Here is improved code for calculating QIC from geeglm in geepack in R (original post). Let me know how it works. I haven’t tested it much, but is seems that QIC may select overparameterized models. In the code below, I … Continue reading →

What are trees? Trees (also called decision trees, recursive partitioning) are a simple yet powerful tool in predictive statistics. The idea is to split the covariable space into many partitions and to fit a constant model of the response variable in each partition. In case of regression, the mean...

Today at Davis R Users’ Group, Rosemary Hartman took us through her work in progress fitting general additive models to organism presence/absence data. Below is her presentation and script. You can get the original script and data here Also, check the comments below for some discussion of other options for this type of analysis, such as...

I recently had a task to take a look at some assessment (audit) data. I was assuming, rather hoping for data with a normal distribution and thought it would be a quick case of Pearson correlation between two columns: "Duration" and "Score". Just conjecture at this point as I did not understand what the assessment process

As promised, the source distribution for R 2.15.2 is now available for download from the master CRAN repository. (Binary distributions for Windows, MacOS and Linux will be available from the CRAN mirror network in the coming days.) This latest point-update — codenamed "Trick or Treat" — improves the performance of the R engine and adds a few minor but...

I was creating a dataset this last week in which I had to partition the observed responses to show how the ANOVA model partitions the variability. I had the observed Y (in this case prices for 113 bottles of wine), … Continue reading →

This is a quick follow-up to my previous post about Color Palettes in RGB Space. Achim Zeileis had commented that, perhaps, it would be more informative to evaluate the color palettes in HCL (polar LUV) space, as that spectrum more accurately describes how humans perceive color. Perhaps more clear trends would emerge in HCL space,

After development of predictive model for transactional product revenue -(Product revenue prediction with R – part 1), we can further improvise the model prediction by modifications in the model. In this post, we will see what are the steps required for model improvement. With the help of a set of model summary parameters, the data

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