[R-sig-ME] zero variance and standard deviation in random effects
Tahsin Ferdous
t@h@|n|erdou@uo|c @end|ng |rom gm@||@com
Wed Nov 3 05:46:07 CET 2021
Thanks a lot, everyone, for your valuable suggestion. I have run another
mixed model that assumes the district as random effects; each district has
values (yield) at different years. There are 12 districts; the data is
repeated measures. I get a very small variance for the districts
(5.692e-17). I checked the coefficients of the model. The intercepts for
all districts are the same. In this case, can I run a random intercept
mixed model? Or, which model will be appropriate in this case? Can I run a
regression model assuming year (2000 to 2018) and district ( 12 districts)
as covariates?
On Tue, Nov 2, 2021 at 9:20 AM Ben Bolker <bbolker using gmail.com> wrote:
> I agree. There is more discussion at
>
>
> http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html#singular-models-random-effect-variances-estimated-as-zero-or-correlations-estimated-as---1
>
> While I appreciate Carola Bloch's input, I think it's a little
> misguided. Having only three levels of the random effect is indeed
> problematic, but it doesn't actually violate any assumptions of the
> model, and there isn't necessarily anything else wrong with the model --
> it's just hard to estimate variance reliably from a sample of three.
> (See https://rpubs.com/bbolker/4187 for some simulated examples.) One
> standard approach to this problem is to treat province as a *fixed* effect.
>
> On 11/2/21 10:57 AM, Viechtbauer, Wolfgang (SP) wrote:
> > When the variance is estimated to be zero, then this is identical to
> removing the random effect altogether. So whether you remove it or not will
> not make any difference. I would leave it in and just report the results
> you obtained. One can also use confint() then to obtain a CI for this
> variance component. While the estimate (and hence lower bound) are 0, the
> upper bound is likely to indicate that there could be (substantial)
> variance associated with this random effect.
> >
> > Best,
> > Wolfgang
> >
> >> -----Original Message-----
> >> From: R-sig-mixed-models [mailto:
> r-sig-mixed-models-bounces using r-project.org] On
> >> Behalf Of Tahsin Ferdous
> >> Sent: Tuesday, 02 November, 2021 14:57
> >> To: Carola Bloch; r-sig-mixed-models using r-project.org
> >> Subject: Re: [R-sig-ME] zero variance and standard deviation in random
> effects
> >>
> >> 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
> > _______________________________________________
> > R-sig-mixed-models using r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
>
> --
> Dr. Benjamin Bolker
> Professor, Mathematics & Statistics and Biology, McMaster University
> Director, School of Computational Science and Engineering
> (Acting) Graduate chair, Mathematics & Statistics
>
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