[R-sig-ME] Wrong convergence warnings with glmer in lme4 version 1.1-6??
UG
uriel.gelin at usherbrooke.ca
Wed May 14 16:13:43 CEST 2014
Tom Davis <tomd792 at ...> writes:
>
> Dear lme4 experts,
>
> Yesterday, I ran the code for two published papers (de Boeck et al.,2011;
> de Boeck and Partchev, 2012) on psychometric modeling with glmer in lme4
> version 1.1-6 and the vast majority of the models I ran produce convergence
> warnings (even the simple ones).
>
> For instance, the basic Rasch model in de Boeck et al. (2011) yields:
>
> > ## our example data set is bundled with lme4
> > data("VerbAgg")
> >
> > ## A first example: A Rasch model with fixed item effects and random
> person effects
> > m1 = glmer(r2 ~ 0 + item + (1|id), data=VerbAgg, family="binomial",
> + control=glmerControl(optCtrl=list(maxfun=100000)))
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.308607 (tol = 0.001)
>
> I am a bit puzzeled because, to my knowledge, especially the models for the
> VerAgg data (included in lme4) have been checked in many other programs
> (also ltm in R) and I heard that glmer produces results that are valid and
> consistent with SAS, HLM, ltm, and so on. However, this dataset produces
> convergence warnings even though the models are comparatively simple for
> psychometric research (basic Rasch and LLTM) and the estimates all seem
> reasonable.
>
> I also tried some datasets in the ltm package. Again, convergence warnings
> (the mobility data). The estimates are close to what ltm gives me.
>
> Even when I simulate data from a basic Rasch model using the rmvlogis
> function in ltm with a couple of extreme item parameters (which occur in
> psychometric tests), I also get these warnings. This happens despite glmer
> seems to recover the true values very well. The "gradient" errors and the
> hessian errors tend to increase with sample size and when I use
> binomial("probit") instead of binomial("logit"). It also seems like vector
> random effects produce many warnings. optimizer="bobqya" tends to reduce
> the warnings but not consistently. To me, it seems like the warnings occur
> randomly all over the place. Sometimes glmer "converges" (no warnings) with
> one optimizer setting and the values that are very close to the true
> values. However, with another optimizer setting, one gets practically
> exactly the same estimates and virtually the same likelihood value but a
> warning. I really do not understand what is going on.
>
> I had no warnings using version 1.0-5 and version 1.0-6 so this seems to be
> a recent problem of lme4?
>
> Is it best to ignore all these convergence warnings for now? Should I
> switch back to an older version of lme4 to avoid this problem? Should I
> generally avoid using large datasets with lme4?
>
> Many thanks in advance,
> Tom
>
> Papers (scripts are online):
> De Boeck, P., Bakker, M., Zwitser, R., Nivard, M., Hofman, A., Tuerlinckx,
> F., & Partchev, I. (2011). The estimation of item response models with the
> lmer function from the lme4 package in R. Journal of Statistical Software,
> 39, 1-28. http://www.jstatsoft.org/v39/i12
>
> De Boeck, P., & Partchev, I. (2012). IRTrees: Tree-based item response
> models of the GLMM family. Journal of Statistical Software, 48, 1-28.
