• influence.ME now supports new lme4 1.0

    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 tools has lagged behind. Hence, we developed influence.ME, which calculates standardized measures of influential data… Continue Reading

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

    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 from origin-countries, and individuals in countries. The strength of multilevel models lies in analyzing data… Continue Reading

  • Influential Data in Multilevel Regression: What are your strategies?

    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 the application of multilevel models in country-comparative studies, however, it has long been overlooked that… Continue Reading

  • influence.ME updated to version 0.9

    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 to CRAN, and should be available now to all users. Several improvements and changes were… Continue Reading

  • Applied R: Manual for the quantitative social scientist

    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 to and control over the data. I think this inspires users to analyze their data more creatively and sometimes in a more advanced way.

  • Index of the R-Sessions

    The R-Sessions are a series of blog entries on using R. A large part consists of an R-manual I once wrote. Other posts include some tricks I found out, as well as entries detailing functions and packages I wrote for R. The series already entails over forty posts, so I decided to create an index.… Continue Reading

  • 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… Continue Reading

  • 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 presentation.… Continue Reading

  • 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… Continue Reading