Monthly Archives: October 2013

Poisson regression fitted by glm(), maximum likelihood, and MCMC

October 29, 2013
By
Poisson regression fitted by glm(), maximum likelihood, and MCMC

The goal of this post is to demonstrate how a simple statistical model (Poisson log-linear regression) can be fitted using three different approaches. I want to demonstrate that both frequentists and Bayesians use the same models, and that it is the fitting procedure and the inference that differs. This is … Continue reading →

Read more »

Rcpp 0.10.6

October 29, 2013
By

A new maintenance release 0.10.6 of Rcpp is now on the CRAN network for GNU R; binaries for Debian have been uploaded as well. This version ties up a number of smaller loose ends, but also adds a few new things, particularly John's new exposeClass....

Read more »

Video: Revolution R Enterprise 7 interview on theCUBE

October 29, 2013
By

I'm in New York City for the Strata + Hadoop World conference, and last night I got the chance to stop by theCUBE for an live interview about Revolution R Enterprise 7. You can watch the full interview below, or click the links on the topics to skip ahead. Many thanks to Dave Vellante of Wikibon and John Furrier...

Read more »

Fellow me

October 29, 2013
By

Last summer I have applied for a NIHR Research Methods fellowship. Earlier this week the results have come out and they have liked my proposal, which is of course great news. The idea of this project is to critically evaluate the stepped...

Read more »

2013-10 Automatic Conversion of Tables to LongForm Dataframes

October 29, 2013
By

TableToLongForm automatically converts hierarchical Tables intended for a human reader into a simple LongForm Dataframe that is machine readable, hence enabling much greater utilisation of the data. It does this by recognising positional cues present in the hierarchical Table (which … Continue reading →

Read more »

Two interesting ideas here: “trading time” price impact of a…

October 29, 2013
By
Two interesting ideas here:
“trading time”
price impact of a…

Two interesting ideas here: "trading time" price impact of a trade proportional to exp( √size ) Code follows: require(quantmod) getSymbols("MER") #Merrill Lynch #Gatheral's model HiLo Op(symbol) #munging mer names(mer) = "UpDay"names(mer) = "HiLo" mer ...

Read more »

Two interesting ideas here: “trading time” price impact of a…

October 29, 2013
By
Two interesting ideas here:
“trading time”
price impact of a…

Two interesting ideas here: "trading time" price impact of a trade proportional to exp( √size ) Code follows: require(quantmod) getSymbols("MER") #Merrill Lynch #Gatheral's model HiLo Op(symbol) #munging mer names(mer) = "UpDay"names(mer) = "HiLo" mer ...

Read more »

Scaling up text processing and Shutting up R: Topic modelling and MALLET

October 29, 2013
By
Scaling up text processing and Shutting up R: Topic modelling and MALLET

In this post I show how a combination of MALLET, Python, and data.table means we can analyse quite Big data in R, even though R itself buckles when confronted by textual data.  Topic modelling is great fun. Using topic modelling I have been able to separate articles about the 'Kremlin' as a) a building, b) an international actor c) the...

Read more »

A first attempt at an individual-based model in R

October 29, 2013
By
A first attempt at an individual-based model in R

I have been curious for a while as to how R might be used for the construction of an individually-based model (IBM), or agent-based model (ABM). In particular, what R objects lend themselves best to storing information on individuals, and allow for new...

Read more »

Forecasting the number of visitors on your website using R. Part II

October 29, 2013
By
Forecasting the number of visitors on your website using R. Part II

This blog is the second post of a series of three blogs. Previous Blog Implementing the time-series exponential smoothing in R: I have used the HoltWinters (also a function in the forecasting package of R ) model to implement the exponential smoothing on the visitors data. This model will take care of the Seasonality, Trend,

Read more »