Posts Tagged ‘ stats ’

The unicorn problem

October 13, 2012
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The unicorn problem

Let’s say your goal is to observe all known species in a particular biological category. Once a week you go out and collect specimens to identify, or maybe you just bring your binoculars to do some spotting. How long will it take you to cross off every species on your list? I’ve been wondering this

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New Zealand school performance: beyond the headlines

September 24, 2012
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New Zealand school performance: beyond the headlines

I like the idea of having data on school performance, not to directly rank schools—hard, to say the least, at this stage—but because we can start having a look at the factors influencing test results. I imagine the opportunity in … Continue reading →

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Mid-August flotsam

August 20, 2012
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Mid-August flotsam

Reached mid-semester point, with quite a few new lectures to prepare. Nothing extremely complicated but, as always, the tricky part is finding a way to make it meaningful and memorable. Sometimes, and this is one of those times, I sound … Continue reading →

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Split-plot 1: How does a linear mixed model look like?

June 24, 2012
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Split-plot 1: How does a linear mixed model look like?

I like statistics and I struggle with statistics. Often times I get frustrated when I don’t understand and I really struggled to make sense of Krushke’s Bayesian analysis of a split-plot, particularly because ‘it didn’t look like’ a split-plot to … Continue reading →

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R, Julia and genome wide selection

April 24, 2012
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R, Julia and genome wide selection

— “You are a pussy” emailed my friend. — “Sensu cat?” I replied. — “No. Sensu chicken” blurbed my now ex-friend. What was this about? He read my post on R, Julia and the shiny new thing, which prompted him … Continue reading →

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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

March 9, 2012
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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

In the last post I presented a function for recovering marginal effects of interaction terms. Here we implement the function with simulated data and plot the results using ggplot2.       #---Simulate Data and Fit a linear model with an...

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Recovering Marginal Effects and Standard Errors from Interaction Terms in R

March 5, 2012
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When I fit models with interactions, I often want to recover not only the interaction effect but also the marginal effect (the main effect + the interaction) and of course the standard errors. There are a couple of ways to do this in R but I ended writ...

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Functional ANOVA using INLA

January 13, 2012
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Functional ANOVA using INLA

Ramsay and Silverman’s Functional Data Analysis is a tremendously useful book that deserves to be more widely known. It’s full of ideas of neat things one can do when part of a dataset can be viewed as a set of

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Iowa: Was the fix in? (a statistical analysis of the results)

January 4, 2012
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Iowa: Was the fix in? (a statistical analysis of the results)

Summary/TL;DR Either the first precincts to report were widely unrepresentative of Iowa as a whole, or something screwy happened. Background Yesterday was the first primary for the 2012 U.S. presidential elections. When I logged off the internet last night, the results in Iowa showed a dead heat between Ron Paul, Mitt Romney, and Rick Santorum.

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Tall big data, wide big data

December 12, 2011
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After attending two one-day workshops last week I spent most days paying attention to (well, at least listening to) presentations in this biostatistics conference. Most presenters were R users—although Genstat, Matlab and SAS fans were also present and not one … Continue reading →

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