Blog Archives

A novel method for modelling interaction between categorical variables

April 18, 2017
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We have been developing weighted effect coding in an ongoing series of publications (hint: a publication in the R Journal will follow). To include nominal and ordinal variables as predictors in regression models, their categories ...

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When Size Matters: Weighted Effect Coding

February 24, 2017
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Categorical variables in regression models are often included by dummy variables. In R, this is done with factor variables with treatment coding. Typically, the difference and significance of each category are tested against a preselected ...

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Exact p-values for pairwise comparison of Friedman rank sums

January 31, 2017
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BMC Bioinformatics has published a paper by colleagues of mine, about calculating exact p-values for pairwise comparison of Friedman rank sums. The paper provides fast and easy-to-use R code, making it an interesting read for ...

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New version of WEC: focus on interactions

January 17, 2017
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We have uploaded a new version of WEC, an R package to apply ‘weighted effect coding’ to your dummy variables. With weighted effect coding, your dummy variables represent the deviation of their respective category from ...

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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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