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

[D. Rizopoulos]

You can set the initial values in the mixed_model() function of my 
GLMMadaptive package using the initial_values argument. For more info, 
check: https://drizopoulos.github.io/GLMMadaptive/reference/mixed_model.html

Best,
Dimitris


On 10/8/2018 1:10 PM, Mario Garrido wrote:
> Thanks again Dr. Rizopoulos,
> i cannot find the command initial values in the Package 'lme4' PDF here:
> https://cran.r-project.org/web/packages/lme4/lme4.pdf
> 
> Where can I look how to use it?
> 
> thanks!
> 
> 2018-10-08 14:09 GMT+03:00 Mario Garrido <gaadio using post.bgu.ac.il>:
> 
>> Thanks so much Dimitris,
>> in any case, now I have recieved another warning
>>
>> fm <- mixed_model(countMyc.qPCR ~ sp + day, random = ~ 0+day | exp.ID,
>> data = MycGLMM, family = poisson())
>> Error in optim(par = b_i, fn = log_post_b, gr = score_log_post_b, method =
>> "BFGS",  :
>>    non-finite value supplied by optim
>>
>> Any idea why?
>>
>> Thanks!
>>
>> PS, sorry to insist, but is not a problem what I said in the mail
>> before? due the differences in scales between the minimum and maximum value
>>
>>
>>
>>
>> El sáb., 6 oct. 2018 19:56, D. Rizopoulos <d.rizopoulos using erasmusmc.nl>
>> escribió:
>>
>>> 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>
>>> *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>
>>> *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
>>>
>>>          [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>
>>
>>
>> --
>> 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
>>
>>
>>
> 
> 

-- 
Dimitris Rizopoulos
Professor of Biostatistics
Department of Biostatistics
Erasmus University Medical Center

Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands
Tel: +31/(0)10/7043478
Fax: +31/(0)10/7043014
Web (personal): http://www.drizopoulos.com/
Web (work): http://www.erasmusmc.nl/biostatistiek/
Blog: http://iprogn.blogspot.nl/