
problem solved: 

As is probably mostly the case, a convergence problem lies in the specification of the model or the data itself.

Some information: I was trying to model the spatial distribution of fish of a particular age. The raw observations consisted of the number of individuals of a particular length. We were interested in modelling the number of fish of age A per hour: Y*a/(t*s) where Y is the count, a is the probability that a fish of length l belongs to age A, t is the haul duration and s is the subsample factor (when not all fish caught are measured). So I decided to use Y as the response variable and log(t*s/a) as the model offset. In some cases a was 0, leading to a model offset of infinity. So I decided to replace those zeros (6 observations) by a small value, but apparently not large enough. This must have caused the convergence problems in gamm.

After some reflection, I believe that one should often include the offset as model weights as well. E.g. assume you treat the log(fishing duration) as a model offset. If the fishing duration is very small compared to the rest or even 0, you haven’t really made an observation, so it should receive a very small or zero model weight. In case of zero weight you might as well remove the observation from the analysis. At least that is how I see it.

So the solutions that worked:
1) Y*a as the response variable and log(t*s) as offset
2) Replacing the zero a’s by not such a small value
3) Removing the rows with zero a’s, using Y as the response variable and log(t*s/a) as the offset and weights. 

I believe that option 3 is most elegant.

Unfortunately it turned out that nobody could answer the question, because nobody knew the details of the data. 

Nevertheless, Simon thanks a lot for your replies!

Cheers Geert





>Geert,

>Sorry for slow reply... I don't see any obvious problems with what you've 
>done, so I guess it's the usual problem that PQL just doesn't *have* to 
>converge, and the bit of extra flexibility of using a smooth is too much for 
>it in this case. If you send me the data offline I can dig a little bit more 
>if you like (I'll only use the data for this purpose etc. etc.) 

>You are right that PQL does the same thing for Poisson and quasi-poisson. I 
>don't think there is an easy way to use the values for the reduced dataset 
>fit in the full dataset fitting, unfortunately. 

>Another option is to use `gam' to fit the random effects. It'll be a bit slow 
>with 70+ random effects, as you have, and it's a bit more work to set up, but 
>it should converge. See ?gam.models which has some examples showing how to do 
>this.

