## 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

April 22, 2013
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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...

## Upcoming GDAT Class May 6-10, 2013

April 22, 2013
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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...

## Gridding data for multi-scale macroecological analyses

April 22, 2013
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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 →

## Time Varying Higher Moments with the racd package.

April 22, 2013
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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.

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

April 22, 2013
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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

April 22, 2013
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(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...

## 2D plot with histograms for each dimension (2013 edition)

April 22, 2013
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In 2009, I wrote about a way to show density plots along both dimensions of a plot. When I ran the code again to adapt it to a new project, it didn't work because ggplot2 has become better in the meantime. Below is the updated code. Using the gridExtra...

## Using data.table for binning

I discovered the impressive data.table package more than a year ago. In order to learn how to use it, I …Continuar leyendo »

## garch and the distribution of returns

April 22, 2013
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Using garch to learn a little about the distribution of returns. Previously There are posts on garch — in particular: A practical introduction to garch modeling The components garch model in the rugarch package garch and long tails There has also been discussion of the distribution of returns, including a satire called “The distribution of … Continue reading...

## Data Analysis for Marketing Research with R Language (1)

April 22, 2013
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Data Analysis technologies such as t-test, ANOVA, regression, conjoint analysis, and factor analysis are widely used in the marketing research areas of A/B Testing, consumer preference analysis, market segmentation, product pricing, sales driver analysis, and sales forecast etc. Traditionally the analysis tools are mainly SPSS and SAS, however, the open source R language is catching

## analyze the medical large claims experience study (mlces) with r

April 21, 2013
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not a survey, not even remotely current, the society of actuaries' medical large claims experience study (mlces) might be the best private health insurance claims data available to the public.  this data should be used to calibrate other data sets...

April 21, 2013
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Thanks to a helpful SO-Answer I was able to download all CLC vector data (43 zip-files) programmatically:require(XML)path_to_files dir.create(path_to_files)setwd(path_to_files)doc urls # function to get zip file namesget_zip_name # function to plug into sapplydl_urls # download all zip-filessapply(urls, dl_urls)# function for unzippingtry_unzip # unzip all files in dir and delete them afterwardssapply(list.files(pattern = "*.zip"),...

## You Can Quote Me on That

April 21, 2013
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The other day I came across the Empirical Quotes page on Mark Byran's blog. Some of his quotes related specifically to econometrics, and I thought I'd share a few others. That certainly doesn't mean that I agree with them all! "It is the preparation skill of the econometric chef that catches the professional eye, not the...

## Evaluating Event Impact Through Social Media Follower Histories, With Possible Relevance to cMOOC Learning Analytics

April 21, 2013
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Last year I sat on a couple of panels organised by I’m a Scientist’s Shane McCracken at various science communication conferences. A couple of days ago, I noticed Shane had popped up a post asking Who are you Twitter?, a quick review of a social media mapping exercise carried out on the followers of the

## What Is the Probability of a 16 Seed Beating a 1 Seed?

April 21, 2013
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Note: I started this post way back when the NCAA men's basketball tournament was going on, but didn't finish it until now. Since the NCAA Men's Basketball Tournament has moved to 64 teams, a 16 seed as never upset a 1 seed. You might be tempted to say ...

## Ordinal data, models with observers

April 21, 2013
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I recently made three posts regarding analysis of ordinal data. A post looking at all methods I could find in R, a post with an additional method and a post using JAGS. Common in all three was using the cheese data, a data set where...

## In three months, I’ll be in Vegas (trying to win against the house)

April 20, 2013
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In fact, I’m going there with my family and some friends, including two probabilists (I mean professionals, I am merely an amateur), with this incredible challenge: will I be able to convince  probabilists to go to play at the Casino? Actually, I also want to study them carefully, to understand how we should play optimally. For example, I hope...

## Prioritizing project stakeholders using social network metrics

April 20, 2013
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Identifying project stakeholders and their requirements is a very important factor in the success of any project. Existing techniques tend to be very ad-hoc. In her PhD thesis Soo Ling Lim came up with a very interesting solution using social network analysis and what is more made her raw data available for download I have

## My new forecasting book is finally finished

April 20, 2013
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My new online forecasting book (written with George Athanasopoulos) is now completed. I previously described it on this blog nearly a year ago. In reality, an online book is never complete, and we plan to continually update it. But it is now at the point where it is suitable for course work, and contains exercises and references. We hope...

## Modeling habitat diversity and species richness

April 20, 2013
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How does habitat diversity affect species richness? Perhaps intuition suggests that habitat diversity increases species richness by facilitating niche or resource partitioning among species. But, for a fixed area, as habitat heterogeneity increases, the area that can be allocated to each habitat type decreases. In a recent paper, Allouche and colleagues (2012) provide a theoretical and empirical treatment...

April 20, 2013
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A new Armadillo release 3.810.0 by Conrad appeared yesterday, and was wrapped up in a new release 0.3.810.0 of RcppArmadillo. Upstream changes bring FFT support as well as more Sparse matrix constructors, and we have an improvement to the sample() fu...

## Basic Mathematical Functions

April 20, 2013
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R can perform the usual mathematical operations, below are the functions:Arithmetic +    - addition-    - subtraction*    - multiplication/    - divisionTrigonometrysin    ...

## Agent-based modeling in R – habitat diversity and species richness

April 20, 2013
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How does habitat diversity affect species richness? Perhaps intuition suggests that habitat diversity increases species richness by facilitating niche or resource partitioning among species. But, for a fixed area, as habitat heterogeneity increases, the area that can be allocated to each habitat type decreases. In a recent paper, Allouche and colleagues (2012) provide a theoretical and empirical treatment...

## Open Source software’s opportunity to reform government

April 19, 2013
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The results from the 2013 Future of Open Source Survey are in — thanks to everyone who contributed by completing the survey. You can read an overview of the results here, or see the detailed breakdowns in the slides at the end of this post. For me, one of the most interesting nuggets from the survey is that a...

## Popup notification from R on Windows

April 19, 2013
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After R is done running a long process, you may need to notify the operator to check the R console and provide the next commands. Without installing any more software or creating any batch files or VBS scripts, here is a simple way to create the popup notice in Windows Continue reading →

## A Course in Data and Computing Fundamentals

April 19, 2013
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Daniel Kaplan and Libby Shoop have developed a one-credit class called Data Computation Fundamentals, which was offered this semester at Macalester College. This course is part of a larger research and teaching effort funded by Howard Hughes Medical Institute (HHMI) to help students … Continue reading →

## Do the same thing to a bunch of variables with lapply()

April 19, 2013
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It is extremely common to have a dataframe containing a bunch of variables, and to do the exact same thing to all of these variables. For instance, lets say we have a dataframe that has a bunch of limb bone measurements of different animals, and we wan...

## Using the SVD to find the needle in the haystack

April 19, 2013
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Sitting with a data set with too many variables? The SVD can be a valuable...