# 2224 search results for "Regression"

## The forestplot of dreams

December 8, 2013
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Displaying large regression models without overwhelming the reader can be challenging. I believe that forestplots are amazingly well suited for this. The plot gives a quick understanding of the estimates position in comparison to other estimates, while also showcasing the uncertainty. This project started with some minor tweaks to prof. Thomas Lumleys forestplot and ended up in a...

## Unobserved Effects With Panel Data

December 4, 2013
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It is common for researchers to be concerned about unobserved effects being correlated with observed explanatory variables.For instance, if we were curious about the effect of meditation on emotional stability we may be concerned that there might be so...

## Maximizing Return from Every Item in the Marketing Research Questionnaire

December 3, 2013
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Consumers will not complete long questionnaires, so marketing research must get the most it can from every item.  In this post, we look into the toolbox of R packages and search for statistical models that enable us to learn a great deal about eac...

## 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 »

## 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...

## Ensemble, Part2 (Bootstrap Aggregation)

December 1, 2013
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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...

## 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

## Errors-in-variables models in stan

November 27, 2013
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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...

## The R Backpages 2

November 27, 2013
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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...

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

November 26, 2013
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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