> http://www.jstatsoft.org/v48/c01/
>
> Simulated data:
>
> > library("reshape")
> > library("ltm")
> > library("lme4")
> > set.seed("12345")
> >
> > simrasch<-data.frame(rmvlogis(200, cbind(seq(-5, 5, 0.5), 1)))
> > rasch(simrasch,IRT.param=F)
>
> Call:
> rasch(data = simrasch, IRT.param = F)
>
> Coefficients:
> X1 X2 X3 X4 X5 X6 X7 X8 X9
> X10 X11 X12
> 17.342 5.157 5.157 3.441 3.141 2.100 2.193 1.969 1.484
> 0.717 0.054 -0.347
> X13 X14 X15 X16 X17 X18 X19 X20
> X21 z
> -0.874 -1.415 -2.103 -2.696 -3.065 -3.152 -4.453 -4.216 -5.175
> 1.091
>
> Log.Lik: -1329.186
>
> >
> > simrasch$person = rownames(simrasch)
> > simraschlong = melt(simrasch, id = "person")
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 2702.484 2842.026 -1329.242 2658.484 4178
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 1.091
> Number of obs: 4200, groups: person, 200
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5
> variableX6 variableX7
> 19.04000 5.14239 5.13514 3.43274 3.13736
> 2.10085 2.19335
> variableX8 variableX9 variableX10 variableX11 variableX12
> variableX13 variableX14
> 1.97086 1.48698 0.71828 0.05206 -0.35255
> -0.88099 -1.42318
> variableX15 variableX16 variableX17 variableX18 variableX19
> variableX20 variableX21
> -2.11070 -2.70069 -3.06758 -3.15545 -4.44743
> -4.21083 -5.16615
> Warning messages:
> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.135068 (tol = 0.001)
> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge: degenerate Hessian with 1 negative eigenvalues
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial,
> + control=glmerControl(optimizer="bobyqa"))
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 2702.482 2842.025 -1329.241 2658.482 4178
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 1.09
> Number of obs: 4200, groups: person, 200
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5
> variableX6 variableX7
> 17.75458 5.14416 5.14417 3.43797 3.13991
> 2.10444 2.19676
> variableX8 variableX9 variableX10 variableX11 variableX12
> variableX13 variableX14
> 1.97412 1.48907 0.72049 0.05389 -0.34882
> -0.87841 -1.42030
> variableX15 variableX16 variableX17 variableX18 variableX19
> variableX20 variableX21
> -2.10751 -2.69767 -3.06471 -3.15126 -4.44429
> -4.20819 -5.16356
>
> > simrasch<-data.frame(rmvlogis(1000, cbind(seq(-5, 5, 0.5), 1)))
> > rasch(simrasch,IRT.param=F)
>
> Call:
> rasch(data = simrasch, IRT.param = F)
>
> Coefficients:
> X1 X2 X3 X4 X5 X6 X7 X8 X9
> X10 X11 X12
> 5.195 4.422 3.868 3.599 3.192 2.558 2.211 1.605 0.984
> 0.559 0.003 -0.398
> X13 X14 X15 X16 X17 X18 X19 X20
> X21 z
> -0.985 -1.590 -1.994 -2.401 -2.876 -3.411 -3.815 -4.683 -4.832
> 1.014
>
> Log.Lik: -6874.631
>
> >
> > simrasch$person = rownames(simrasch)
> > simraschlong = melt(simrasch, id = "person")
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 13793.707 13968.657 -6874.853 13749.707 20978
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 1.014
> Number of obs: 21000, groups: person, 1000
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5
> variableX6 variableX7
> 5.182206 4.415991 3.871713 3.599747 3.191841
> 2.561370 2.219830
> variableX8 variableX9 variableX10 variableX11 variableX12
> variableX13 variableX14
> 1.611983 0.992610 0.564159 0.003716 -0.398537
> -0.987771 -1.591732
> variableX15 variableX16 variableX17 variableX18 variableX19
> variableX20 variableX21
> -1.998437 -2.400455 -2.870716 -3.408691 -3.806151
> -4.672642 -4.813643
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.907091 (tol = 0.001)
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial,
> + control=glmerControl(optimizer="bobyqa"))
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 13793.696 13968.646 -6874.848 13749.696 20978
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 1.014
> Number of obs: 21000, groups: person, 1000
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5
> variableX6 variableX7
> 5.184774 4.414100 3.862784 3.594478 3.190096
