[R-sig-ME] glmer() Gamma distribution - constant coefficient of variation
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Tue Mar 30 02:54:02 CEST 2021
I would start by checking the scale-location plot, i.e.
plot(fitted_model, sqrt(abs(resid(., type="pearson"))) ~ fitted(.))
if that's fairly flat, you should be OK.
Not that important, but can you tell me why you're fitting an
identity-link Gamma model? I'm always curious (I've read Lo and Andrews
[2015] but don't find their argument particularly convincing ...). Do
you have reasons to believe the relationships are linear rather than
log-linear?
Lo, Steson, and Sally Andrews. “To Transform or Not to Transform: Using
Generalized Linear Mixed Models to Analyse Reaction Time Data.”
Frontiers in Psychology 6 (August 7, 2015).
https://doi.org/10.3389/fpsyg.2015.01171.
On 3/22/21 5:24 PM, Hedyeh Ahmadi wrote:
> Hi all,
> I am running a glmer() with Gamma distribution and identity link. The R output is as follows. I would like to check the constant coefficient of variation assumption in R but I am not sure where to start. Any help would be appreciated.
>
> Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
> Family: Gamma ( identity )
> Formula: Y ~ 1 + pm252016aa + race +prnt.empl + overall.income + (1 | site)
> Data: Family
> Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 100000))
>
> AIC BIC logLik deviance df.resid
> 68781.7 68917.1 -34371.8 68743.7 9180
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -1.9286 -0.7314 -0.0598 0.6770 3.9599
>
> Random effects:
> Groups Name Variance Std.Dev.
> site (Intercept) 0.66157 0.8134
> Residual 0.04502 0.2122
> Number of obs: 9199, groups: site, 21
>
> Fixed effects:
> Estimate Std. Error t value Pr(>|z|)
> (Intercept) 52.3578 1.3102 39.962 < 0.0000000000000002 ***
> pm252016aa -0.1260 0.1099 -1.147 0.251212
> race_1 1.0913 0.7106 -1.536 0.124628
> race_2 -1.1787 0.6870 3.171 0.001518 **
> prnt.empl 2.8852 0.4377 4.307 0.000016517 **
> overall.income[>=100K] -1.8476 0.3693 -5.003 0.000000566 ***
> overall.income[>=50K & <100K] -0.8644 0.3403 -2.540 0.011078 *
> ---
> Signif. codes: 0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1
>
>
> Best,
>
> Hedyeh Ahmadi, Ph.D.
> Statistician
> Keck School of Medicine
> Department of Preventive Medicine
> University of Southern California
>
> Postdoctoral Scholar
> Institute for Interdisciplinary Salivary Bioscience Research (IISBR)
> University of California, Irvine
>
> LinkedIn
> www.linkedin.com/in/hedyeh-ahmadi<http://www.linkedin.com/in/hedyeh-ahmadi>
> <http://www.linkedin.com/in/hedyeh-ahmadi><http://www.linkedin.com/in/hedyeh-ahmadi>
>
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