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 ...

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 ...

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 ...

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 ...

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 ...

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’ ...

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 ...

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 ...

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

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 ...

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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