I came across a dataset of NBA player salaries from the 1984-1985 season to the 2017-2018 season here, and I thought it would be a fun dataset to practice my tidyverse skills on. All the code for this post can … Continue reading →

I recently came across this short fun post on R-bloggers that demonstrated how to use the image.ContourDetector package (available on CRAN) to extract contours from an image. The image of the contours looked really cool so I thought I would … Continue reading →

Let’s say we have data such that . (We assume no ties among the ‘s for simplicity.) Isotonic regression gives us a monotonic fit for the ‘s by solving the problem (See this previous post for more details.) Nearly-isotonic regression, … Continue reading →

Isotonic regression is a method for obtaining a monotonic fit for 1-dimensional data. Let’s say we have data such that . (We assume no ties among the ‘s for simplicity.) Informally, isotonic regression looks for such that the ‘s approximate … Continue reading →

I’ve had the privilege of working with Trevor Hastie on an extension of the glmnet package which has just been released. In essence, the glmnet() function’s family parameter can now be any object of class family. This enables the user … Continue reading →

I’m writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R’s documentation. In this post, instead of looking at one of … Continue reading →

I learnt from a recent post on John Cook’s excellent blog that it’s really easy to do extended floating point computations in R using the Rmpfr package. Rmpfr is R’s wrapper around the C library MPFR, which stands for “Multiple … Continue reading →

I’m writing a series of posts on various function options of the glmnet function (from the package of the same name), hoping to give more detail and insight beyond R’s documentation. In this post, we will look at the type.gaussian … Continue reading →

I’m proud to announce that my latest research project, reluctant generalized additive modeling (RGAM), is complete (for now)! In this post, I give a brief overview of the method: what it is trying to do and how you can fit … Continue reading →

Assume that we are in the time series data setting, where we have data at equally-spaced times which we denote by random variables . The AR(1) model, commonly used in econometrics, assumes that the correlation between and is , where is … Continue reading →

Imagine that one has a data matrix consisting of observations, each with features, as well as a response vector . We want to build a model for using the feature columns in . In ordinary least squares (OLS), one seeks … Continue reading →

I’ve been working on a project/package that closely mirrors that of GAMSEL (generalized additive model selection), a method for fitting sparse generalized additive models (GAMs). In preparing my package, I realized that (i) the gamsel package which implements GAMSEL doesn’t have … Continue reading →

When plotting an lm object in R, one typically sees a 2 by 2 panel of diagnostic plots, much like the one below: This link has an excellent explanation of each of these 4 plots, and I highly recommend giving … Continue reading →

To plot multiple figures on a single canvas in base R, we can change the graphical parameter mfrow. For instance, the code below tells R that subsequent figures will by drawn in a 2-by-3 array: If we then run this … Continue reading →