> 2.559954 2.214893
> variableX8 variableX9 variableX10 variableX11 variableX12
> variableX13 variableX14
> 1.610161 0.988363 0.562450 0.002823 -0.400700
> -0.990108 -1.595035
> variableX15 variableX16 variableX17 variableX18 variableX19
> variableX20 variableX21
> -1.997929 -2.403493 -2.875794 -3.408200 -3.809483
> -4.674454 -4.822606
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.00124419 (tol = 0.001)
> >
> >
> >
>
> > set.seed("12345")
> > simrasch<-data.frame(rmvlogis(200, cbind(c(seq(-3, 3, 1),10), 1)))
> > rasch(simrasch,IRT.param=F)
>
> Call:
> rasch(data = simrasch, IRT.param = F)
>
> Coefficients:
> X1 X2 X3 X4 X5 X6 X7
> X8 z
> 3.226 1.820 1.461 0.044 -0.706 -1.494 -2.771 -10.454
> 0.801
>
> Log.Lik: -647.099
>
> >
> > simrasch$person = rownames(simrasch)
> > simraschlong = melt(simrasch, id = "person")
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 1312.7956 1361.1955 -647.3978 1294.7956 1591
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 0.7881
> Number of obs: 1600, groups: person, 200
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5 variableX6
> variableX7 variableX8
> 3.21214 1.82080 1.46473 0.04478 -0.71031 -1.49811
> -2.76154 -27.10659
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge: degenerate Hessian with 1 negative eigenvalues
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial,
> + control=glmerControl(optimizer="bobyqa"))
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 1312.7956 1361.1955 -647.3978 1294.7956 1591
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 0.7881
> Number of obs: 1600, groups: person, 200
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5 variableX6
> variableX7 variableX8
> 3.21213 1.82080 1.46473 0.04478 -0.71032 -1.49811
> -2.76154 -19.56371
>
> > set.seed("12345")
> > simrasch<-data.frame(rmvlogis(10000, cbind(c(seq(-3, 3, 1),10), 1)))
> > rasch(simrasch,IRT.param=F)
>
> Call:
> rasch(data = simrasch, IRT.param = F)
>
> Coefficients:
> X1 X2 X3 X4 X5 X6 X7 X8 z
> 3.018 2.026 1.011 0.012 -1.013 -1.941 -2.977 -9.696 0.986
>
> Log.Lik: -31914.71
>
> >
> > simrasch$person = rownames(simrasch)
> > simraschlong = melt(simrasch, id = "person")
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial)
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 63888.47 63972.08 -31935.24 63870.47 79991
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 0.9739
> Number of obs: 80000, groups: person, 10000
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5 variableX6
> variableX7 variableX8
> 3.01884 2.03562 1.02945 0.02299 -1.01066 -1.93548
> -2.95234 -9.44503
> Warning messages:
> 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 25.6865 (tol = 0.001)
> 2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge: degenerate Hessian with 1 negative eigenvalues
> > glmer(value ~ 0 + variable + (1|person), data=simraschlong,
> family=binomial,
> + control=glmerControl(optimizer="bobyqa"))
> Generalized linear mixed model fit by maximum likelihood (Laplace
> Approximation) ['glmerMod']
> Family: binomial ( logit )
> Formula: value ~ 0 + variable + (1 | person)
> Data: simraschlong
> AIC BIC logLik deviance df.resid
> 63887.88 63971.49 -31934.94 63869.88 79991
> Random effects:
> Groups Name Std.Dev.
> person (Intercept) 0.9737
> Number of obs: 80000, groups: person, 10000
> Fixed Effects:
> variableX1 variableX2 variableX3 variableX4 variableX5 variableX6
> variableX7 variableX8
> 3.00598 2.02857 1.01952 0.01191 -1.02146 -1.94484
> -2.96534 -9.65211
> Warning message:
> In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
> Model failed to converge with max|grad| = 0.0128742 (tol = 0.001)
>
> [[alternative HTML version deleted]]
>
>
Hi,
Sorry for the length of the message below but I also would need some help
and advice.
I am having similar issues (warnings about convergence, may be more) than
other people on this subject but I have been totally lost among all
suggestions and their meanings.
