In R, the traditional way to load packages can sometimes lead to situations where several lines of code need to be written just to load packages. These lines can cause errors if the packages are not installed, and can also be hard to maintain, particularly during deployment.
Fortunately, there is a way to create a function in R...
Blog Archives
Loading and/or Installing Packages Programmatically
Monitoring Progress Inside a Foreach Loop
The foreach package for R is excellent, and allows for code to easily be run in parallel. One problem with foreach is that it creates new RScript instances for each iteration of the loop, which prevents status messages from being logged to the console output. This is particularly frustrating during long-running tasks, when we are often unsure...
Using LaTeX, R, and Sweave to Create Reports in Windows
LaTeX is a typesetting system that can easily be used to create reports and scientific articles, and has excellent formatting options for displaying code and mathematical formulas. Sweave is a package in base R that can execute R code embedded in LaTe...
Parallel R Model Prediction Building and Analytics
Modifying R code to run in parallel can lead to huge performance gains. Although a significant amount of code can easily be run in parallel, there are some learning techniques, such as the Support Vector Machine, that cannot be easily parallelized. However, there is an often overlooked way to speed up these and other models. It...
Analyzing US Government Contract Awards in R
As I was exploring open data sources, I came across USA spending. This site contains information on US government contract awards and other disbursements, such as grants and loans. In this post, we will look at data on contracts awarded in the state of Maryland in the fiscal year 2011, which is available by selecting "Maryland"...
R Regression Diagnostics Part 1
Linear regression can be a fast and powerful tool to model complex phenomena. However, it makes several assumptions about your data, and quickly breaks down when these assumptions, such as the assumption that a linear relationship exists between the predictors and the dependent variable, break down. In this post, I will introduce some diagnostics that you can...
Analyzing Federal Government Bailout Recipients in R
I was searching for open data recently, and stumbled on Socrata. Socrata has a lot of interesting data sets, and while I was browsing around, I found a data set on federal bailout recipients. Here is the data set. However, data sets on Socrata are not always the most recent versions, so I followed a...
An Intro to Ensemble Learning in R
Introduction
This post incorporates parts of yesterday's post about bagging. If you are unfamiliar with bagging, I suggest that you read it before continuing with this article.
I would like to give a basic overview of ensemble learning. Ensemble learning involves combining multiple predictions derived by different techniques in order to create a stronger overall prediction....
Improve Predictive Performance in R with Bagging
Bagging, aka bootstrap aggregation, is a relatively simple way to increase the power of a predictive statistical model by taking multiple random samples(with replacement) from your training data set, and using each of these samples to construct a separate model and separate predictions for your test set. These predictions are then averaged to create a, hopefully more accurate,...
Time Based Arbitrage Opportunities in Tick Data
I recently posted an introduction to the Kaggle Algorithmic Trading Challenge, which I competed in.I said that I would post about my experiences, and this is hopefully the first of a series. We were given tick data from the London Stock Exchange(speci...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
