[R-sig-ME] zero variance and standard deviation in random effects

[Tahsin Ferdous]

Thanks a lot. My model is a random intercept model. But from the "coef(m2)"
command, I have found the following results:

Prov      Intercept
 AB.       0.07346574
 MB.      0.07346574
 SK.       0.07346574

That means intercepts are identical for all three provinces. In this model,
Prov is the random effect that has three-level (AB, MB and SK). In this
case, what should I do? If I remove province, the model will not be then
mixed model. But my data is repeated measures. I have also attached the
plot by running the command ( performance::check_model()).

On Tue, Nov 2, 2021 at 12:11 AM Carola Bloch <carola.bloch using uk-koeln.de>
wrote:

> Hi,
>
>
> thanks for sharing your problem. Concerning your first question, I would
> not recommend running a regular regression, as the data points in your
> sample are not independent and this would inflate the type 1 error rate.
>
>
> In order to find out why the residual variance shows strange values, I
> would try some trouble shooting. You could run coef(m2) and check whether
> there are actually different intercepts for Prof. Second I would check
> the model assumptions, possibly there is a violation of the assumptions
> that affects model fit (I'd recommend performance::check_model()).
> Furthermore, how many factor levels does Prof have, I assume 3 according
> to your output? A small number of levels might be problematic, see
> Singman & Kellen, 2019*.
>
>
> *Singmann, H., & Kellen, D. (2019). An introduction to mixed models for
> experimental psychology. In *New methods in cognitive psychology* (pp.
> 4-31). Routledge.
>
>
> Hope this helps!
> ------------------------------
> *Von:* R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> im
> Auftrag von Tahsin Ferdous <tahsinferdousuofc using gmail.com>
> *Gesendet:* Dienstag, 2. November 2021 05:57:26
> *An:* r-sig-mixed-models using r-project.org
> *Betreff:* [R-sig-ME] zero variance and standard deviation in random
> effects
>
> Hi,
>
> I am running a mixed model using lmer like this:
>
> m2<-lmer( logSeverity~  Incidence+Year+ (1|Prov), data = prov1,REML =
> FALSE)
>
> Here, prov is my random effect. But I have the result, where the random
> intercept of random effect is zero.
>
> Random effects:
>  Groups   Name        Variance Std.Dev.
>  Prov     (Intercept) 0.00000  0.0000
>  Residual             0.01149  0.1072
> Number of obs: 54, groups:  Prov, 3
>
> Should I still run a mixed model using Prov as a random effect, or I run
> regression model here instead of mixed model by removing "Prov".
> My data structure is like this:
>
>    Prov Year Incidence Severity
>   MB 2020 31.5 0.29
>   MB 2019 21.8 0.36
>   MB 2018 20.4 0.23
>   MB 2017 31.1 0.31
>   MB 2016 90.1 1.34
>   MB 2015 63.4 0.5
>   MB 2014 57.5 0.7
>   MB 2013 44.1 0.45
>   MB 2012 42.9 0.8
>   MB 2011 15.6 0.92
>   MB 2010 50.9 1.23
>   MB 2009 32.1 1.56
>   MB 2008 52.4 1.71
>   MB 2007 15.1       0.83
>   MB 2006 4.3       0.65
>   MB 2005 47.7 1.4
>   MB 2004 16.4 1.58
>   MB 2003 39.3 0.33
>   SK 2020 25.7 0.33
>   SK 2019 37.3 0.54
>   SK 2018 14.2 0.32
>   SK 2017 4.8        0.51
>   SK 2016 85.2 1.53
>   SK 2015 53.2 0.57
>   SK 2014 68.1        1.45
>   SK 2013 23.2 0.39
>   SK 2012 49.8 1.14
>   SK 2011 10.6 0.79
>   SK 2010 13.5 1.5
>   SK 2009 6.9       0.56
>   SK 2008 7.6 0.92
>   SK 2007 2.4 0.75
>   SK 2006 0.7 0.58
>   SK 2005 4.1 0.71
>   SK 2004 1.7 0.4
>   SK 2003 1.9 0.09
>   AB 2020 8 0.34
>   AB 2019 28.3 0.52
>   AB 2018 2.8 0.37
>   AB 2017 3.7 0.49
>   AB 2016 32.8 0.59
>   AB 2015 9.2 0.29
>   AB 2014 24.6 0.25
>   AB 2013 17.6 0.4
>   AB 2012 10.3 0.63
>   AB 2011 5.2 0.87
>   AB 2010 3.9 1.68
>   AB 2009 3.2 1.13
>   AB 2008 0.4 0.78
>   AB 2007 0.1 0.45
>   AB 2006 0.1 0.78
>   AB 2005 1.1 1.09
>   AB 2004 1.2 0.82
>   AB 2003 1.2 0.08
>
>         [[alternative HTML version deleted]]
>
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