3575 search results for "gis"

Better modelling and visualisation of newspaper count data

February 19, 2013
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Better modelling and visualisation of newspaper count data

<!-- Styles for R syntax highlighter In this post I outline how count data may be modelled using a negative binomial distribution in order to more accurately present trends in time series count data than using linear methods. I also show how to...

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Sketches Around Twitter Followers

February 19, 2013
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Sketches Around Twitter Followers

I’ve been doodling… Following a query about the possible purchase of Twitter followers for various public figure accounts (I need to get my head round what the problem is with that exactly?!), I thought I’d have a quick look at some stats around follower groupings… I started off with a data grab, pulling down the

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New Rcpp master class scheduled for New York

February 18, 2013
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A new Rcpp master class is scheduled for March 9 in New York. The format will an updated version of the one-day workshops I have given at the University of Rochester in 2010, in San Franciso in 2011 (organised by Revolution Analytics) and at the UseR...

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Data fishing: R and XML part 3

February 18, 2013
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Data fishing: R and XML part 3

I’ve recently posted two blogs about gathering data from web pages using functions in R. Both examples showed how we can create our own custom functions to gather data about Minnesota lakes from the Lakefinder website. The first post was an example showing the use of R to create our own custom functions to get

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Predictors, responses and residuals: What really needs to be normally distributed?

February 18, 2013
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Predictors, responses and residuals: What really needs to be normally distributed?

Introduction Many scientists are concerned about normality or non-normality of variables in statistical analyses. The following and similar sentiments are often expressed, published or taught: "If you want to do statistics, then everything needs to be normally distributed." "We normalized…Read more →

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Run production, one team at a time

February 17, 2013
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In a previous post, I used R to process data from the Lahman database to calculate index values that compare a team's run production to the league average for that year.  For the purpose of that exercise, I started the sequence at 1947, but for what follows I re-ran the code with the time period...

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A look at strucchange and segmented

February 17, 2013
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A look at strucchange and segmented

After last week's post it was commented that strucchange and segmented would be more suitable for my purpose. I had a look at both. Strucchange can find a jump in a time series, which was what I was looking for. In contrast segmented is more suitable f...

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Finding outliers in numerical data

Finding outliers in numerical data

One of the topics emphasized in Exploring Data in Engineering, the Sciences and Medicine is the damage outliers can do to traditional data characterizations.  Consequently, one of the procedures to be included in the ExploringData package is FindOutliers, described in this post.  Given a vector of numeric values, this procedure supports four different methods for identifying possible outliers.Before...

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Video: Data Mining with R

February 15, 2013
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Yesterday's Introduction to R for Data Mining webinar was a record setter, with more than 2000 registrants and more than 700 attending the live session presented by Joe Rickert. If you missed it, I've embedded the video replay below, and Joe's slides (with links to many useful resources) are also available. During the webinar, Joe demoed several examples of...

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Clustering Loss Development Factors

February 15, 2013
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Clustering Loss Development Factors

  Anytime I get a new hammer, I waste no time in trying to find something to bash with it. Prior to last year, I wouldn’t have known what a cluster was, other than the first half of a slang term used to describe a poor decision-making process. Now I’ve seen it in action a

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