[R-sig-ME] help: error in (function...): Downdated VtV is not positive definite and convergence problems

[D. Rizopoulos]

You could give a try to the GLMMadaptive package that can fit the same model using the adaptive Gaussian quadrature, i.e.,

library(GLMMadaptive)
fm <- mixed_model(countMyc.qPCR ~ sp + day, random = ~ 0 day | exp.ID, data = your_data, family = poisson())
summary(fm)

Best,
Dimitris


- - - - - -
Dimitris Rizopoulos
Professor of Biostatistics
Erasmus University Medical Center
The Netherlands

From: Mario Garrido <gaadio using post.bgu.ac.il<mailto:gaadio using post.bgu.ac.il>>
Date: Saturday, 06 Oct 2018, 4:26 PM
To: r-sig-mixed-models using r-project.org <r-sig-mixed-models using r-project.org<mailto:r-sig-mixed-models using r-project.org>>
Subject: [R-sig-ME] help: error in (function...): Downdated VtV is not positive definite and convergence problems

Hello,
I tried to fit a GLMM and I get the following error. I know that my data is
probably more negative binomial than Poisson (sd>>mean), but I want to
understand where this problem comes from

glmer(countMyc.qPCR ~ sp+day +(0+day|exp.ID), family=poisson)

Error in (function (fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, maxit =
100L,  :
  Downdated VtV is not positive definite

*countMyc.qPCR*:amount of bacteria ina particualr individual     Numeric
discrete value
*sp*:species each individual vbelongs to Factor w/ 3 levels "GA","GG","GP"
*day*: day of infection    Numeric discrete value
*exp.ID*: number of individual under experiment  Factor w/ 33 levels
"EA1","EA10","EA12",..: 3 6 7 10 11 14 18 21 22 31 ...

I fixed the random factor as 0+day|exp.ID cause at day zero the amount of
bacteria is zero

Can be the error due the differences in scales between the minimum and
maximum value
> describe.by(countMyc.qPCR)
   vars   n     mean       sd       median   trimmed     mad      min
   max      range       skew      kurtosis       se
X1  1    363  127789.2  783829.6      6      455.65      8.9       0
 8434322   8434322     7.84        67.75      41140.39

In addition, when I tried to fix an simpler data I have also warnings, but
other kinds

glmer(countMyc.qPCR ~day +(0+day|exp.ID), family=poisson)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
 Family: poisson  ( log )
Formula: countMyc.qPCR ~ day + (0 + day | exp.ID)
       AIC        BIC     logLik   deviance   df.resid
 213021061  213021073 -106510528  213021055        360
Random effects:
 Groups Name Std.Dev.
 exp.ID day  0.1742
Number of obs: 363, groups:  exp.ID, 33
Fixed Effects:
(Intercept)          day
    13.3201      -0.1898
convergence code 0; 1 optimizer warnings; 0 lme4 warnings
Warning message:
In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
  Model is nearly unidentifiable: very large eigenvalue
 - Rescale variables?


PS. I saw a similar question before (19th July) but cannot find a solution
there
--
Mario Garrido Escudero, PhD
Dr. Hadas Hawlena Lab
Mitrani Department of Desert Ecology
Jacob Blaustein Institutes for Desert Research
Ben-Gurion University of the Negev
Midreshet Ben-Gurion 84990 ISRAEL

gaiarrido using gmail.com; gaadio using post.bgu.ac.il
phone: (+972) 08-659-6854

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