Blog Archives

Presenting Weighted Effect Coding

November 8, 2016
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Weighted effect coding is a variant of dummy coding to include categorical variables in regression analyses, in which the estimate for each category represents the deviation of that category from the sample mean. The ‘wec’ ...

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Weighted Effect Coding: Dummy coding when size matters

October 31, 2016
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If your regression model contains a categorical predictor variable, you commonly test the significance of its categories against a preselected reference category. If all categories have (roughly) the same number of observations, you can also ...

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Update influence.ME, or why I love the open source community

August 17, 2016
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The other day, Kevin Darras contacted me about my R package influence.ME. The package didn’t work with the kind of models he wanted to estimate, and Kevin was looking for a solution. He had been ...

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Influence.ME now supports sampling weights

December 18, 2014
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Influence.ME is an R package that helps detecting influential cases in multilevel regression models. It has been around for a while now, and recent changes in lme4 broke the functionality of using influence.ME with sampling weights. Thanks to a kind contribution of some code by user Jennifer Bufford, influence.ME now should work with multilevel models… Continue Reading

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influence.ME now supports new lme4 1.0

August 21, 2013
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influence.ME is an R package for detecting influential data in multilevel regression models (or, mixed effects models as they are referred to in the R community). The application of multilevel models has become common practice, but the development of diagnostic ...

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Sure, this is silly, but this makes me feel a little bit cooler

July 24, 2013
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Look at this nice video on R statistics. It really advertises doing statistics in a way that is open to anyone!

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Influence.ME: Tools for Detecting Influential Data in Multilevel Regression Models

December 20, 2012
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Despite the increasing popularity of multilevel regression models, the development of diagnostic tools lagged behind. Typically, in the social sciences multilevel regression models are used to account for the nesting structure of the data, such as students in classes, migrants ...

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Influential Data in Multilevel Regression: What are your strategies?

November 13, 2012
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The application of multilevel regression models has become common practice in the field of social sciences. Multilevel regression models take into account that observations on individual respondents are nested within higher-level groups such as schools, classrooms, states, and countries. In ...

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influence.ME updated to version 0.9

July 13, 2012
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Influence.ME is an R extension package for R that provides tools for detecting influential data in multilevel regression models. It is developed by Rense Nieuwenhuis (that’s me), Manfred te Grotenhuis, and Ben Pelzer. Recently, a new version (0.9) was uploaded ...

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Applied R: Manual for the quantitative social scientist

March 23, 2011
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Applied R for the quantitative social scientist is a manual on R written specifically as an introduction for the quantitative social scientist. To my opinion, R-Project is a magnificent statistical program, ready to be accepted and implemented in the social sciences. The flexibility of this program and the way data are handled gives the user a sense of closeness...

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