Category: stats (Page 5 of 8)

New Zealand school performance: beyond the headlines

2012-09-24 / Luis

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 the not so distant future to run hierarchical models combining Ministry of Education data with Census/Statistics New Zealand data.

At the same time, there is the temptation to come up with very simple analyses that would make appealing newspaper headlines. I’ll read the data and create a headline and then I’ll move to something that, personally, seems more important. In my previous post I combined the national standards for around 1,000 schools with decile information to create the standards.csv file.
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Gratuitous picture: Lola, Lisbon, Portugal(Photo: Luis).

Mid-August flotsam

2012-08-20 / Luis

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 like a broken record but I’m a bit obsessive about helping people to ‘get’ a topic.

I gave knitr a go while preparing model answers for an assignment. I liked the integration with RStudio and the possibility of using several markup languages. For simple documents Markdown + R is an excellent combination to generate HTML output. However, I wanted to generate PDF output with some math, so I went for latex integration. Jeromy Anglim’s site provides a very good starting point for using knitr to document reproducible analyses.
Simply statistics on Statistics/Statisticians need better marketing.
France in the year 2000: a series of futuristic pictures by Jean-Marc Côté and other artists issued in France in 1899, 1900, 1901 and 1910.
Forty five self-portraits under different psychoactive drugs.
David Smith: “SAS still fighting the open-source wars from last decade” when recurring to FUD.
Nothing like language Schadenfreude: MATLAB can’t read plain text data out of a wet paper bag via Mike Croucher.
Nature Genetics published ‘A mixed-model approach for genome-wide association studies of correlated traits in structured populations’, where variance components were estimated using ASReml. Via Christophe Lalanne.

Gratuitous picture: Lola, Lisbon, Portugal.

Gratuitous picture: Detail of Mosteiro dos Jerónimos, Belém, Lisboa, Portugal (Photo: Luis).

Split-plot 2: let’s throw in some spatial effects

2012-07-31 / Luis

Disappeared for a while collecting frequent flyer points. In the process I ‘discovered’ that I live in the middle of nowhere, as it took me 36 hours to reach my conference destination (Estoril, Portugal) through Christchurch, Sydney, Bangkok, Dubai, Madrid and Lisbon.

Where was I? Showing how split-plots look like under the bonnet (hood for you US readers). Yates presented a nice diagram of his oats data set in the paper, so we have the spatial location of each data point which permits us playing with within-trial spatial trends.
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Split-plot 1: How does a linear mixed model look like?

2012-06-25 / Luis

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 me.

Additionally, I have made a few posts discussing linear mixed models using several different packages to fit them. At no point I have shown what are the calculations behind the scenes. So, I decided to combine my frustration and an explanation to myself in a couple of posts. This is number one and the follow up is Split-plot 2: let’s throw in some spatial effects.
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Having a look at different—statistical—horses

R, Julia and genome wide selection

2012-04-25 / Luis

— “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 to assume that I was the proverbial old dog unwilling (or was it unable?) to learn new tricks. (Incidentally, with friends like this who needs enemies? Hi, Gus.)
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