glmer warning messages using version 1.1.7

Thu, Mar 12, 2015 1:00 PM

The bad news is that when I run the profList function I get an error
message instead

+                    bobyqa=g0.bobyqa,
+                    NM=g0.NM,
+                    nlminb=g0.nlminb,
+                    LBFGSB=g0.LBFGSB),profile,dev.tol=1e-3,
+                    delta=0.1)
There were 14 warnings (use warnings() to see them)
Error in diag(vcov(object, use.hessian = use.hessian)) :
  error in evaluating the argument 'x' in selecting a method for function
'diag': Error in solve.default(h) : system is computationally singular:
reciprocal condition number = 3.04588e-17

warnings()
Warning messages:
1: In zeta(shiftpar, start = opt[seqpar1][-w]) :
  slightly lower deviances (diff=-3.55271e-15) detected
2: In nextpar(mat, cc, i, delta, lowcut, upcut) :
  Last two rows have identical or NA .zeta values: using minstep
3: In nextpar(mat, cc, i, delta, lowcut, upcut) :
  Last two rows have identical or NA .zeta values: using minstep
4: In profile.merMod(X[[1L]], ...) : non-monotonic profile
5: In zeta(shiftpar, start = opt[seqpar1][-w]) :
  slightly lower deviances (diff=-3.55271e-15) detected
6: In nextpar(mat, cc, i, delta, lowcut, upcut) :
  Last two rows have identical or NA .zeta values: using minstep
7: In nextpar(mat, cc, i, delta, lowcut, upcut) :
  Last two rows have identical or NA .zeta values: using minstep
8: In profile.merMod(X[[2L]], ...) : non-monotonic profile
9: In nextpar(mat, cc, i, delta, lowcut, upcut) :
  Last two rows have identical or NA .zeta values: using minstep
10: In profile.merMod(X[[3L]], ...) : non-monotonic profile
11: In zetafun(np, ns) : slightly lower deviances (diff=-3.55271e-15)
detected
12: In zetafun(np, ns) : slightly lower deviances (diff=-3.55271e-15)
detected
13: In nextpar(mat, cc, i, delta, lowcut, upcut) :
  Last two rows have identical or NA .zeta values: using minstep
14: In profile.merMod(X[[4L]], ...) : non-monotonic profile


 Many thanks,
Sophia

Message: 5

Date: Thu, 12 Mar 2015 14:45:05 -0400
From: Ben Bolker <bbolker at gmail.com>
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] glmer warning messages using version 1.1.7
Message-ID: <5501DEB1.30100 at gmail.com>
Content-Type: text/plain; charset=windows-1252

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

   I think these are ignorable.  If you want to turn them off,
check out ?glmerControl.

Here's what I looked at:

H <- g0.bobyqa at optinfo$derivs$Hessian
V <- solve(H)
diag(V)
## [1] 2.674870e+05 5.314489e-02
sqrt(diag(V))
## [1] 517.1914636   0.2305318

So lme4 is detecting that the surface is much flatter
in one direction than the other (the Wald std dev is about 2500
times greater for the first parameter (the RE std. dev) than for
the second (the intercept).

I have been working on the profiling stuff a bit in the development
version, so your results may not be identical, but mine seem to show
that only with Nelder-Mead (which is a bit funny since it is otherwise
often the worst optimizer out of the bunch) do we successfully profile
the RE standard dev.  I think I need to work on the robustness of
the profiler a little bit more.  The good news is that we do manage
to fit the profile, we see what we expect -- the sqrt(zeta) line is
very flat near the origin (corresponding to low curvature/large
uncertainty),
but steepens as we get away from it so that the actual profile
confidence intervals are not too big.

profList <- lapply(list(default=g0.bobyqa,
                   bobyqa=g0.bobyqa,
                   NM=g0.NM,
                   nlminb=g0.nlminb,
                   LBFGSB=g0.LBFGSB),profile,dev.tol=1e-3,
                   delta=0.1)
profdfList <-   Map(function(x,n) {
                          data.frame(as.data.frame(x),opt=n)
                       },
                       profList,names(profList))
profdf <- do.call(rbind,profdfList)
library("ggplot2"); theme_set(theme_bw())
ggplot(profdf,aes(.focal,abs(.zeta),colour=opt))+
         geom_point()+geom_line()+facet_grid(opt~.par,scale="free")+
             geom_hline(yintercept=1.96,col="gray")

On 15-03-12 01:56 PM, Sophia Kyriakou wrote:

Hello,

I run a simulation study fitting a GLMM random intercept model and
I get warning messages. I tried various optimizers but they all
give warning messages. Please find below one of the simulated
samples. I am using the lme4 package version 1.1.7 on R 3.1.3. Do I
ignore these warning messages or is there a way of dealing with
them?

Thanks in advance.

library(lme4)
library(optimx)
m <- 6
q <- 20
beta  <- 2
sigma2 <- 0.1
set.seed(206)
alpha <- rnorm(q, 0, sqrt(sigma2))
Y <- rbinom(q,m,plogis(alpha+beta))
cl <- seq.int(q)
tot <- rep(m,q)
dat <- data.frame(y = Y, tot = tot, cl = cl)
g0.default <- glmer(cbind(y, tot - y) ~ 1 + (1 | cl), data = dat,family =
  binomial(),nAGQ = 20)
g0.bobyqa <- glmer(cbind(y, tot - y) ~ 1 + (1 | cl), data = dat,family =
  binomial(),nAGQ = 20,control=glmerControl(optimizer="bobyqa"))
g0.NM <- update(g0.bobyqa,control=glmerControl(optimizer="Nelder_Mead"))
g0.nlminb <-

 update(g0.bobyqa,control=glmerControl(optimizer="optimx",optCtrl=list(method="nlminb")))
g0.LBFGSB <-

update(g0.bobyqa,control=glmerControl(optimizer="optimx",optCtrl=list(method="L-BFGS-B")))

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)) } g0.bobyqa2 <-
update(g0.bobyqa,control=glmerControl(optimizer=nloptwrap2)) g0.NM2
<-

update(g0.bobyqa,control=glmerControl(optimizer=nloptwrap2,optCtrl=list(algorithm="NLOPT_LN_NELDERMEAD")))



dd <- g0.bobyqa at optinfo$derivs
with(dd,max(abs(solve(Hessian,gradient)))<2e-3)

glmer warning messages using version 1.1.7

Thread (2 messages)