The Keep Function
Reading Time: 2 minutes Occasionally when I am jotting some code I find myself creating several temporary variables with the intention of later getting rid of them. These variables involve...continue reading.
Category: R
Why I use Panel/Multilevel Methods
I don’t understand why any researcher would choose not to use panel/multilevel methods on panel/hierarchical data. Let’s take the following linear regression as an example: , where is a random...continue reading.
Hot news detection using Wikipedia
Wikipedia is, indeed, one of the largest free access crowdsourcing [1] sources of information in today’s world. Every day, thousands of people land on its pages to get information about...continue reading.
Stop and Frisk: Spatial Analysis of Racial Differences
In my last post, I compiled and cleaned publicly available data on over 4.5 million stops over the past 11 years. I also presented preliminary summary statistics showing that blacks had...continue reading.
Cluster analysis on earthquake data from USGS
Theoretical BackgroundIn some cases we would like to classify the events we have in our dataset based on their spatial location or on some other data. As an example we...continue reading.
Cracking Safe Cracker with R
My wife got me a Safe Cracker 40 puzzle a while back. I believe I misplaced the solution some time back. The company, Creative Crafthouse, stands behind their products. They...continue reading.
Modeling Ebola Contagion Using Airline Networks in R
I first became interested in networks when reading Matthew O’Jackson’s 2010 paper describing their application to economics. During the 2014 ebola outbreak, there was a lot of concern over the disease spreading to the...continue reading.
Live Earthquake Map with Shiny and Google Map API
In the post Exchange data between R and the Google Maps API using Shiny I presented a very simple way to allow communication between R and javascript using Shiny.This is...continue reading.
Granger Causality Test
# READ QUARTERLY DATA FROM CSV library(zoo) ts1 <- read.zoo(‘Documents/data/macros.csv’, header = T, sep = ",", FUN = as.yearqtr) # CONVERT THE DATA TO STATIONARY TIME SERIES ts1$hpi_rate <- log(ts1$hpi...continue reading.
Interactive maps of Crime data in Greater London
In the previous post we looked at ways to perform some introductory point pattern analysis of open data downloaded from Police.uk. As you remember we subset the dataset of crimes...continue reading.
Simple Regime Change Detection with t-test
It is always fun to find trend in time series data. But what about the scenarios where the trend in the time series changes. Detecting the point of this trend...continue reading.
How Predictable is the English Premier League?
The reason why football is so exciting is uncertainty. The outcome of any match or league is unknown, and you get to watch the action unfold without knowing what’s going...continue reading.
Coal and the Conservatives
Interesting election results in the UK over the weekend, where the Conservatives romped to victory. This was despite a widespread consensus that neither the Conservative or Labour party would get...continue reading.
Read A Block of Spreadsheet with R
In R, there are two ways to read a block of the spreadsheet, e.g. xlsx file, as the one shown below. The xlsx package provides the most intuitive interface with...continue reading.
Modeling Count Time Series with tscount Package
The example below shows how to estimate a simple univariate Poisson time series model with the tscount package. While the model estimation is straightforward and yeilds very similar parameter estimates...continue reading.
rPithon vs. rPython
Similar to rPython, the rPithon package (http://rpithon.r-forge.r-project.org) allows users to execute Python code from R and exchange the data between Python and R. However, the underlying mechanisms between these two...continue reading.
Autoregressive Conditional Poisson Model – I
Modeling the time series of count outcome is of interest in the operational risk while forecasting the frequency of losses. Below is an example showing how to estimate a simple...continue reading.
Ensemble Learning with Cubist Model
The tree-based Cubist model can be easily used to develop an ensemble classifier with a scheme called “committees”. The concept of “committees” is similar to the one of “boosting” by...continue reading.
Model Segmentation with Cubist
Cubist is a tree-based model with a OLS regression attached to each terminal node and is somewhat similar to mob() function in the Party package (https://statcompute.wordpress.com/2014/10/26/model-segmentation-with-recursive-partitioning). Below is a demonstrate...continue reading.
NYC Motor Vehicle Collisions – Street-Level Heat Map
In this post I will extend a previous analysis creating a borough-level heat map of NYC motor vehicle collisions. The data is from NYC Open Data. In particular, I will go from borough-level...continue reading.
