Category: rblogs (Page 10 of 22)

Suicide statistics and the Christchurch earthquake

2012-09-06 / Luis

Suicide is a tragic and complex problem. This week New Zealand’s Chief Coroner released its annual statistics on suicide, which come with several tables and figures. One of those figures refers to monthly suicides in the Christchurch region (where I live) and comes with an interesting comment:

Suicides in the Christchurch region (Timaru to Kaikoura) have risen from 67 (2010/11) to 81 (2011/12). The average number of suicides per year for this region over the past four years is 74. The figure of 67 deaths last year reflected the drop in suicides post-earthquake. The phenomenon of a drop in the suicide rate after a large scale crisis event, such as a natural disaster, has been observed elsewhere. [my emphasis]

Gratuitous picture: horse race in the beach (Photo: Luis).

m x n matrix with randomly assigned 0/1

2012-08-29 / Luis

Today Scott Chamberlain tweeted asking for a better/faster solution to building an m x n matrix with randomly assigned 0/1. He already had a working version:

r <- 1000
c <- 1000
m0 <- matrix(0, r, c)
apply(m0, c(1,2), function(x) sample(c(0,1),1))

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: Cubist apartments not in Norway (Photo: Luis).

INLA: Bayes goes to Norway

2012-08-16 / Luis

INLA is not the Norwegian answer to ABBA; that would probably be a-ha. INLA is the answer to ‘Why do I have enough time to cook a three-course meal while running MCMC analyses?”.

Integrated Nested Laplace Approximations (INLA) is based on direct numerical integration (rather than simulation as in MCMC) which, according to people ‘in the know’, allows:

the estimation of marginal posteriors for all parameters,
marginal posteriors for each random effect and
estimation of the posterior for linear combinations of random effects.

Gratuitous image: Tree spread on metal frame to provide shade in a plaza, Lisbon, Portugal. Some days I would love to have a coffee there without computer, just watching the world pass by. (Photo: Luis).

Careless comparison bites back (again)

2012-08-07 / Luis

When running stats labs I like to allocate a slightly different subset of data to each student, which acts as an incentive for people to do their own work (rather than copying the same results from a fellow student). We also need to be able to replicate the results when marking, so we need a record of exactly which observations were dropped to create a particular data set. I have done this in a variety of ways, but this time I opted for code that looked like:

setwd('~/Dropbox/teaching/stat202-2012')

biom <- read.csv('biom2012.csv', header = TRUE)
drops <- read.csv('lab4-dels.csv', header = TRUE)

# Use here your OWN student code
my.drop <- subset(drops, student.code == 'mjl159')
my.data <- subset(biom, !(id %in% my.drop))