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

Carola Bloch c@ro|@@b|och @end|ng |rom uk-koe|n@de
Tue Nov 2 07:11:06 CET 2021


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

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