R course anyway, I decided to write up the following brief tutorial. In the tutorial, I go through the three basic steps of ML estimation in R for both a statistical example (the simple linear model) and a psychological model of choice (the Proportional Difference model, Gonzalez-Vallejo, 2001).

You can view the tutorial on the preceding link. If you have any comments, criticisms or questions, please let me know and I will try to update the document!

Note: I am in debt to Dr. Stephen Lewandowsky and Dr. Simon Farrell’s summer school on computational cognitive modelling for teaching me these techniques. You can purchase their great book on the subject here Computational Modeling in Cognition: Principles and Practice

To leave a comment for the author, please follow the link and comment on their blog: R – Nathaniel D. Phillips, PhD.

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