Here's a quick R implementation of David Varadi's alternative to the RSI(2). Michael Stokes over at the MarketSci blog has three great posts exploring this indicator: Varadi’s RSI(2) Alternative: The DV(2) RSI(2) vs. DV(2) Last Couple...

miss attach miss result1 summary(result1) Call: glm(formula = a ~ b, family = binomial(logit)) Deviance Residuals: Min 1Q Median 3Q Max -1.8864 -1.2036 0.7397 0.9425 1.4385 Coefficients: ...

miss attach miss result1 summary(result1) Call: glm(formula = a ~ b, family = binomial(logit)) Deviance Residuals: Min 1Q Median 3Q Max -1.8864 -1.2036 0.7397 0.9425 1.4385 Coefficients: ...

1. Installing the required tools To build an R package in Windows, you will need to install some additional software tools. These are summarized at http://www.murdoch-sutherland.com/Rtools 1.1 Essential: Rtools This is a collection of unix-like tools that can be run from the DOS command prompt. It also contains the MinGW compilers that are used for

1. Installing the required tools To build an R package in Windows, you will need to install some additional software tools. These are summarized at http://www.murdoch-sutherland.com/Rtools 1.1 Essential: Rtools This is a collection of unix-like tools...

As I mentioned in my quick write-up of UseR 2009, one of my talks was about cran2deb: a system to turn (essentially) all CRAN packages into directly apt-get-able binary packages. This is essentially a '2.0' version of earlier work with Steffen Moel...

As I mentioned in my quick write-up of UseR 2009, one of my talks was about cran2deb: a system to turn (essentially) all CRAN packages into directly apt-get-able binary packages. This is essentially a '2.0' version of earlier work with Steffen Moelle...

As I mentioned in my quick write-up of UseR 2009, one of my talks was about cran2deb: a system to turn (essentially) all CRAN packages into directly apt-get-able binary packages. This is essentially a '2.0' version of earlier work with Steffen Moel...

Working some more with time series data. Here we have a graph of Obama job approval numbers, with two LOWESS-fit lines added for trending:Figure1. President Obama job approval, Jan 2009 - present.There's actually some pretty fancy stuff going on there, as the following code shows.polls lfit1 lfit2 plot (app~daten, ylim=c(40,80), xlim=c(-3,210),pch=16, col="gray",cex.lab=1.25,cex.axis=0.75,col.lab = "#777777", xlab="",ylab="Obama...

Working some more with time series data. Here we have a graph of Obama job approval numbers, with two LOWESS-fit lines added for trending: Figure1. President Obama job approval, Jan 2009 - present.There's actually some pretty fancy stuff going on there, as the following code shows.polls lfit1 lfit2 plot (app~daten, ylim=c(40,80), xlim=c(-3,210),pch=16, col="gray",cex.lab=1.25,cex.axis=0.75,col.lab = "#777777", xlab="",ylab="Obama...

I spent most of last week in Rennes, the capital of Brittany in France, as it was time for UseR! 2009, the annual R conference. Francois Husson, Aline Legrand and others at the Agrocampus Ouest had put together a really well-run conference, and it w...

I spent most of last week in Rennes, the capital of Brittany in France, as it was time for UseR! 2009, the annual R conference. Francois Husson, Aline Legrand and others at the Agrocampus Ouest had put together a really well-run conference, and it was ...

I spent most of last week in Rennes, the capital of Brittany in France, as it was time for UseR! 2009, the annual R conference. Francois Husson, Aline Legrand and others at the Agrocampus Ouest had put together a really well-run conference, and it w...

I've been following the discussion on causal inference over at Gelman's blog with quite a bit of interest. Of course, this is in response to Judea Pearl's latest book on causal inference, which differs quite a bit from the theory that had been forwarde...