I test the effect of various covriant including inbreeding (O or 1) on
number of occurence of a given behaviour. As it is counting data, I use a
poisson family
The output of my model is :
Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 1.26172 (tol = 0.001)
2: In if (resHess$code != 0) { :
the condition has length > 1 and only the first element will be used
3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
> summary(test)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
Family: poisson ( log )
Formula: behaviour~ inbreeding+ opponent inbreeding + sexratio + opponent
aggressiveness+ opponent mass + duration of the test +
inbreeding:sexratio + opponent inbreeding:opponent mass+ (1 |
family/IDfocal)
Data: datamal
AIC BIC logLik deviance df.resid
1469.3 1507.5 -723.6 1447.3 228
Scaled residuals:
Min 1Q Median 3Q Max
-3.211 -1.223 -0.279 0.847 4.491
Random effects:
Groups Name Variance Std.Dev.
ID:family (Intercept) 0.25751 0.5075
family (Intercept) 0.04598 0.2144
Number of obs: 239, groups: ID:family, 63; family, 19
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.4300096 0.3798771 1.132 0.25765
inb -0.6387831 0.4348942 -1.469 0.14188
inbopp 1.2304585 0.4218720 2.917 0.00354 **
sexratio -0.7260132 0.4096143 -1.772 0.07632 .
oppaggre 0.0017798 0.0004272 4.166 3.1e-05 ***
oppmass 0.0010126 0.0078721 0.129 0.89764
duration 0.0065125 0.0003442 18.922 < 2e-16 ***
inb:sexratio 1.7354628 0.6444371 2.693 0.00708 **
inbopp:oppmass -0.0499209 0.0175064 -2.852 0.00435 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) inb inbopp sexrat oppaggr oppmass dura inb:sx
inb -0.598
inbopp -0.241 -0.004
sexratio -0.761 0.686 -0.007
oppaggre -0.121 0.021 -0.116 0.013
oppmass -0.545 0.048 0.482 0.052 -0.101
duration -0.079 -0.053 -0.013 -0.019 -0.331 -0.095
inb:sexrati 0.499 -0.900 0.004 -0.665 -0.039 -0.048 0.100
inbopp:oppmass 0.198 0.012 -0.987 0.012 0.154 -0.436 -0.001 -0.012
> Anova(test, type="III")
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: behaviour
Chisq Df Pr(>Chisq)
(Intercept) 1.2814 1 0.257647
inb 2.1574 1 0.141880
inbopp 8.5069 1 0.003538 **
sexratio 3.1415 1 0.076323 .
oppaggre 17.3554 1 3.1e-05 ***
oppmass 0.0165 1 0.897644
duration 358.0516 1 < 2.2e-16 ***
inb:sexratio 7.2522 1 0.007081 **
inbopp:oppmass 8.1315 1 0.004350 **
As usual, I looked at the distribution of residuals: nicely normal.
After that, according to the suggestions, I tested that:
relgrad <- with(test at optinfo$derivs,solve(Hessian,gradient))
max(abs(relgrad))
[1] 0.0002681561
-> if this is <0.001 or 0.002, is it enough to assume that the model is ok?
In addition, I tested other optimizer, even if I do not really understand
what it means.
I started with:
testbobyqa <- update(test,control=glmerControl(optimizer="bobyqa"))
> max(abs(relgrad))
[1] 0.01506629
testnelder <- update(test,control=glmerControl(optimizer="Nelder_Mead"))
> max(abs(relgrad))
[1] 0.001706096
both showed the same warnings and similar results than the first model.
