Monthly Archives: October 2012

Ordinal football

October 1, 2012
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Ordinal football

I've had a quick look at this article on R-bloggers $-$ I don't think I've followed the whole exchange, but I believe they have discussed what models should/could be applied to estimate football scores (specifically, in this case they are using the Dut...

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Designing real-world 3-D objects with R

October 1, 2012
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Designing real-world 3-D objects with R

The Maker Movement has led to the production of open-source 3-D printers and other manufacturing machines that allow hobbyists to design, create and produce real-world objects affordably. Now R user Ian Walker, in a post at the Psychological Statistics blog, shows how to use the R language to transform 3-D surfaces into real-world physical objects with a 3-D printer....

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A Brief Tip on Generating Fractional Factorial Designs in R

October 1, 2012
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A number of marketing researchers use the orthoplan procedure in SPSS to generate fractional factorial designs.  It is not surprising, then, that I received a number of questions concerning the recent article in the Journal of Statistical Software by Hideo Aizaki on “Basic Functions for Supporting an Implementation of Choice Experiments in R.”  To summarize their issues,...

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Example 10.4: Multiple comparisons and confidence limits

October 1, 2012
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Example 10.4: Multiple comparisons and confidence limits

A colleague is a devotee of confidence intervals. To him, the CI have the magical property that they are immune to the multiple comparison problem-- in other words, he feels its OK to look at a bunch of 95% CI and focus on the ones that appear to exclude the null. This though...

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When Russell 2000 is Low Vol

October 1, 2012
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When Russell 2000 is Low Vol

Continuing in my exploration of the Russell 2000 (Russell 2000 Softail Fat Boy), I thought I would try to approach the topic with a low volatility paradox mindset.  Since 2005, beta of the Russell 2000 compared to the S&P 500 has exceeded 1.2 ...

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Level fit summaries can be tricky in R

October 1, 2012
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Level fit summaries can be tricky in R

Model level fit summaries can be tricky in R. A quick read of model fit summary data for factor levels can be misleading. We describe the issue and demonstrate techniques for dealing with them.When modeling you often encounter what are commonly called categorical variables, which are called factors in R. Possible values of categorical variables Related posts:

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How does my computer know what language I am using? – An approach of statistical learning (Language, Computer Science)

October 1, 2012
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How does my computer know what language I am using? – An approach of statistical learning (Language, Computer Science)

Quick and Easy Subsetting

October 1, 2012
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Quick and Easy Subsetting

Public health datasets can be enormous and difficult to look at.  Often it is great to be able to only look at specific parts of the dataset, or to only run analysis on a specific part of a dataset.  There are two ways that you can subset a d...

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Rcpp 0.9.14

October 1, 2012
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Another release of Rcpp has just appeared on CRAN and was just uploaded to Debian. It addresses yet another issue we had on OS X and should hopefully put the build issues to rest. Three new (vectorized) sugar functions were added, along with some ne...

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Making random, equally-sized partitions

October 1, 2012
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Making random, equally-sized partitions

Sometimes, as with cross-validation, one needs to generate k partitions, each with an equal number of observations. There are probably an infinite number of ways this could be done in R, but the Gist below illustrates one way to do it in four lines, w...

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