[R-sig-ME] Zero cells in contrast matrix problem
Viechtbauer Wolfgang (STAT)
wolfgang.viechtbauer at maastrichtuniversity.nl
Wed May 27 23:21:28 CEST 2015
You may need to consider using an 'exact', Bayesian, or penalized likelihood approach (along the lines proposed by Firth).
Maybe a place to start: http://sas-and-r.blogspot.nl/2010/11/example-815-firth-logistic-regression.html
Best,
Wolfgang
> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-
> project.org] On Behalf Of Francesco Romano
> Sent: Wednesday, May 27, 2015 23:00
> To: r-sig-mixed-models at r-project.org
> Subject: [R-sig-ME] Zero cells in contrast matrix problem
>
> After giving up on a glmer for my data, I remembered a post by Roger Levy
> suggesting to try the use non mixed effects glm when one of the cells in
> a
> matrix is zero.
>
> To put this into perspective:
>
> > trial<-glmer(Correct ~ Syntax.Semantics + (1 | Part.name), data =
> trialglm, family = binomial)
>
> Warning messages:
> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
> :
> Model failed to converge with max|grad| = 0.053657 (tol = 0.001,
> component 4)
> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
> :
> Model is nearly unidentifiable: large eigenvalue ratio
> - Rescale variables?
>
> My data has a binary outcome, correct or incorrect, a fixed effect
> predictor factor with 8 levels, and a random effect for participants. I
> believe the problem R is encountering is with one level of the factor
> (let
> us call it level B) which has no counts (no I won' t try to post the
> table
> from the paper with the counts because I know it will get garbled up!).
>
> I attempt a glm with the same data:
>
> > trial<-glm(Correct ~ Syntax.Semantics, data = trialglm, family =
> binomial)
> > anova(trial)
> Analysis of Deviance Table
>
> Model: binomial, link: logit
>
> Response: Correct
>
> Terms added sequentially (first to last)
>
>
> Df Deviance Resid. Df Resid. Dev
> NULL 384 289.63
> Syntax.Semantics 7 34.651 377 254.97
> > summary(trial)
>
> Call:
> glm(formula = Correct ~ Syntax.Semantics, family = binomial,
> data = trialglm)
>
> Deviance Residuals:
> Min 1Q Median 3Q Max
> -0.79480 -0.62569 -0.34474 -0.00013 2.52113
>
> Coefficients:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -1.6917 0.4113 -4.113 3.91e-05 ***
> Syntax.Semantics A 0.7013 0.5241 1.338 0.1809
> Syntax.Semantics B -16.8744 904.5273 -0.019 0.9851
> Syntax.Semantics C -1.1015 0.7231 -1.523 0.1277
> Syntax.Semantics D 0.1602 0.5667 0.283 0.7774
> Syntax.Semantics E -0.8733 0.7267 -1.202 0.2295
> Syntax.Semantics F -1.4438 0.8312 -1.737 0.0824 .
> Syntax.Semantics G 0.4630 0.5262 0.880 0.3789
> ---
> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> (Dispersion parameter for binomial family taken to be 1)
>
> Null deviance: 289.63 on 384 degrees of freedom
> Residual deviance: 254.98 on 377 degrees of freedom
> AIC: 270.98
>
> Number of Fisher Scoring iterations: 17
>
> The comparison I'm interested in is between level B and the reference
> level but it cannot be estimated as shown by the ridiculously high
> estimate
> and SE value.
>
> Any suggestions on how to get a decent beta, SE, z, and p? It's the only
> comparison missing in the table for the levels I need so I think it would
> be a bit unacademic of me to close this deal saying 'the difference could
> not be estimated due to zero count'.
>
> And by the way I have seen this comparison being generated using other
> stats.
>
> Thanks in advance,
>
> Frank
>
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>
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