Articles by Rense Nieuwenhuis

Influence.ME: don’t specify the intercept

June 18, 2009 | Rense Nieuwenhuis

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 ... [Read more...]

One outlier and you’re out: Influential data and racial prejudice

June 16, 2009 | Rense Nieuwenhuis

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 ... [Read more...]

Introducing Influence.ME: Tools for detecting influential data in mixed models

April 29, 2009 | Rense Nieuwenhuis

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, a... [Read more...]

useR! 2009 acceptance: presenting influence.ME

April 23, 2009 | Rense Nieuwenhuis

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 ... [Read more...]

R-Sessions 32: Forward.lmer: Basic stepwise function for mixed effects in R

February 13, 2009 | Rense Nieuwenhuis

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. Neverthel... [Read more...]

R-Sessions 31: Combining lmer output in a single table (UPDATED)

February 5, 2009 | Rense Nieuwenhuis

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.... [Read more...]

R-Sessions 30: Visualizing missing values

January 8, 2009 | Rense Nieuwenhuis

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. ...