by Joseph Rickert When talking with data scientists and analysts — who are working with large scale data analytics platforms such as Hadoop — about the best way to do some sophisticated modeling task it is not uncommon for someone to say, "We have all of the data. Why not just use it all?" This sort of comment often...
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What’s New in 6.2: Stepwise Regression for Big Data
by Thomas Dinsmore This is the third in a series of posts highlighting new features in Revolution R Enterprise Release 6.2, which is scheduled for General Availability April 22. This week's post features our new Stepwise Regression capability. The Stepwise process starts with a specified model and then sequentially adds into or removes from the model the variable that...
Does It Make Sense to Segment Using Individual Estimates from a Hierarchical Bayes Choice Model?
(This article was first published on Engaging Market Research, and kindly contributed to R-bloggers) I raise this question because we see calls for running segmentation with individual estimates from hierarchical Bayes choice models without any mention of the possible complications that might accompany such an approach. Actually, all the calls seem to be from those using MaxDiff to analyze the data from...
Using Norms to Understand Linear Regression
Introduction In my last post, I described how we can derive modes, medians and means as three natural solutions to the problem of summarizing a list of numbers, \((x_1, x_2, \ldots, x_n)\), using a single number, \(s\). In particular, we measured the quality of different potential summaries in three different ways, which led us to
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. 
Modes, Medians and Means: A Unifying Perspective
Introduction / Warning Any traditional introductory statistics course will teach students the definitions of modes, medians and means. But, because introductory courses can’t assume that students have much mathematical maturity, the close relationship between these three summary statistics can’t be made clear. This post tries to remedy that situation by making it clear that all
Plotting lm and glm models with ggplot #rstats
Update I followed the advice from Tim’s comment and changed the scaling in the sjPlotOdds-function to logarithmic scaling. The screenshots below showing the plotted glm’s have been updated. Summary In this posting I will show how to plot results from … Weiterlesen →
Animating neural networks from the nnet package
My research has allowed me to implement techniques for visualizing multivariate models in R and I wanted to share some additional techniques I’ve developed, in addition to my previous post. For example, I think a primary obstacle towards developing a useful neural network model is an under-appreciation of the effects model parameters have on model 
What’s New in 6.2: Open Source R 2.15.3
by Thomas Dinsmore Last week, Revolution Analytics released the Limited Availability edition of Revolution R Enterprise Release 6.2. Interest in this new release is high, and we're very pleased with user response. Over the next several weeks, I will share more detailed information about the capabilities included in this new release. Revolution R Enterprise Release 6.2 supports open source...
Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)
Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
