Publications by Bluecology blog
Choosing R packages for mixed effects modelling based on the car you drive
Choosing R packages for mixed effects modelling based on the car you drive There are many roads you can take to fit a mixed effects model (sometimes termed hierarchical models) in R. There are numerous packages that each deploy different engines to fit mixed effects models. In this driveability review, I look at some of the dominant packages for ...
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Visualising uncertainty in time-series using animations
Visualising uncertainty in time-series using animations I was reading an example of visualising election outcome uncertainty using a wobbly needle and wondered, could animating uncertainty help communicate for the kind of work I do on population modelling? One challenge when plotting uncertainty intervals for population trends is that confidence...
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Quantifying the magnitude of a population decline with Bayesian time-series modelling
Quantifying the magnitude of a population decline with Bayesian time-series modelling Population abundances tend to vary year to year. This variation can make it make it hard detect a change and hard to quantify exactly what that change is. Bayesian time-series analysis can help us quantify a decline and put uncertainty bounds on it too. Here I w...
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Smoothing a time-series with a Bayesian model
Smoothing a time-series with a Bayesian model Recently I looked at fitting a smoother to a time-series using Bayesian modelling. Now I will look at how you can control the smoothness by using more or less informative priors on the precision (1/variance) of the random effect. We will use the same dataset as the last post. To control the priors for...
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What does under or over-dispersion look like?
What does under or over-dispersion look like? One issue that often comes up in analysis with linear models is under or over dispersion. For instance, if you are fitting a linear regression model, you are assuming the residuals (difference between the line of best fit and the data-points) are normally distributed. Residuals are said to be overdisp...
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Data visuals notes for my talks in 2017
Data visuals 2017 Supplementary notes for CJ Brown’s talks on dataviz in 2017 for Griffith University’s honours students and the UQ Winterschool in Bioinformatics. Skip to the quiz Structure of this talk Tools for dataviz Eleven principles for effective dataviz Breaking the rules Change the world The ideal dataviz tool Sensible and clean d...
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Data visuals notes for my talks in 2017
Data visuals: notes for my talks in 2017 Supplementary notes for CJ Brown’s talks on dataviz in 2017 for Griffith University’s honours students and the UQ Winterschool in Bioinformatics. Skip to the quiz Visualsing sexual dimorphism in elephant seals I picked this example to demonstrate a simple barchart for representing the sizes of differen...
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What analysis program do conservation scientists use?
International Congress for Conservation Biology: What analysis program do conservation scientists use? With the International Congress for Conservation Biology starting 23rd July I was wondering, what analysis programs are most commonly used by conservation scientists? And, what do they use for spatial analysis and mapping? To find out, if you ar...
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What are people saying about ICCB2017 on Twitter?
What are people saying about International Congress for Conservation Biology 2017 on Twitter? Here’s a brief analysis of tweets from the International Congress for Conservation Biology to date. The conference started on the starting 23rd July I was curious to see what people are talking about there and who is doing the talking on twitter. As of...
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Memorable dataviz with the R program, talk awarded people’s choice prize
“Memorable dataviz with the R program” awarded people’s choice prize For the past two years Dr Nick Hamilton has invited me to give a talk on creating data visuals with the R program at the wonderful UQ Winterschool in Bioinformatics. This year I was lucky enough to be awarded a prize for my talk – best speaker from a mid-career presenter...
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