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 s?b., 6 oct. 2018 19:56, D. Rizopoulos <d.rizopoulos at erasmusmc.nl>
escribi?:
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 at post.bgu.ac.il>
*Date: *Saturday, 06 Oct 2018, 4:26 PM
*To: *r-sig-mixed-models at r-project.org <r-sig-mixed-models at 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 at gmail.com; gaadio at post.bgu.ac.il
phone: (+972) 08-659-6854
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