Monthly Archives: April 2013

Review: Kölner R Meeting 12 April 2013

April 23, 2013
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Review: Kölner R Meeting 12 April 2013

Our 5th Cologne R user group meeting was the best attended meeting so far, with 20 members finding their way to the Institute of Sociology for two talks by Diego de Castillo on shiny and Stephan Holtmeier on cluster analysis, followed by beer and schnitzel at the Lux, a gastropub nearby.ShinyDiego gave an overview of...

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Installation of WRS package (Wilcox’ Robust Statistics)

April 22, 2013
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Some users had trouble installing the WRS package from R-Forge. Here’s a method that should work automatically and fail-safe: ?View Code RSPLUS# first: install dependent packages install.packages(c("MASS", "akima", "robustbase"))   # second: install suggested packages install.packages(c("cobs", "robust", "mgcv", "scatterplot3d", "quantreg", "rrcov", "lars", "pwr", "trimcluster", "parallel", "mc2d", "psych", "Rfit"))   # third: install WRS install.packages("WRS", repos="http://R-Forge.R-project.org",

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Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization

Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization

In an earlier post, I introduced the golden section search method – a modification of the bisection method for numerical optimization that saves computation time by using the golden ratio to set its test points.  This post contains the R function that implements this method, the R functions that contain the 3 functions that were

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The Golden Section Search Method: Modifying the Bisection Method with the Golden Ratio for Numerical Optimization

The Golden Section Search Method: Modifying the Bisection Method with the Golden Ratio for Numerical Optimization

Introduction The first algorithm that I learned for root-finding in my undergraduate numerical analysis class (MACM 316 at Simon Fraser University) was the bisection method.  It’s very intuitive and easy to implement in any programming language (I was using MATLAB at the time).  The bisection method can be easily adapted for optimizing 1-dimensional functions with

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Adding Percentiles to PDQ

April 22, 2013
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Adding Percentiles to PDQ

Pretty Damn Quick (PDQ) performs a mean value analysis of queueing network models: mean values in; mean values out. By mean, I mean statistical mean or average. Mean input values include such queueing metrics as service times and arrival rates. These could be sample means. Mean output values include such queueing metrics as waiting time and queue...

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Upcoming GDAT Class May 6-10, 2013

April 22, 2013
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Upcoming GDAT Class May 6-10, 2013

Enrollments are still open for the Level III Guerrilla Data Analysis Techniques class to be held during the week May 6—10. Early-bird discounts are still available. Enquire when you register. As usual, all classes are held at our lovely Larkspur...

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Gridding data for multi-scale macroecological analyses

April 22, 2013
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Gridding data for multi-scale macroecological analyses

These are materials for the first practical lesson of the Spatial Scale in Ecology course. All of the data and codes are available here. The class covered a 1.5h session. R code for the session is also at the end…Read more →

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Time Varying Higher Moments with the racd package.

April 22, 2013
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Time Varying Higher Moments with the racd package.

The Autoregressive Conditional Density (ACD) model of Hansen (1994) extended GARCH models to include time variation in the higher moment parameters. It was a somewhat natural extension to the premise of time variation in the conditional mean and variance, though it probably raised more questions than it, or subsequent research have been able to answer.

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Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

April 22, 2013
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Veterinary Epidemiologic Research: Count and Rate Data – Poisson & Negative Binomial Regressions

Still going through the book Veterinary Epidemiologic Research and today it’s chapter 18, modelling count and rate data. I’ll have a look at Poisson and negative binomial regressions in R. We use count regression when the outcome we are measuring is a count of number of times an event occurs in an individual or group

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R et Twitter

April 22, 2013
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R et Twitter

(This article was first published on Learning Data Science , and kindly contributed to R-bloggers) On va dans ce post, illustrer une utilisation simple des packages twitteR, StreamR, tm qui permettent faire du textmining. En réalité, les deux premiers permettent de récuperer les tweets et de faire des comptages simples et complexes et le dernier permet de faire du...

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