2131 search results for "regression"

Visualizing systems of linear equations and linear transformations

December 2, 2013
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Visualizing systems of linear equations and linear transformations

This is a lecture post for my students in the CUNY MS Data Analytics program. In this series of lectures …Continue reading »

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Speeding up model bootstrapping in GNU R

December 2, 2013
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After my last post I have recurringly received two questions: (a) is it worthwhile to analyze GNU R speed in simulations and (b) how would simulation speed compare between GNU R and Python. In this post I want to address the former question and next ti...

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Ensemble, Part2 (Bootstrap Aggregation)

December 1, 2013
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Ensemble, Part2 (Bootstrap Aggregation)

Part 1 consisted of building a classification tree with the "party" package.  I will now use "ipred" to examine the same data with a bagging (bootstrap aggregation) algorithm. > library(ipred)> train_bag = bagging(class ~ ., data=train, coob...

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Ordinary Least Squares is dead to me

November 28, 2013
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Most books that discuss regression modeling start out and often finish with Ordinary Least Squares (OLS) as the technique to use; Generalized Least Squares (GLS) sometimes get a mention near the back. This is all well and good if the readers’ data has the characteristics required for OLS to be an applicable technique. A lot

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Errors-in-variables models in stan

November 27, 2013
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Errors-in-variables models in stan

In a previous post, I gave a cursory overview of how prior information about covariate measurement error can reduce bias in linear regression. In the comments, Rasmus Bååth asked about estimation in the absence of strong priors. Here, I’ll describe a Bayesian approach for estimation and correction for covariate measurement error using a latent-variable based errors-in-variables...

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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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