[R-sig-ME] Testing assumption multilevel analysis

[Ben Bolker]

  Most of the assumptions of mixed/multivel/hierarchical are inherited
from the standard (generalized) linear modeling assumptions, and at
least the graphical diagnostics are done in the same way (residual vs
fitted plot, scale-location plot, Q-Q plots of residuals, influence
plots).  The main additional assumptions have to do with the group-level
effects, which are typically assumed to be normally distributed.
Typically people use "caterpillar plots" (plots of the BLUPS/conditional
modes for each group, sorted, with error bars based on the conditional
variances) to evaluate these distributional assumptions.

  There are some worked examples of mixed models (in ecology) at
https://bbolker.github.io/mixedmodels-misc/ecostats_chap.html

  Giving more context (as Phillip Alday suggests) would be a good idea.
 In particular, different research fields may emphasize different
criteria more or less.

On 2019-08-26 8:14 a.m., Phillip Alday wrote:
> This is a rather open-ended request -- you're more likely to get helpful
> advice if you're a bit more specific. For example, which model
> assumptions do you want to test in particular? What do your data look
> like? Which assumptions do you think your data might violate? Why do you
> want to explicitly test assumptions? (e.g. Are you worried about
> inflated Type-I error? Often it's better to worry less about assumptions
> per se and instead focus on "does my model capture the relevant aspects
> of my data?")
> 
> Phillip
> 
> On 24/8/19 11:08 am, Katharina Tostmann wrote:
>> Hello together,
>>
>> I'm calculating a multi-level analysis in R. However, I do not understand
>> how to test the model assumptions. In my second hypothesis I also have a
>> mediation with, whereby I also have no idea how to test the model
>> assumptions.
>> Can anyone help here? Thank you and best regards
>>
>> Katharina
>>
>> 	[[alternative HTML version deleted]]
>>
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