# Posts Tagged ‘ Rstats ’

## Simudidactic

November 23, 2013
By

auto·di·dact n. A self-taught person. From Greek autodidaktos, self-taught : auto-, auto- + didaktos, taught; + sim·u·late v. To create a representation or model of (a physical system or particular situation, for example). From Latin simulre, simult-, from similis, like; = (If you can get past the mixing of Latin and Greek roots) sim·u·di·dactic adj. To learn by creating a representation or model of a physical system or […]

Read more »

## Montreal R User Group – Dr. Ramnath Vaidyanathan on his rCharts package

October 27, 2013
By

Monday, October 28, 2013. 6:00pm at Notman House 51 Sherbrooke W., Montreal, QC. We are very pleased to welcome back Dr. Ramnath Vaidyanathan for a talk on interactive documents as it relates to his excellent rCharts package. Bringing a laptop to follow along is highly encouraged. I would recommend installing rCharts prior to the workshop. library(devtools) pkgs <- c(‘rCharts’, ‘slidify’, ‘slidifyLibraries’) install_github(pkgs, ‘ramnathv’, ref […]

Read more »

## Follow up to Johnson et al Post

October 21, 2013
By

Last week I posted a comment on a paper by Neil Johnson and colleagues that I now regret. The comment amounted to a bit of statistical pedantry on my part regarding some of the wording in the paper. It was my wording in this post, and specifically the title, which would have benefited from some […]

Read more »

## P-value fallacy alive and well: Latest case in Scientific Reports

October 17, 2013
By

Erratum (10/17/13): The paper was published in Scientific Reports, an OA journal from the publishers of Nature, and not in the Journal Nature as originally reported. Clarification (10/17/13): The paper discussed here is quite good overall and very interesting. I do not believe that anything in this post calls into question any of its main […]

Read more »

## Calculating AUC the hard way

October 10, 2013
By

The Area Under the Receiver Operator Curve is a commonly used metric of model performance in machine learning and many other binary classification/prediction problems. The idea is to generate a threshold independent measure of how well a model is able to distinguish between two possible outcomes. Threshold independent here just means that for any model […]

Read more »

## Marginal likelihood from tempered Bayesian posteriors

October 1, 2013
By
$Marginal likelihood from tempered Bayesian posteriors$

Introduction In the previous post I showed that it is possible to couple parallel tempered MCMC chains in order to improve mixing. Such methods can be used when the target of interest is a Bayesian posterior distribution that is difficult to sample. There are (at least) a couple of obvious ways that one can temper […]

Read more »

## Parallel tempering and Metropolis coupled MCMC

September 29, 2013
By
$Parallel tempering and Metropolis coupled MCMC$

Introduction Parallel tempering is a method for getting Metropolis-Hastings based MCMC algorithms to work better on multi-modal distributions. Although the idea has been around for more than 20 years, and works well on many problems, it still isn’t routinely used in applications. I think this is partly because relatively few people understand how it works, […]

Read more »

## Summary stats for ABC

September 1, 2013
By
$Summary stats for ABC$

Introduction In the previous post I gave a very brief introduction to ABC, including a simple example for inferring the parameters of a Markov process given some time series observations. Towards the end of the post I observed that there were (at least!) two potential problems with scaling up the simple approach described, one relating […]

Read more »

## Time-series forecasting: Bike Accidents

August 20, 2013
By

About a year ago I posted this video visualization of all the reported accidents involving bicycles in Montreal between 2006 and 2010. In the process I also calculated and plotted the accident rate using a monthly moving average. The results followed a pattern that was for the most part to be expected. The rate shoots up […]

Read more »

## From Whale Calls to Dark Matter: Competitive Data Science with R and Python

July 12, 2013
By

Back in June I gave a fun talk at Montreal Python on some of my dabbling in the competitive data science scene. The good people at Savior-fair Linux recorded the talk and have edited it all together into a pretty slick video. If you can spare twenty-minutes or so, have a look. If you want […]

Read more »

 Tweet

Email: