Monthly Archives: June 2014

Bayesian First Aid: Test of Proportions

June 26, 2014
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Bayesian First Aid: Test of Proportions

Does pill A or pill B save the most lives? Which web design results in the most clicks? Which in vitro fertilization technique results in the largest number of happy babies? A lot of questions out there involves estimating the proportion or relative frequency of success of two or more groups (where success could be a saved life, a...

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Include promo/activity effect into the prediction (extended ARIMA model with R)

Include promo/activity effect into the prediction (extended ARIMA model with R)

I want to consider an approach of forecasting I really like and frequently use. It allows to include the promo campaigns (or another activities and other variables as well) effect into the prediction of total amount. I will use a fictitious example and data in this post, but it works really good with my real data.  So, you can... Read More »

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Statistics, and the Goldilocks Principle

June 26, 2014
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By the end of May, in Toronto, we had that great talk at the SSC by Jeff Rosenthal, on monte carlo techniques, and Jeff mention the name of “the Goldilocks principle” (it was in the contect of MCMC, and I did mention it in my talk in London on MCMC, when I discussed the value of the rejection rate...

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(Py, R, Cmd) Stan 2.3 Released

June 26, 2014
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We’re happy to announce RStan, PyStan and CmdStan 2.3. Instructions on how to install at: http://mc-stan.org/ As always, let us know if you’re having problems or have comments or suggestions. We’re hoping to roll out the next release a bit quicker this time, because we have lots of good new features that are almost ready The post

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Updates to R package raincpc: Global Daily Rainfall for over 35 years

June 26, 2014
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Updates to R package raincpc: Global Daily Rainfall for over 35 years

The Climate Prediction Center's  (CPC) global rainfall data, 1979 - present, 50 km resolution, is one of the few high-quality, long-term, observation-based, daily rainfall products available for free. Although raw data is available at&nb...

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Review of Applied Predictive Modeling by Kuhn and Johnson

June 26, 2014
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Review of Applied Predictive Modeling by Kuhn and Johnson

by Joseph Rickert Predictive Modeling or “Predictive Analytics”, the term that appears to be gaining traction in the business world, is driving the new “Big Data” information economy. Predictably, there is no shortage of material to be found on this subject. Some discussion of predictive modeling is sure to be found in any reasonably technical presentation of business decision...

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Maybe I Don’t Really Know R After All

June 26, 2014
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Maybe I Don’t Really Know R After All

Lately, I’ve been feeling that I’m spreading myself too thin in terms of programming languages. At work, I spend most of my time in Hive/SQL, with the occasional Python for my smaller data. I really prefer Julia, but I’m alone at work on that one. And since I maintain a package on CRAN (RSiteCatalyst), I frequently spend Related posts:

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Jun 26-27, 2014 – Introduction to Data Science with R in NYC

June 26, 2014
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Jun 26-27, 2014 – Introduction to Data Science with R in NYC

You can either register from eventbrite or our school site NYC Data Science Academy. Date: Thursday/Friday , June 26th and 27th, 2014 Time:  9:00am to 5:00pm Location: 500 7th Ave, 17th Floor, glass door classroom, New York, NY 10018 NYC Data Science Academy, training subbrand of SupStat (Official Training partner with RStudio Inc) is hosting our... Read more »

Tailoring univariate probability distributions

June 26, 2014
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Tailoring univariate probability distributions

This post shows how to build a custom univariate distribution in R from scratch, so that you end up with the essential functions: a probability density function, cumulative distribution function, quantile function and random number generator. In the beginning all you need is an equation of the probability density function, … Continue reading →

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Be Careful with Using Model Design in R

June 25, 2014
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Be Careful with Using Model Design in R

In R, useful functions for making design matrices are model.frame and model.matrix. I will to discuss some of the differences of behavior across and within the two functions. I also have an example where I have run into this problme and it caused me to lose time. Using model.frame for a design matrix Whenever I

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