-----Original Message-----
From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org]
On Behalf Of Ben Bolker
Sent: Monday, August 26, 2019 12:23 PM
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Testing assumption multilevel analysis
Carrying on (in case it's useful to future readers):
- I'll go a little bit further than Phillip and point out that
independence of errors is difficult to test *at all* without further
information (e.g. spatial and temporal structure). If you do have
spatial/temporal structure you can try computing autocorrelation functions
(e.g. using lme() and ACF())
- lack of multicollinearity is *not* an assumption of multilevel
analysis. It is a potential problem (in that it makes inference and
prediction harder), but not a violation of the assumptions. I like this
paper:
Graham, Michael H. ?Confronting Multicollinearity in Ecological Multiple
Regression.? Ecology 84, no. 11 (2003): 2809?15.
https://doi.org/10.1890/02-3114.
On 2019-08-26 11:35 a.m., Phillip Alday wrote:
Please keep the list in CC.
As Ben Bolker mentioned in his reply: for most things, the assumptions
carry over from the non-mixed case and the graphical diagnostics are
done the same way. I would in general avoid explicit statistical tests
of model assumptions (e.g. various tests of normality) because, like
all tests, they have failure modes (especially related to sensitivity
and
specificity) and don't actually tell you what any potential violation
of assumptions is doing to your statistical procedure.
For multicollinearity, there is one additional diagnostic that lme4
gives you in its summary output, namely the correlation of fixed
effects. The exact meaning of this is perhaps a little technical
(https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q1/001941.html)
, but in practical terms a high correlation suggests that there may be
multicollinearity. Multicollinearity also tends to show itself in
inflated standard errors (in the fixed effects), much as it does for
standard linear regression.
Regarding independence of errors: I find that to be an assumption that
is often best checked by knowing something about your data generating
process. For example, there may be some autocorrelation in the errors
between observations due to the way data are collected.
Best,
Phillip
On 26/8/19 2:23 pm, Katharina Tostmann wrote:
Hello Phillip,
Yes, I know it is a very big question about the assumptions in general.
At this time I got a little information about linearity, normal
distibution and variance homogenity. But what ist about
mulitcollinearity and independency? Do you have any idea to check
this in a multilevel context?
Thank you in advance.
best regards from Germany
Katharina
Am Mo., 26. Aug. 2019 um 14:14?Uhr schrieb Phillip Alday
<phillip.alday at mpi.nl <mailto:phillip.alday at mpi.nl>>:
This is a rather open-ended request -- you're more likely to get
advice if you're a bit more specific. For example, which model
assumptions do you want to test in particular? What do your data
like? Which assumptions do you think your data might violate? Why
want to explicitly test assumptions? (e.g. Are you worried about
inflated Type-I error? Often it's better to worry less about
per se and instead focus on "does my model capture the relevant
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
> how to test the model assumptions. In my second hypothesis I also
> 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]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org
<mailto:R-sig-mixed-models at r-project.org> mailing list