I continued with:
library(optimx)
testnlminb <- update(test,control=glmerControl(optimizer="optimx",
optCtrl=list(method="nlminb")))
testBFGS <- update(test,control=glmerControl(optimizer="optimx",
optCtrl=list(method="L-BFGS-B")))
-> in both case: error
testnlminb:
Error in ans.ret[meth, ] <- c(ans$par, ans$value, ans$fevals, ans$gevals, :
number of items to replace is not a multiple of replacement length
In addition: Warning messages:
1: In optimx.check(par, optcfg$ufn, optcfg$ugr, optcfg$uhess, lower, :
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
2: In pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) :
Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 431
3: In pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) :
Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 431
testBFGS:
Error in eval(expr, envir, enclos) :
(maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate
In addition: Warning messages:
1: In optimx.check(par, optcfg$ufn, optcfg$ugr, optcfg$uhess, lower, :
Parameters or bounds appear to have different scalings.
This can cause poor performance in optimization.
It is important for derivative free methods like BOBYQA, UOBYQA, NEWUOA.
2: In pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) :
Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 431
3: In pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) :
Cholmod warning 'not positive definite' at
file:../Cholesky/t_cholmod_rowfac.c, line 431
4: In optwrap(optimizer, devfun, start, rho$lower, control = control, :
convergence code 9999 from optimx
I then used:
library(nloptr)
## from https://github.com/lme4/lme4/issues/98:
defaultControl <- list(algorithm="NLOPT_LN_BOBYQA",xtol_rel=1e-6,maxeval=1e5)
nloptwrap2 <- function(fn,par,lower,upper,control=list(),...) {
for (n in names(defaultControl))
if (is.null(control[[n]])) control[[n]] <- defaultControl[[n]]
res <- nloptr(x0=par,eval_f=fn,lb=lower,ub=upper,opts=control,...)
with(res,list(par=solution,
fval=objective,
feval=iterations,
conv=if (status>0) 0 else status,
message=message))
}
test.bobyqa2 <- update(g0.bobyqa,control=glmerControl(optimizer=nloptwrap2))
test.NM2 <- update(g0.bobyqa,control=glmerControl(optimizer=nloptwrap2,
optCtrl=list(algorithm="NLOPT_LN_NELDERMEAD")))
-> similar results than test, testbobyqa, testnelder and warnings for both:
Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 1.88422 (tol = 0.001)
2: In if (resHess$code != 0) { :
the condition has length > 1 and only the first element will be used
3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
> relgrad <- with(test.bobyqa2 at optinfo$derivs,solve(Hessian,gradient))
> max(abs(relgrad))
[1] 1.845328e-05
> relgrad <- with(test.NM2 at optinfo$derivs,solve(Hessian,gradient))
> max(abs(relgrad))
[1] 1.925339e-05
I also tried that:
getpar <- function(x) c(getME(x,c("theta")),fixef(x))
modList <- list(bobyqa=testbobyqa,NM=testnelder ,
+ bobyqa2=test.bobyqa2,NM2=test.NM2)
ctab <- sapply(modList,getpar)
library(reshape2)
mtab <- melt(ctab)
library(ggplot2)
theme_set(theme_bw())
ggplot(mtab,aes(x=Var2,y=value,colour=Var2))+
+ geom_point()+facet_wrap(~Var1,scale="free")
->I don't really understand wht does this graph mean, if you could explain
to me?
I also looked at this:
likList <- sapply(modList,logLik)
round(log10(max(likList)-likList),1)
bobyqa NM bobyqa2 NM2
-2.3 -2.3 -9.2 -Inf
->What do those values mean?
lbound <- getME(test,"lower")
theta <- getME(test,"theta")
any(lbound==0 & theta<1e-8)
[1] FALSE
-> it means that it is good?
Finally, I tried rescalling my covariables to see if it could be the source
of warnings by using:
datamal$variable.scaled <- datamal$variable/sd(datamal$variable)
and when I introduced the sclaed variables in the model, I received this
message:
Error in FUN(X[[1L]], ...) :
Invalid grouping factor specification, noms:famille
I have tried many things and I am totally lost so if you can help me
clarifying all that, it would be very appreciated.
I hope I wrote down all you needed to understand.
Thanks in advance for your help and sorry again for thlength of the message.
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