2226 search results for "regression"

The R Backpages 2

November 27, 2013
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The R Backpages 2

by Joseph Rickert In this roundup of R-related news: Domino enables data science collaboration; Plotly adds an R graphics gallery; Revolution Analytics R user group sponsorship applications are open; and Quandl adds new data sets. San Francisco startup takes on collaborative Data Science Domino, a San Francisco based startup, is inviting users to sign up to beta test its...

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The little non-informative prior that could (be informative)

November 26, 2013
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The little non-informative prior that could (be informative)

Christian Robert reviewed on line a paper that was critical of non-informative priors. Among the points that were discussed by him and other contributors (e.g. Keith O’Rourke), was the issue of induced priors, i.e. priors which arise from a transformation of original parameters, or of observables. I found this exchange interesting because I did something

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Bootstrapping for Propensity Score Analysis

November 26, 2013
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Bootstrapping for Propensity Score Analysis

I am happy to announce that version 1.0 of the PSAboot package has been released to CRAN. This package implements bootstrapping for propensity score analysis. This deviates from typical implementations such as boot in that it allows for separate sampling specifications for treatment and control units. For example, in the case where the ratio of treatment-to-control units is...

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Getting Started with Mixed Effect Models in R

November 25, 2013
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Getting Started with Multilevel Modeling in R Getting Started with Multilevel Modeling in R Jared E. Knowles Introduction Analysts dealing with grouped data and complex hierarchical structures in their data ranging from measurements nested within participants, to counties nested within states or students nested within classrooms often find themselves...

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Book Review: Applied Predictive Modeling by Max Kuhn and Kjell Johnson

November 24, 2013
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This is a gem of a book.From the introduction: We intend this work to be a practitioner’s guide to the predictive modeling process and a place where one can come to learn about the approach and to gain intuition about the many commonly used and modern, powerful models. …it was our goal to be as hands-on as possible, enabling the readers...

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R and Bayesian Statistics

November 21, 2013
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R and Bayesian Statistics

by Joseph Rickert Drew Linzer, the Bayesian statistician who attracted considerable attention last year with his spot-on, R-based forecast of the 2012 presidential election, recently gave a tutorial on Bayesian statistics to the Bay Area useR Group (BARUG). Drew covered quite a bit of ground running R code that showed how to make use of WinBugs, JAGS and Stan,...

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How to format plots for publication using ggplot2 (with some help from Inkscape)

November 20, 2013
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How to format plots for publication using ggplot2 (with some help from Inkscape)

The following is the code from a presentation made by Rosemary Hartman to the Davis R Users’ Group. I’ve run the code through the spin function in knitr to produce this post. Download the script to walk through here. First, make your plot. I am going to use the data already in R about sleep habits...

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Predicting optimal of iterations and completion time for GBM

November 20, 2013
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Predicting optimal of iterations and completion time for GBM

When choosing the hyperparameters for Generalized Boosted Regression Models, two important choices are shrinkage and the number of trees. Generally a smaller shrinkage with more trees produces a better model, but the modeling time significantly increases. Building a model with too many trees that are heavily cut back by cross validation wastes time, while building a model...

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Art of Statistical Inference

November 20, 2013
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Art of Statistical Inference

(This article was first published on MATHEMATICS IN MEDICINE, and kindly contributed to R-bloggers) Art of Statistical Inference Art of Statistical Inference This post was written by me a few years ago, when I started learning the art and science of data analysis. It will be a good starter for the amateur data analysts. Introduction What is statistics? There...

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On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

November 19, 2013
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On the use of marginal posteriors in marginal likelihood estimation via importance-sampling

Perrakis, Ntzoufras, and Tsionas just arXived a paper on marginal likelihood (evidence) approximation (with the above title). The idea behind the paper is to base importance sampling for the evidence on simulations from the product of the (block) marginal posterior distributions. Those simulations can be directly derived from an MCMC output by randomly permuting the

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