While traveling across the Visayas, I encountered barangay (villages) with the name same as my last name. Using R and map data from gadm.org I search and mapped other villages in the country named “Salvacion”.

R Packages growth Curve Why R is so popular? There are a lot of reasons, such as: easy to learn and convenient to use, active community, open source, etc. Another important reason is the numerous contributed packages. Up to yesterday, there are 3854 R packages on CRAN. The following figure shows the growth curve of R package:

This post is from my new book Forecasting: principles and practice, available freely online at OTexts.com/fpp/. A non-seasonal ARIMA model can be written as (1) or equivalently as (2) where is the backshift operator, and is the mean of . R uses the parametrization of equation (2). Thus, the inclusion of a constant in a non-stationary ARIMA...

Random number generation is a core topic in numerical computer science. There are many efficient algorithms for generating random (strictly speaking, pseudo-random) variates from different probability distributions. The figure below shows a sampling of 1000 two-dimensional random variates from the … Continue reading →

The Bland-Altman plot is a visual aid for assessing differences between two ways of measuring something. For example, one might compare two scales this way, or two devices for measuring particulate matter. The plot simply displays the difference between the measures against their average. Rather than a statistical test, it is intended...

Revolution Analytics is proud to announce the latest update to our enhanced, production-grade distribution of R, Revolution R Enterprise. This update expands the range of supported computation platforms, adds new Big Data predictive models, and updates to the latest stable release of open source R (2.14.2), which improves performance of the R interpreter by about 30%. This release expands...

This is my third update to my original post on predicting the NBA playoffs with an algorithm. Here are updates 1 and 2. The algorithm correctly predicted a Boston win, but missed on the Spurs/Thunder game, so it is currently 4-2. Haven't had any time to update yet, so I will only be able to give you predictions for...

Say you havea <- c(1,3,5,7,9)b <- c(3,6,8,9,10)c <- c(2,3,4,5,7,9)A straightforward way to do the job is:intersect(intersect(a,b),c)More cleverly, and more conveniently if you have a lot of arguments:Reduce(intersect, list(a,b,c))The Reduce fu...

Today I want to examine the performance of stocks in the S&P 500 grouped into Quantiles based on one year historical Volatility. The idea is very simple: each week we will form Volatility Quantiles portfolios by grouping stocks in the S&P 500 into Quantiles using one year historical Volatility. Next we will backtest each portfolio

Following the announcement of the US Government Big Data Initiative, I was asked to write a small article about applications of R in government. The article has just appeared in Government Security News (and I believe will appear in their daily newsletter tomorrow). In the article, I highlighted several R applications that been highlighted here in the blog: In...

In our pre-conference workshop, Brian Peterson and I worked with the EDHEC hedge fund indexes as a way to demonstrate how to use PortfolioAnalytics within the context of long-term allocation problems. Although they are not investible, these indexes are probably more representative than most given that they are, in fact, meta-indexes. Other indexes might be

Following the previous post on life contingencies and actuarial models in life insurance, I upload additional material for the short course at the 6th R/Rmetrics Meielisalp Workshop & Summer School on Computational Finance and Financial Engineering organized by ETH Zürich, https://www.rmetrics.org/. The second part of the talk (on Actuarial models with R) will be dedicated to longevity and mortality. A complete...

Today we’re very excited to announce RPubs, a free service that makes it easy to publish documents to the web from R. RPubs is a quick and easy way to disseminate data analysis and R code and do ad-hoc collaboration with peers. RPubs documents are based on R Markdown, a new feature of knitr 0.5 and RStudio 0.96. To publish

Recently needed to extract a small "chunk" from a collection of adjacent MrSid mosaics, each about 4Gb in size. Once again, GDAL came to the rescue, and saved much time and agony wile working with very large, compressed, and proprietary-format files. T...

Most of regression methods assume that the response variables follow some exponential distribution families, e.g. Guassian, Poisson, Gamma, etc. However, this assumption was frequently violated in real world data by, for example, zero-inflated overdispersion problem. A number of methods were developed to deal with such problem, and among them, Quasi-Poisson and Negative Binomial are the most popular methods perhaps due...

Many of you have heard about RStudio’s latest release and it’s new R Markdown feature. Today, I’d like to announce the markdown package for R, a tool for converting Markdown documents to HTML, created in collaboration with RStudio. It...

The following post shows how to manually convert a Sweave LaTeX document into a knitr R Markdown document. The post (1) reviews many of the required changes; (2) provides an example of a document converted to R Markdown format based on an analysis of Winter Olympic Medal data up to and including 2006; and (3) discusses the pros...

Maximum drawdown is blazingly variable. Psychology Probably the most salient feature that an investor notices is the amount lost since the peak: that is, the maximum drawdown. Just because drawdown is noticeable doesn’t mean it is best to notice. Statistics The paper “About the statistics of the maximum drawdown in financial time series” explores drawdown … Continue reading...

by Yanchang Zhao, RDataMining.com There are some nice slides and R code examples on Data Mining and Exploration at http://www.inf.ed.ac.uk/teaching/courses/dme/, which are listed below. PDF Slides: - Overview of Data Mining http://www.inf.ed.ac.uk/teaching/courses/dme/2012/slides/datamining_intro4up.pdf - Visualizing Data http://www.inf.ed.ac.uk/teaching/courses/dme/2012/slides/visualisation4up.pdf - Decision trees http://www.inf.ed.ac.uk/teaching/courses/dme/2012/slides/classification4up.pdf … Continue reading →