[R-sig-ME] ZERO variance and ZERO sd of random effect in lmer - justified to run a glm instead?

Tue May 22 05:24:54 CEST 2012

Try
dat$sitc <- scale(dat$sit, center = TRUE, scale = FALSE)

On 05/21/12, Julia Sommerfeld 
 wrote:
> the fixed effect "sit" is a logit transformed proportion of "psit". Some of
> the values in "psit" equal zero. Other than that, I don't know what could
> be the problem. To be honest, I haven't use the scale function before in a
> lmer. Thus, I don't quite understand why a different coef estimate is a
> problem? Since I transformed the "sit" values, why shouldn't the coef
> change accordingly?
> 
> Julia
> 
> 
> 
> 2012/5/22 Ewart Thomas <ethomas at stanford.edu>
> 
> > julia, there's something funny about your new fixed effects. i expected
> > the intercept to change, and it did - a reduction of 1.1.
> >
> > i expected the effect of sitc (1.3) to be the same as that of sit (.75);
> > and the effect of dive to be the same. why the change in the sitc coeff.
> > scale() just centers, it doesn't standardise, correct?
> >
> > see if you can figure out what's 'wrong', e.g., by looking at your raw
> > data file, coding of birds (as a factor), coding of sit as numeric. i
> > estimate the sit mean to be 0.84. is this right? ...
> > ewart
> >
> > On May 21, 2012, at 6:31 PM, Julia Sommerfeld wrote:
> >
> > Hi Ewart,
> >
> > Thanks for the quick reply. I've run the model using "sitc". But the
> > variance is still zero.
> >
> > dat$sitc <- scale(dat$sit)
> >
> > > mod8 <- lmer(ars1 ~ sitc + dive + (1|bird), data=dat, family=binomial)
> >
> > > summary(mod8)
> > Generalized linear mixed model fit by the Laplace approximation
> > Formula: ars1 ~ sitc + dive + (1 | bird)
> > Data: dat
> > AIC BIC logLik deviance
> > 159.4 171.7 -75.71 151.4
> > Random effects:
> > Groups Name Variance Std.Dev.
> > bird (Intercept) 0 0
> > Number of obs: 160, groups: bird, 25
> >
> > Fixed effects:
> > Estimate Std. Error z value Pr(>|z|)
> > (Intercept) -1.5423 0.3705 -4.163 3.14e-05 ***
> > sitc 1.2913 0.2495 5.175 2.28e-07 ***
> > dive 1.3076 0.4374 2.990 0.00279 **
> > ---
> > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> >
> > Correlation of Fixed Effects:
> > (Intr) sitc
> > sitc -0.156
> > dive -0.827 0.005
> >
> >
> > 2012/5/22 Ewart Thomas <ethomas at stanford.edu>
> >
> >> julia, while doug bates is right in noting that the 0 estimate suggests
> >> that var(intercept) is truly 0 (and can be omitted), i think your result is
> >> suspicious (not wrong).
> >>
> >> try centering 'sit': sitc = scale(sit)
> >>
> >> then use sitc instead of sit in mod6. the meaning of 'intercept' for
> >> each bird has changed: it is now a bird's average response (on the logit(p)
> >> scale) when the bird happens to have it's 'sit' value = the sample mean of
> >> 'sit'. you may know how best to interpret such 'intercepts'.
> >>
> >> good luck!
> >> ewart
> >>
> >> On May 21, 2012, at 6:07 PM, Julia Sommerfeld wrote:
> >>
> >> > Dear list,
> >> >
> >> > I'm running a lmer (package lme4) with a binomial error distribution and
> >> > "bird" as the random effect (160 observations of 25 birds). The response
> >> > variable "ars1" is coded as 0, 1.
> >> > The fixed effect "sit" is numerical and "dive" is categorical (0, 1).
> >> >
> >> > What puzzles me a little is that the a variance and sd of the random
> >> effect
> >> > is ZERO. Same question has been posted before and Douglas Bates answer
> >> was:
> >> >
> >> > "No, an estimate of zero is not suspicious. It is simply an indication
> >> >
> >> > that the variability between individuals is not significantly larger
> >> > than what one would expect from the random variability in the
> >> > response."
> >> >
> >> >
> >> > While another answer suggested that the model was "wrong":
> >> >
> >> >
> >> > "A zero estimate of a variance possibly indicates the model is wrong."
> >> This
> >> > wrong model seemed to be related to a negative covariation of one of the
> >> > fixed effects ?
> >> >
> >> >
> >> >
> >> > My simplified model is:
> >> >
> >> > mod6 <- lmer(ars1 ~ sit + dive + (1|bird), data=dat, family=binomial)
> >> >
> >> >> summary(mod6)
> >> > Generalized linear mixed model fit by the Laplace approximation
> >> > Formula: ars1 ~ sit + dive + (1 | bird)
> >> > Data: dat
> >> > AIC BIC logLik deviance
> >> > 159.4 171.7 -75.71 151.4
> >> > Random effects:
> >> > Groups Name Variance Std.Dev.
> >> > bird (Intercept) 0 0
> >> > Number of obs: 160, groups: bird, 25
> >> >
> >> > Fixed effects:
> >> > Estimate Std. Error z value Pr(>|z|)
> >> > (Intercept) -0.3615 0.4037 -0.895 0.37059
> >> > sit 0.7492 0.1448 5.175 2.28e-07 ***
> >> > dive 1.3076 0.4374 2.990 0.00279 **
> >> > ---
> >> > Signif. codes: 0 Œ***‚ 0.001 Œ**‚ 0.01 Œ*‚ 0.05 Œ.‚ 0.1 Œ ‚ 1
> >> >
> >> > Correlation of Fixed Effects:
> >> > (Intr) sit
> >> > sit 0.422
> >> > dive -0.756 0.005
> >> >>
> >> >
> >> >
> >> > Based on the summary output (zero variance and sd) and the two plots
> >> below,
> >> > I'm inclined to believe that in fact my random effect bird does not
> >> account
> >> > for any of the variance in the model. I.e., that there is no significant
> >> > variability between birds that I should account for.
> >> >
> >> > *QUESTION: Could I be overlooking something or is it justified to run a
> >> glm
> >> > without the random effect bird instead of a lmer?*
> >> >
> >> > Thank you!
> >> >
> >> > Best regards, Julia
> >> >
> >> >
> >> > dotplot(ranef(mod6, postVar=TRUE))
> >> >
> >> >
> >> >
> >> > qqnorm(unlist(ranef(mod6)), main="normal qq-plot, random effects")
> >> > qqline(unlist(ranef(mod6))) # qq of random effects
> >> >
> >> > [[alternative HTML version deleted]]
> >> >
> >> > _______________________________________________
> >> > R-sig-mixed-models at r-project.org mailing list
> >> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >>
> >>
> >
> >
> > --
> > Julia Sommerfeld - PhD Candidate
> > Institute for Marine and Antarctic Studies
> > University of Tasmania
> > Private Bag 129, Hobart
> > TAS 7001
> >
> > Phone: +61 477 289 301
> > Email: julia.somma at gmx.de
> > Julia.Sommerfeld at utas.edu.au
> >
> >
> >
> 
> 
> -- 
> Julia Sommerfeld - PhD Candidate
> Institute for Marine and Antarctic Studies
> University of Tasmania
> Private Bag 129, Hobart
> TAS 7001
> 
> Phone: +61 477 289 301
> Email: julia.somma at gmx.de
> Julia.Sommerfeld at utas.edu.au
> 
> [[alternative HTML version deleted]]
> 
> 
> 
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models