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How to fit a categorical random effect model?
There are two main ways to fit such a model, the first one is: This model estimated for all parameters their variation between the workers (see the Std.Dev column above) plus the correlation in the varying effect. Basically this tells us that worker that were better than average on machine a tended to be a bit worst than average on machine b and c.
Can a categorical variable be used in lme4?
To wrap up: one can use categorical variables as varying terms in lme4 but one need to be aware of what the model specification means and if there are enough data to allow the model to fit.
How to fit mixed effect models with varying effects?
The aim of this post is to see how to fit mixed effect models with varying effects when the explanatory variable that varies is a categorical variables. For instance imagine the following R formula:
Which is an alternative model structure for random effects?
So an alternative model structure would be: According to the github FAQ this random effect notation assume intercept varying among workers and among machines within workers (nested random effects).
How to calculate nested random effects in lme4?
According to the github FAQ this random effect notation assume intercept varying among workers and among machines within workers (nested random effects). So the model will estimate for each worker a deviation from the average operating time, the model estimated that the standard deviation of these deviations was 1.88.
Can a mixed effect model estimate many variance covariance parameters?
Anyhow it is pretty costly and hard for a mixed effect model to estimate many variance-covariance parameters, one can quickly get into convergence warnings when the number of levels increase (remember that for N levels there is N * (N + 1) / 2 variance-covariance parameter to estimate). So an alternative model structure would be: