the panel study of income dynamics (psid) is a one-trick pony. better than anything else out there, this survey allows you to answer the question, "where are they now?" after tracking the same nationally-representative cohort of americans (...

the panel study of income dynamics (psid) is a one-trick pony. better than anything else out there, this survey allows you to answer the question, "where are they now?" after tracking the same nationally-representative cohort of americans (...

I’ve been getting emails asking questions about my upcoming course on Forecasting using R. Here are some answers. Do I need to use the Revolution Enterprise version of R, or can I use open-source R? Open source R is fine. Revolution Analytics is organizing the course, but there is no requirement to use their software. I will be using...

If you haven't already registered, don't miss tomorrow's webinar presented by Cloudera's Director of Product Strategy, Jairam Ranganathan and Michele Chambers, Chief Strategy Officer at Revolution Analytics. This will be a great opportunity to learn how R and CDH (Cloudera's Hadoop distribution) work together with the forthcoming Revolution R Enterprise 7. Here's what they will be talking about: The...

We are pleased to announce details of the next New Jersey R meeting; this is a free event open to anyone using or interested in using R. Venue: Hilton Woodbridge, 120 Wood Avenue South, Iselin, NJ 08830 Time: 6.30 pm – 10pm Presentations (from 7pm) • Using knitr to create reports – Adam Rich, Beazley Group • Using R...

How does it work? Simple! Once you build your model in R using any of the PMML supported model types, pass the model object as an input parameter to the pmml package as shown in the figure below.The pmml package offers export for a variety of model types, including: • ksvm (kernlab): Support Vector Machines • nnet: Neural Networks • rpart: C&RT Decision Trees • lm & glm (stats):...

There can never be too many examples for transforming data with R. So, here is another example of reshaping a data.frame into a matrix.Here I have a data frame that shows incremental claim payments over time for different loss occurrence (origin) years...

I admit it, the title sounds weird. The problem I want to address this evening is related to the use of the stepwise procedure on a regression model, and to discuss the use of categorical variables (and possible misinterpreations). Consider the following dataset > db = read.table("http://freakonometrics.free.fr/db2.txt",header=TRUE,sep=";") First, let us change the reference in our categorical variable (just to...

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