Monthly Archives: July 2013

A Recipe for Trellis/Faceted Plots Using Base R Graphics

July 5, 2013
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A Recipe for Trellis/Faceted Plots Using Base R Graphics

I frequently use lattice and ggplot2 to create trellis/faceted graphics. But, I gave up using these packages in a recent application, where I had initially constructed a complex graphic using the base R plotting functions. When I later decided that I wanted a faceted version, there was a dilema: re-create the complex graphic using lattice

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Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and Frailty Models

July 5, 2013
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Veterinary Epidemiologic Research: Modelling Survival Data – Parametric and Frailty Models

Last post on modelling survival data from Veterinary Epidemiologic Research: parametric analyses. The Cox proportional hazards model described in the last post make no assumption about the shape of the baseline hazard, which is an advantage if you have no idea about what that shape might be. With a parametric survival model, the survival time

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Optimal Pricing for a Simple Monopolist

July 5, 2013
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Optimal Pricing for a Simple Monopolist

# A single price monopolist is a monopolist because it is the only supplier of a particular product. The monopolist therefore has the power to choose a price to sell the product at. # Those who have a willingness to pay which is greater than the ...

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Allocation Models With Bounded Dependent Variables

July 5, 2013
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Allocation Models With Bounded Dependent Variables

(This article was first published on Econometrics Beat: Dave Giles' Blog, and kindly contributed to R-bloggers) My post yesterday, on Allocation Models, drew a comment to the effect that in such models the dependent variables take values that must to be non-negative fractions. Well, as I responded, that's true sometimes (e.g., in the case of market shares); but not in...

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Meet the Revolution Analytics UK team

July 5, 2013
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Revolution Analytics has a new office in London to serve our customers in Europe. This press release introduces the team members who are based there: Neil Miller, Managing Director, International: Neil Miller is responsible for Revolution Analytics' business and growth outside the Americas and is based in the U.K. Neil Miller joined the company after 14 years at Accenture...

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ggplot2 Chloropleth of Supreme Court Decisions: A Tutorial

July 4, 2013
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ggplot2 Chloropleth of Supreme Court Decisions: A Tutorial

I don't do much GIS but I like to. It's rather enjoyable and involves a tremendous skill set. Often you will find your self grabbing data sets from some site, scraping, data cleaning and reshaping, and graphing. On the ride … Continue reading →

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Using neural networks for credit scoring: a simple example

July 4, 2013
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Using neural networks for credit scoring: a simple example

Credit scoring is the practice of analysing a persons background and credit application in order to assess the creditworthiness of the person. One can take numerous approaches on analysing this creditworthiness. In the end it basically comes down to first selecting the correct independent variables (e.g. income, age, gender) that lead to a given level of creditworthiness. In...

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R-ratio vs mean-variance optimization

July 4, 2013
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R-ratio vs mean-variance optimization

I am looking at the following tickers from 2005-01-01 to 2008-01-01. The tickers are GE, F, MSFT, DELL, INTC. I will find the mean-variance weights and the R-ratio weights and then test portfolio performance from 2008-01-01 to 2010-01-01. This is a … Continue reading →

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Creating good looking survival curves – the ‘ggsurv’ function

July 4, 2013
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Creating good looking survival curves – the ‘ggsurv’ function

This is a guest post by Edwin Thoen Currently I am doing my master thesis on multi-state models. Survival analysis was my favourite course in the masters program, partly because of the great survival package which is maintained by Terry Therneau. The …Read more »

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Fun simulating Wimbledon in R and Python

July 4, 2013
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Fun simulating Wimbledon in R and Python

R and Python have different strengths. There's little you can do in R you absolutely can't do in Python and vice versa, but there's a lot of stuff that's really annoying in one and nice and simple in the other. I'm sure simulations can be run in R, but it seems frightfully tricky. Recently I wrote a simple Tennis simulator...

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