I will be teaching a one semester hour class at UMKC, Introduction to R (MEDB 5505) on Monday, August 8, 2016 through Friday, August 12, 2016. It runs from 9am to noon on all five days. This is part of a series of classes that cover a basic introduction to statistical packages: data import, data management, simple graphs, and simple descriptive statistics. The other classes (MEDB 5506 and MEDB 5507) cover SPSS and SAS.
Here are some details about this class. Continue reading
I wrote a program in R Markdown that shows how the lasso regression model works. It has too many pictures to be easily ported to this blog, so I’ll share a link to a pdf file instead. You can also find the R Markdown code at my github repository.
Sometimes you end up trying to be so clever that you out-clever yourself. This happened to me today in R. I tried something that was supposed to save me time and it ended up costing me more time instead. Continue reading
I’ve been working with sparse matrices a bit for my work with the Greater Plains Collaborative. They are a very useful way of storing matrices where most of the entries are zero. This occurs quite often in medical data. There are thousands of medical procedures that you can torture your patients with, so any matrix that has indicator variables for every medical procedure will be quite big. Fortunately, both for us and for the patients, the number of procedures that a particular patient has to endure is quite a bit smaller. So for each row of the matrix, the number of non-zero entries will be very small, probably in the single digits. A sparse matrix will be much smaller because it stores only the location of the non-zero entries. Here’s some R code that shows how this works. I have the code available at my new github site. Continue reading
Sometimes you forget things. Here’s an example. Continue reading
Working with R is great in that if anything has been done in Statistics, there is an R package that will do it. The problem is that sometimes there are four packages that will do it. So when this happens what do you do? I want to outline what I did recently when I needed to find a package to calculate Cronbach’s alpha. Continue reading
This page promotes a new approach to a broad class of models (spatio-temporal models, latent variable models, mixed models) using a fast approximation to the Bayesian solution. It runs under R and appears to handle very large datasets. I have not had a chance to try this, but it looks very interesting. Continue reading
When you are drawing a graph in R, sometimes you have to check how big things are before you can do anything else. There are many different ways of measuring size. Here are some simple examples. You can find the R Markdown code that created these examples here. Continue reading
I wanted to provide an overview of how you analyze a classic nonlinear regression model. It is a difference of two exponential functions. This nonlinear function is used commonly in pharmocokinetic models and is a simply way to model the oral administration of a drug. I want to show how the model works in a mathematical sense and then how you fit it using R. Continue reading
This Revolutions blog talks about a fairly rigorous evaluation of popular programming languages done by the Institute of Electrical and Electronics Engineers (known by most people by its acronym, IEEE). The list shows all programming languages, including general purpose programming languages. Java C, and C++ are at the top of the list, but R, a language pretty much dedicated to data analysis, is number 6 on the list (up three places from the previous year. Quite an impressive showing. I have mentioned another webpage, http://r4stats.com/articles/popularity/, that compares R and other statistical software packages, and that is worth reading as well. Continue reading