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 →
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...
(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...
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
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