We had a great turnout yesterday for our Zero to R Hero workshop at the Quebec Centre for Biodiversity Science. We went from the absolute basics of the command line, to the intricacies of importing data, and finally we had a look at plotting using ggplot2. We didn’t have time to get to this extra module 
Update I followed the advice from Tim’s comment and changed the scaling in the sjPlotOdds-function to logarithmic scaling. The screenshots below showing the plotted glm’s have been updated. Summary In this posting I will show how to plot results from … Weiterlesen →
Why I used html5 for my today’s talk? My last presentation was in html5. This time I wanted to do my slides in something new. I prepared first few slides in Jessyink. Then I got to know that my friend … Continue reading →
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Just a short post to celebrate that I learned today how incredibly easy it is to make a heatmap of correlations with ggplot2 (and reshape2, of course). So, what is going on in that short passage? cor makes a correlation matrix with all the pairwise correlations between variables (twice; plus a diagonal of ones). melt 
It is time for the NCAA Basketball Tournament. Sixty-four teams dream big (er…I mean 68…well actually by now, 64) and schools like Iona and Florida Gulf Coast University (go Eagles!) are hoping that Robert Morris astounding victory in the N.I.T. … Continue reading →
While preparing my slides for statistical graphics, a plot really caught my eye when I was playing around with the data.
I started off by plotting the time seriesof GNI per capita by country, and as expected it got quite messy and...
tl;dr: Play with the app here
When you live in Paris, chances are you are (home or work) very close to a metro station, so when you want to meet with some friends, you usually end up picking another metro station as a meeting point. Yet, finding the optimal place to meet can easily become a complex problem considering...
My research has allowed me to implement techniques for visualizing multivariate models in R and I wanted to share some additional techniques I’ve developed, in addition to my previous post. For example, I think a primary obstacle towards developing a useful neural network model is an under-appreciation of the effects model parameters have on model 
A great. recent enhancement for the Shiny App is the ability to upload local files. Now, in addition to users being able to interact with data provided on the host e.g. Soccer Tables or via the web, Wikipedia Search Rates they can use apps to view and analyse their own data I have knocked up
Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
