This post is actually about replicating the figures in Otto and Day: A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution. The figures I’m interested in for this post are Figures 9.1 and 9.2 in the chapter ‘General Solutions … Continue reading →
(This article was first published on G-Forge » R, and kindly contributed to R-bloggers) Sitting with a data set with too many variables? The SVD can be a valuable tool when you’re trying to sift through a large group of continuos variables. The image is CC by Jonas in China. It can feel like a daunting task when you...
PCA is widely used method for finding patterns in high-dimensional data. Whether you use it to compress large matrix or to remove one of the principal components in biological datasets, you’ll end up with the task of performing series of … Continue reading →
Next topic on logistic regression: the exact and the conditional logistic regressions. Exact logistic regression When the dataset is very small or severely unbalanced, maximum likelihood estimates of coefficients may be biased. An alternative is to use exact logistic regression, available in R with the elrm package. Its syntax is based on an events/trials formulation. 
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...
Partial least squares projection to latent structures or PLS is one of my favorite modeling algorithms. PLS is an optimal algorithm for predictive modeling using wide data or data with rows << variables. While there is s a wealth of literature regarding the application of PLS to various tasks, I find it especially useful for biological 
Recently, I have been doing some analysis for a project I am involved in. In particular, I was interested what role pacific sea surface temperatures play with regard to rainfall in East Africa. I spare you the details as I … Continue reading →
Working with wide data is already hard enough, add to this row outliers and things can get murky fast. Here is an example of an anlysis of a wide data set, 24 rows x 84 columns. Using imDEV, written in R, to calculate and visualize a principal components analysis (PCA) on this data set. We find that 
For some reason, I feel like have much better control with plot.xts function from the xtsExtra package described here over some of the other more refined R graphical packages. Maybe, it is just my simple mind, but recently I wanted to shade holding per...
It’s survey analysis season for me at work! When analyzing survey data, the one kind of analysis I have realized that I’m not used to doing is finding patterns in binary data. In other words, if I have a question … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
