In a previous post I explained how to create all possible combinations of the levels of two factors using expand.grid(). Another use for this function is to create a regular grid for two variables to create a levelplot or a … Continue reading →

The previous posts, part 1 and part 2, detailed the procedure to successfully import the data and transform the data so that we can extract some useful information from them. Now it's time to get our hands dirty with some predictive modelling. The dependent variable here is a binary variable taking values "0" and "1", indicating whether the customer...

Sometimes a student may use a self explained chart, instead of a boring table for showing outcomes in a research paper. Yet, graphs are efficient in showing the broad picture of an issue and also for present results. In political science, you can getting into this topic reading Kastellec and Leoni (2007), for instance. I

I am running GEE logistic regression model for my fetal loss paper. As usual, I compare results between Stata and R and make sure they are consistent. To my surprise, the models assuming independent correlation structure give similar results but the mo...

Title: Bayesian Computation with RAuthor(s): Jim AlbertPublisher/Date: Springer/2009Statistics level: High Programming level: Low Overall recommendation: Recommended Bayesian Computation with R focuses primarily on providing the reader with a basic understanding of Bayesian thinking and the relevant analytic tools included in R. It does not explore either of those areas in detail, though it does hit The post Bayesian...

Multivariate ordination methods are commonly used in ecology to investigate patterns in species composition in space or time. Constrained ordination methods such as redundancy analysis (RDA) and canonical correspondence analysis (CCA) are effectively just multiple regressions, but we lack the … Continue reading →

Multivariate ordination methods are commonly used in ecology to investigate patterns in species composition in space or time. Constrained ordination methods such as redundancy analysis (RDA) and canonical correspondence analysis (CCA) are effectively just multiple regressions, but we lack the parametric theory to adequately test the statistical significance of terms in the model. Other techniques likewise lack the appropriate...