Manfred te Grotenhuis passed away. He was a respected sociologist, statistician, and teacher. I’ll leave it to others to comment on these achievements. To me, he was my teacher and mentor in statistics, and a ... [Read more...]

Weighted effect coding is a technique for dummy coding that can have attractive properties, particularly when analysing observational data. In a new publication in the R Journal we explain the rationale of weighted effect coding, ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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’ ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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, ... [Read more...]

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 ... [Read more...]

Look at this nice video on R statistics. It really advertises doing statistics in a way that is open to anyone! [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]

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 ... [Read more...]