[R-sig-ME] zero variance and standard deviation in random effects
Tahsin Ferdous
t@h@|n|erdou@uo|c @end|ng |rom gm@||@com
Tue Nov 2 14:57:19 CET 2021
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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