# Posts Tagged ‘ stats ’

## The unicorn problem

October 13, 2012
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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

## New Zealand school performance: beyond the headlines

September 24, 2012
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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 →

## Mid-August flotsam

August 20, 2012
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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 →

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

June 24, 2012
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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 →

## R, Julia and genome wide selection

April 24, 2012
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— “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 →

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

March 9, 2012
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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...

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

March 9, 2012
By

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...

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

March 9, 2012
By

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...

## 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...

## Recovering Marginal Effects and Standard Errors from Interaction Terms in R

March 5, 2012
By

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...