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  • in reply to: Compute effect sizes for mixed() objects #409

    A follow-up question in terms of the report of unstandardized effect sizes:
    If I fit a model using mixed, no bs are reported for the different fixed effects (which makes sense, as the factor levels might not be equally spaced and have no numerical representation at all).
    However, how can I then obtain reliable unstandardized effect sizes? Here you mention, that the bs from the summary of the full model are essentially not interpretable for k>2 factor levels.
    Would it be then be more appropriate to report the estimate produced by a post-hoc contrast analysis using emmeans? Can I then aggregate over the levels of the other factors present in the model, resulting in something that is comparable to a b? Or is this estimate generally only interpretable as a difference in the means, estimated by emmeans?

    in reply to: Correct Split-plot Mixed Model Specification #408

    Thanks for pointing me to the right direction: it seems to be a specific error/warning of lmerTest when used on centered, continuous predictor variables (so exactly the case here). It could therefore be replicated with afex using either method ("S" or "KR"). Source: Issue on Github

    Apparently this has not been resolved yet, however it seems a bit unclear to me, if this is a truly erroneous warning or something that points to a computational error deeper within the lmerTest code.

    Nevertheless, thanks again for your help and time! Keep up your great work and support!

    in reply to: Correct Split-plot Mixed Model Specification #406

    Thanks for the advice.

    Unfortunately the warning message persists even with the manual centering (producing a num vector ranging from -2.5 to 2.5).
    The model being used is mixed(quest ~ blockID.c + condition * SM.blockDiff + (blockID.c + SM.blockDiff|VP), data=df) – this should be correct, right?
    Using the uncentered blockID (num vector ranging from 0-5) only returns the warning about the missing centering but not the missing cells warning.

    in reply to: Correct Split-plot Mixed Model Specification #404

    Thank you again for your swift reply.
    I was not aware of your chapter, so thank you very much for the read. I will definitely have a look.

    Yes, your assumption is correct. Then I’m glad, that the model specification is correct.

    Concerning the centering: I now tried to rerun the model using a centered blockID – generated using blockID.c <- scale(blockID, scale = F).
    Fitting the model this way now generates a warning message stating: Missing cells for: blockID.c. Interpret type III hypotheses with care..
    The variable does not contain any NAs and the centering was successful (with distinct values of blockID.c ranging from -2.5 to 2.5 as expected with blockIDs ranging from 0-5). I was also not expecting any empty cells in the covariance matrix as every participant was assigned a condition, went trough the 6 blocks and experienced both difficulties.

    I’m a bit unsure where this warning comes from and how it should be treated.

    Sorry for bothering and thanks again for your help!

    in reply to: Correct Split-plot Mixed Model Specification #402

    Hi Henrik,

    thank you for your quick response and excellent reads!

    You are right, this is only a warning and not an error. I see the point of centering and the change in interpretation caused by this. We were just a little bit unsure whether centering would be applicable for a time variable such as this one (as the interpretation of a “mean” block felt rather unnatural).

    Concerning the “controlling for” of this factor: when inspecting the data visually, we concluded that probably only a linear effect would be appropriate (if at all an effect of blockID would play a role). In order to show, that this linear exhaustion effect plays no role in the interpretation of the other effects, we wanted to include it in the model.
    Judging from your model proposal, it would not be possible for us, to uncover interaction effects on condition if we wanted to avoid the centering issue, correct?

    If I understand your answer correctly, you would propose (if including it at all) either using blockID as a main effect only or if including it with its interaction term interpreting the other two factors as simple main effects, correct?

    What worried us as well is the question if the error term (when including blockID) was correctly specified as (blockID + SM.blockDiff|VP) judging from the experimental design. Is that one correct in your eyes?

    Thank you again and with regards,

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