1537 search results for "Regression"

GEE QIC update

November 15, 2012
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GEE QIC update

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 →

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Webinar Tomorrow: Big Data Trees and Hadoop Connection in Revolution R Enterprise 6.1

November 14, 2012
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Tomorrow at 9AM Pacific, Revolution Analytics VP of Product Development Sue Ranney will introduce two key Big Data features of the new Revolution R Enterprise 6.1. Now you can train classification and regression trees on data sets of unlimited size, quickly and using the resources of multiple processors and clusters. (This white paper describes our implementation of tree models...

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How the Democrats may have won the House, but lost the seats

November 14, 2012
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How the Democrats may have won the House, but lost the seats

  The 2012 election is over and in the books. A few very close races remain to be officially decided, but for the most part everything has settled down over the last week. By all accounts it was a very good night for the Democrats, with wins in the presidency, senate and state houses. They also performed

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Trees with the rpart package

November 13, 2012
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Trees with the rpart package

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...

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Benchmarking bigglm

November 13, 2012
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By Joseph Rickert In a recent blog post, David Smith reported on a talk that Steve Yun and I gave at STRATA in NYC about building and benchmarking Poisson GLM models on various platforms. The results presented showed that the rxGlm function from Revolution Analytics’ RevoScaleR package running on a five node cluster outperformed a Map Reduce/ Hadoop implementation...

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analyze the consumer expenditure survey (ce) with r

November 13, 2012
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the consumer expenditure survey (ce) is the primo data source to understand how americans spend money.  participating households keep a running diary about every little purchase over the year.  those diaries are then summed up into precise expenditure categories.  how else are you gonna know that the average american household spent $34 (±2) on bacon, $826 (±17) on cellular...

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The guts of a statistical factor model

November 12, 2012
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The guts of a statistical factor model

Specifics of statistical factor models and of a particular implementation of them. Previously Posts that are background for this one include: Three things factor models do Factor models of variance in finance The BurStFin R package The quality of variance matrix estimation The problem Someone asked me some questions about the statistical factor model in … Continue reading...

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Exploring GAMs with Rosemary Hartman

November 9, 2012
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Exploring GAMs with Rosemary Hartman

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...

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Terrain effects on SUHI estimates

November 9, 2012
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Terrain effects on SUHI estimates

Introduction Zhang and Imhoff (2010)  pdf here utilized NLCD impervious surface area (ISA), Olson biomes, and MODIS Land Surface temperature (LST) to estimate the magnitude of UHI in large cities across the US.  Peng  employed a   similar approach in studying 419 large cities ( population greater than 1m ) around world. Peng’s work suggests a limit or

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Data types part 2: Using classes to your advantage

November 8, 2012
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Data types part 2: Using classes to your advantage

Last week I talked about objects including scalars, vectors, matrices, dataframes, and lists.  This post will show you how to use the objects (and their corresponding classes) you create in R to your advantage.First off, it's important to remember...

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