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I have a dataset where different features were measured for three conditions. Each feature consists of sub-features, and sub-features are nested within features. I'm interested in the contrasts cond1 vs ctrl and cond2 vs ctrl on the feature level. Here is a part of the dataset (the full data contains ~10k features) and here's an overview of the design: enter image description here

Each sub-feature has it's own intercept enter image description here and so I fit a model readout ~ (1|subfeature) + (condition|feature). This gives a boundary (singular) fit warning, likely because a corresponding fixed-effects model readout ~ subfeature + condition:feature has a non full-rank design.

How can I simplify the mixed-effects model to have a full-rank design? In the fixed-effects only case, I would replace the design matrix columns corresponding to the interaction effects by columns corresponding to the contrasts conditioncond{1|2}_vs_conditionctrl:featuref{1|2}.

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If the model matrix for subfeature + condition:feature is not of full rank, then I would simply remove condition from the random structure.

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  • $\begingroup$ Does this answer your question ? If so, please consider marking it as accepted. If not, please let us know why. Thanks ! $\endgroup$
    – Robert Long
    Commented Jul 19, 2020 at 4:48

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