Publications by Rense Nieuwenhuis
R-Sessions 30: Visualizing missing values
It always takes some time to get a grip on a new dataset, especially large ones. The code-books are often as indispensable as they are massive, and not always as clear as one would want. Routings, and resulting and strange patterns of missing values are at times difficult to find. I found a nice way to plot missing values, using R. Basically, I ...
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R-Sessions 31: Combining lmer output in a single table (UPDATED)
There are various ways of getting your output from R to your publication draft. Most of them are highly efficient, but unfortunately I couldn’t find a function that combines the output from several (lmer) models and presents it in a single table. lmer is the mixed effects model function from … Related To leave a comment for the...
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R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R
Intended to be a customized solution, it may have grown to be a little more. forward.lmer is an early installment of a full stepwise function for mixed effects regression models in R-Project. I may put in some work to extend it, or I may not. Nevertheless, in a ‘forward sense … Related To leave a comment for the author, please ...
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useR! 2009 acceptance: presenting influence.ME
The organizing committee of the useR! 2009 conference just informed me, that my submission for presenting my extension package influence.ME, has been accepted! Influence.ME is a new R package that I’m currently developing, with the indispensable help of Ben Pelzer and Manfred te Grotenhuis. Although I did not yet introduce … Rel...
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Introducing Influence.ME: Tools for detecting influential data in mixed models
I’m highly excited to announce that influence.ME is now available. Influence.ME is a new software package for R, providing statistical tools for detecting influential data in mixed models. It has been developed by Rense Nieuwenhuis, Ben Pelzer, and Manfred te Grotenhuis. The basic rationale behind identifying influential data is tha...
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One outlier and you’re out: Influential data and racial prejudice
Currently preparing a presentation on analyzing influential data in mixed effects models myself, my eye fell on an article in which important claims on racial prejudice were refuted. An important aspect of the criticism on existing work, is that in one article the main correlation was completely due to a single observation. Solely based on this s...
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Influence.ME: don’t specify the intercept
Just recently, I was contacted by a researcher who wanted to use influence.ME to obtain model estimates from which iteratively some data was deleted. In his case, observations were nested within an area, but there were very unequal numbers of observations in each area. Unfortunately, he wasn’t able to use the influence.ME package on his models....
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Presenting influence.ME at useR!
Today I presented influence.ME at the useR! conference in Rennes. Influence.ME is an R package for detecting influential data in mixed models. I developed this package together with Ben Pelzer and Manfred te Grotenhuis. More information about influence.ME can be found on another section of my website. Below, please find the slides of the presenta...
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Influence.ME: Simple Analysis
With the introduction of our new package for influential data influence.ME, I’m currently writing a manual for the package. This manual will address topics for both the experienced, and the inexperienced users. I will also present much of the content of this manual on my blog. Of course, feel free to comment on it, and readers are encouraged t...
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R Sessions 33: Select (nested) observations with equal number of occurences
Recently, I was contacted with an question about R code. A befriended researcher was working with nested data, which was unbalanced. He was working with data in a ‘long’ format: all observations nested within the same group had the same identification number. But, the number of observations in each of the groups differed (hence: unbalanced da...
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