Chapter 4 of Using R for Introductory Statistics gets us started working with multivariate data. The question is: what are the relationships among the variables? One way to go about answering it is by pairwise comparison of variables. Another techniq...
See also NEURAL NETWORKS.
In this past June's issue of R journal, the 'neuralnet' package was introduced. I had recently been familiar with utilizing neural networks via the 'nnet' package (see my post on Data Mining in A Nutshell) but I find the neuralnet package more useful because it will allow you to actually plot...
Just two hours ago, Professor John Fox has announced on the R-help mailing list of a new (second) edition to his book “An R and S Plus Companion to Applied Regression”, now title . “An R Companion to Applied Regression, Second Edition”. John Fox is (very) well known in the R community for many contributions to R, including the...
Describing multivariate data is not easy. Especially, if you think that statisticians have not developed any new tools after the ANOVA and principal component analysis (PCA). For social and experimental scientists the most important new technique are structural equation models that combine measurement models (that substitute reliability analysis and PCA) and structural models (that substitute
J-Doug at Rational Pastime has some cool posts looking at umpire strike zones at his site (and cross-posted at Beyond the Boxscore). I was curious about this issue as well with some work I've been doing here in the office (which I'll refrain from talk...
Recently I've been doing a lot of work with predictive models using logistic regression. Logistic regression is great for determing probable outcomes of a independent binary target variable. R is a great tool for accomplishing this task.&nb...
Recently I've been doing a lot of work with predictive models using logistic regression. Logistic regression is great for determing probable outcomes of a independent binary target variable. R is a great tool for accomplishing this task.&nb...
Last week, I received a box of books from the International Statistical Review, for reviewing them. I thus grabbed the one whose title was most appealing to me, namely Bayesian Model Selection and Statistical Modeling by Tomohiro Ando. I am indeed interested in both the nature of testing hypotheses or more accurately of assessing models, 
I was recently reading a bit about logistic regression in a book on hierarchical/multilevel modeling when I first learned about the "divide by 4 rule" for quickly interpreting coefficients in a logistic regression model in terms of the predicted probabilities of the outcome. The idea is pretty simple. The logistic curve (predicted probabilities) is steepest at the center where...
In examples 8.15 and 8.16 we considered Firth logistic regression and exact logistic regression as ways around the problem of separation, often encountered in logistic regression. (Re-cap: Separation happens when all the observations in a category sha...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
