statistics

What is Cluster Analysis? Cluster analysis is a grouping technique that aims to put in the same group (or cluster) objects that are more similar to each other. The intent is very similar to what our brain does when we try to recognise a pattern and group elements based on similarity.

Often, when implementing statistical analysis we ignore variability, and focus on systematic effects of independent variables. This is possible because we assume that variability occurs at random and equally across variables, stimuli , participants, and time points - that it is homogeneous.

We take off from where we left, and add an interaction term to the mixed model, to run customised linear contrasts. As usual, we are using the sleepstudy dataset: {r} # install.

Regression (and multilevel/mixed models) gives an estimate of the effect of an independent variable on the dependent variable. When estimating the effect of a categorical independent variable, the mixed model will estimate the effect of each of its levels, and tell us if each effect is significantly different from zero.

At one of my first lab meetings as a postdoc, I started advertising multilevel/mixed models to my colleagues to convince them to give up multiple testing. On that occasion, my supervisor struck me with the question:

In this final video of the series, I demonstrate running a simple mixed model in R with the function lmer. This video is a step-by-step demonstration of how to run a mixed model in R with lmer, using the sleepstudy dataset.

As part of this series on Mixed Models, Mary Agyapong shared her analytic pipeline to run a mixed model in SPSS using the mixed command. Our motivation to run this demo was a common misconception of the definition of mixed/multilevel models as a repeated-measures regression only, originating from the fact that SPSS mixed command can be run without specifying the random effect (such model would be a Generalised Estimating Equation (GEE) model, and not a mixed model).

The example we have looked at before only included two variables, so we are going to add a third variable, and we are going to investigate the interaction between the independent variable (Days) and this newly generated variable (Lunch):

Understanding a multilevel model in R is one thing, but interpreting the output, plotting the results is another story. Let’s go through all the steps of fitting and interpreting the model with some example data.

Linear Mixed Models are, literally, one of my favourite techniques. When I first started learning Mixed Models during my last year of PhD (2017), they were not at all common in my field (Psychology), but their use quickly spread and publications and resources multiplied.