[R-sig-ME] repeated-measures mixed models

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Mon Jun 2 08:45:47 CEST 2014

Dear Ligia,

You have a one on one relation between treatment and enclosure. Therefore it is not possible estimate the separate effect. So you must drop either (1|enclosure) or treat from your model. (1|enclosure) is the obvious choice.

Unless you have multiple measurements per animal at each time, (Time|ID) can lead to a perfect fit. Reduce that to (1|ID).

lmer(response ~ treat + Time + (1|ID), data = data1) is a more sensible model given your data.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
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Thierry.Onkelinx op inbo.be

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-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces op r-project.org [mailto:r-sig-mixed-models-bounces op r-project.org] Namens Ligia Pizzatto do Prado
Verzonden: maandag 2 juni 2014 3:44
Aan: r-sig-mixed-models op r-project.org
Onderwerp: [R-sig-ME] repeated-measures mixed models

I've been struggling with this analyses for a while now. Did quite a bit of reading but I'm getting more confused than before.
I wanna look at the the response of animals to a treatment (4 levels). Each group of 5 animals were subjected to one treatment level, and kept in the same enclosure. Response was measured for each subjected at time0 (previous to treat.), 5days, 10 and 50days post treatment. Covariate is log(BodyMass at time0). So subjects are repeated and nested within bin. After setting ID, enclosure, and treatment as factors I am using lme4 to fit the model:
m1<-lmer(response~treat + Time +(Time|ID)+(1|enclosure), data=data1) and get the Warning messages:1: In optwrap(optimizer, devfun, getStart(start, rho$lower, rho$pp),  :  convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :  Model failed to converge with max|grad| = 1.55933 (tol = 0.002)3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :  Model failed to converge: degenerate  Hessian with 2 negative eigenvalues What am I doing wrong?
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