1042 search results for "regression"

Fun with the proto package: building an MCMC sampler for Bayesian regression

August 12, 2010
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Fun with the proto package: building an MCMC sampler for Bayesian regression

The proto package is my latest favourite R goodie. It brings prototype-based programming to the R language - a style of programming that lets you do many of the things you can do with classes, but with a lot less up-front work. Louis Kates and Thomas P...

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Fun with the proto package: building an MCMC sampler for Bayesian regression

August 12, 2010
By
Fun with the proto package: building an MCMC sampler for Bayesian regression

The proto package is my latest favourite R goodie. It brings prototype-based programming to the R language - a style of programming that lets you do many of the things you can do with classes, but with a lot less up-front work. Louis Kates and Thomas Petzoldt provide an excellent introduction to using proto in the

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R Environments for Gibbs Sampler State

August 10, 2010
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R Environments for Gibbs Sampler State

I recently decided to revisit some R code that implements a Gibbs sampler in an attempt to decrease the iteration time. My strategy was to implement the sampler state as an R environment rather than a list. The rationale was that passing an environment to and from functions would reduce the amount of duplication (memory

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Big data for R

August 5, 2010
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Big data for R

Revolutions Analytics recently announced their "big data" solution for R. This is great news and a lovely piece of work by the team at Revolutions.

However, if you want to replicate their analysis in standard R, then you can absolutely do so and we show you how.

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Big data for R

August 5, 2010
By
Big data for R

Revolutions Analytics recently announced their "big data" solution for R. This is great news and a lovely piece of work by the team at Revolutions.

However, if you want to replicate their analysis in standard R, then you can absolutely do so and we show you how.

Read more »

use R! 2010 conference — reflections

August 4, 2010
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From July 20-23, this year's use R! conference took place in Gaithersburg near Washington. I attended the conference as part of my holidays in the
U.S. and had a good time there. I met some people, even though that is not the easiest thing for me to do, and I got some inspirations and ideas
I outline below:


Stat apps


One...

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Meeting in the middle; or fudging model II regression with nls

August 3, 2010
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Meeting in the middle; or fudging model II regression with nls

My colleague Karen needed an equation to predict trunk diameter given tree height, which she hoped to base on measurements of trees in semi-arid Australian woodlands. This is the dark art of allometry and a quick google found a large number of formulae that have been used in different studies of tree dimensions. No problem: I started to...

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Meeting in the middle; or fudging model II regression with nls

August 3, 2010
By
Meeting in the middle; or fudging model II regression with nls

My colleague Karen needed an equation to predict trunk diameter given tree height, which she hoped to base on measurements of trees in semi-arid Australian woodlands. This is the dark art of allometry and a quick google found a large number of formulae...

Read more »

Announcing Big Data for Revolution R

August 3, 2010
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I've hinted this was coming a few times before, but with today's press release the announcement is official: the next release of Revolution R Enterprise will include "Big Data" capabilities thanks to the new RevoScaleR package. We're pretty excited at how it's turned out: it's kinda amazing to be able to use R's formula syntax like this: arrDelayLm2 <-...

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Using Optmatch and RItools for Observational Studies

July 29, 2010
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Using Optmatch and RItools for Observational Studies

I am a contributor to the optmatch and the RItools packages for R. These two packages are separate, but complimentary. Both packages provide tools for adjusting observational data to exhibit “balance” on observed covariates. In a randomized control trial, treatment and control groups should have identical distributions over all covariates, observed and unobserved. Matching provides a...

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