In my last post I said that I would try to investigate the question of who actually does want a casino, and whether place of residence is a factor in where they want the casino to be built. So, here … Continue reading →
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Which Torontonians Want a Casino? Survey Analysis Part 2
Eigen-analysis of Linear Model Behavior in R
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 →
Volatility Regimes: Part 1
This is a ‘do over’ of a project I started while at my former employer in the fall of 2012. I presented part 1 of this
framework at the FX Invest West Coast conference on September 11, 2012. I have made some changes and expanded the
analysis since then. Part 2 is complete and will follow this post in the week...
Using the SVD to find the needle in the haystack
(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...
Reconstructing Principal Component Analysis Matrix
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 →
Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional Logistic Regressions
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. 
Violin plots and regional income distribution
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...
Evaluation of Orthogonal Signal Correction for PLS modeling (OSC-PLS and OPLS)
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 
reading raster data using library(parallel)
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 →
PCA to PLS modeling analysis strategy for WIDE DATA
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 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