best,
Simon
 

On Thursday 29 January 2009 08:20, geert aarts wrote:
> Simon, thanks for your reply and your suggestions.
>
> I fitted the following glmm's
>
> gamm3<-try(glmmPQL(count~offset(offsetter)+poly(lon,3)*poly(lat,3),random=l
>ist(code_tripnr=~1),family="poisson"))
>
> Which worked OK (see summary below)
>
> I also fitted a model using quasipoisson, but that didn't help. I actually
> also thought that glmmPQL and gamm estimate the dispersion parameter and
> hence assumes a quasipoisson distribution, even if you specify poisson. Is
> that correct?
>
> Finally I tried fitting a model to less data, and sometimes gamm managed to
> converge (see summary below). So would it be possible to use the parameter
> estimates from the model fitted to less data as starting values for the
> gamm fitted to the full data set? Or do you have any other suggestions?
>
> Thanks.
> Cheers Geert
>
>
>
>
>
>
> gamm3<-try(glmmPQL(count~offset(offsetter)+poly(lon,3)*poly(lat,3),random=l
>ist(code_tripnr=~1),f
>
> amily="poisson"))
>
>
>
> iteration
> 1
>
> iteration
> 2
>
> iteration
> 3
>
> >   detach(Disc_age)
>
> summary(gamm3)
>
> Linear
> mixed-effects model fit by maximum likelihood
>
>  Data: NULL
>
>   AIC BIC logLik
>
>    NA  NA
> NA
>
>
>
> Random
> effects:
>
>  Formula: ~1 | code_tripnr
>
>         (Intercept) Residual
>
> StdDev:
> 0.001391914 231.9744
>
>
>
> Variance
> function:
>
>  Structure: fixed weights
>
>  Formula: ~invwt
>
> Fixed
> effects: count ~ offset(offsetter) + poly(lon, 3) * poly(lat, 3)
>
>                                 Value
> Std.Error   DF     t-value p-value
>
> (Intercept)                    -1.582     11.96 2024 -0.13232174  0.8947
>
> poly(lon,
> 3)1                  -4.048   1397.33 2024 -0.00289673  0.9977
>
> poly(lon,
> 3)2                 -22.013    699.71 2024 -0.03145996  0.9749
>
> poly(lon,
> 3)3                  -8.538    593.87 2024 -0.01437683  0.9885
>
> poly(lat,
> 3)1                -109.624    666.05 2024 -0.16458856  0.8693
>
> poly(lat,
> 3)2                -104.179    381.37 2024 -0.27316977  0.7848
>
> poly(lat,
> 3)3                 -10.661    221.93 2024 -0.04803585  0.9617
>
> poly(lon,
> 3)1:poly(lat, 3)1  4290.737  61369.98 2024
> 0.06991589  0.9443
>
> poly(lon,
> 3)2:poly(lat, 3)1  1853.559  36835.63 2024
> 0.05031972  0.9599
>
> poly(lon,
> 3)3:poly(lat, 3)1  -240.521  25771.80 2024 -0.00933272  0.9926
>
> poly(lon,
> 3)1:poly(lat, 3)2  2540.147  41378.38 2024
> 0.06138826  0.9511
>
> poly(lon,
> 3)1:poly(lat, 3)2  2540.147  41378.38 2024
> 0.06138826  0.9511
>
> poly(lon,
> 3)2:poly(lat, 3)2 -1803.911  21522.17
> 2024 -0.08381643  0.9332
>
> poly(lon,
> 3)3:poly(lat, 3)2  1040.858  16352.56 2024
> 0.06365109  0.9493
>
> poly(lon,
> 3)1:poly(lat, 3)3   632.587  12180.28 2024
> 0.05193535  0.9586
>
> poly(lon,
> 3)2:poly(lat, 3)3  -394.339  13088.72 2024 -0.03012818  0.9760
>
> poly(lon,
> 3)3:poly(lat, 3)3  -543.502   6221.71 2024 -0.08735569  0.9304
>
>  Correlation:
>
>                             (Intr) ply(ln,3)1
> ply(ln,3)2 ply(ln,3)3 ply(lt,3)1
>
> poly(lon,
> 3)1                0.889
>
> poly(lon,
> 3)2                0.938  0.878
>
> poly(lon,
> 3)3                0.843  0.981
> 0.792
>
> poly(lat,
> 3)1               -0.829 -0.949     -0.906
> -0.882
>
> poly(lat,
> 3)2                0.859  0.578      0.742
> 0.538     -0.474
>
> poly(lat,
> 3)3               -0.552 -0.783     -0.579
> -0.756      0.837
>
> poly(lon,
> 3)1:poly(lat, 3)1 -0.947 -0.974
> -0.940     -0.940      0.925
>
> poly(lon,
> 3)2:poly(lat, 3)1 -0.934 -0.950
> -0.857     -0.929      0.881
>
> poly(lon,
> 3)3:poly(lat, 3)1 -0.818 -0.963
> -0.866     -0.945      0.931
>
> poly(lon,
> 3)1:poly(lat, 3)2  0.808  0.975
> 0.784      0.968     -0.928
>
> poly(lon,
> 3)2:poly(lat, 3)2  0.737  0.575
> 0.853      0.465     -0.659
>
> poly(lon,
> 3)3:poly(lat, 3)2  0.735  0.896
> 0.647      0.938     -0.765
>
> poly(lon,
> 3)1:poly(lat, 3)3 -0.794 -0.592
> -0.823     -0.518      0.591
>
> poly(lon,
> 3)2:poly(lat, 3)3 -0.542 -0.737
> -0.419     -0.781      0.635
>
> poly(lon,
> 3)3:poly(lat, 3)3 -0.398 -0.383
> -0.534     -0.334      0.425
>
>                             ply(lt,3)2
> ply(lt,3)3 p(,3)1:(,3)1 p(,3)2:(,3)1
>
> poly(lon,
> 3)1
>
> poly(lon,
> 3)2
>
> poly(lon,
> 3)3
>
> poly(lat,
> 3)1
>
> poly(lat,
> 3)2
>
> poly(lat,
> 3)3               -0.136
>
> poly(lon,
> 3)1:poly(lat, 3)1 -0.708      0.690
>
> poly(lon,
> 3)2:poly(lat, 3)1 -0.701      0.710      0.933
>
> poly(lon,
> 3)3:poly(lat, 3)1 -0.499      0.738      0.956        0.849
>
> poly(lon,
> 3)1:poly(lat, 3)2  0.458     -0.845
> -0.915       -0.934
>
> poly(lon,
> 3)2:poly(lat, 3)2  0.683     -0.344
> -0.719       -0.522
>
> poly(lon,
> 3)2:poly(lat, 3)2  0.683     -0.344
> -0.719       -0.522
>
> poly(lon,
> 3)3:poly(lat, 3)2  0.464     -0.655
> -0.834       -0.884
>
> poly(lon,
> 3)1:poly(lat, 3)3 -0.823      0.241      0.752        0.594
>
> poly(lon,
> 3)2:poly(lat, 3)3 -0.300      0.707      0.612        0.788
>
> poly(lon,
> 3)3:poly(lat, 3)3 -0.266      0.148      0.493        0.250
>
>                             p(,3)3:(,3)1
> p(,3)1:(,3)2 p(,3)2:(,3)2 p(,3)3:(,3)2
>
> poly(lon,
> 3)1
>
> poly(lon,
> 3)2
>
> poly(lon,
> 3)3
>
> poly(lat,
> 3)1
>
> poly(lat,
> 3)2
>
> poly(lat,
> 3)3
>
> poly(lon,
> 3)1:poly(lat, 3)1
>
> poly(lon,
> 3)2:poly(lat, 3)1
>
> poly(lon,
> 3)3:poly(lat, 3)1
>
> poly(lon,
> 3)1:poly(lat, 3)2 -0.928
>
> poly(lon,
> 3)2:poly(lat, 3)2 -0.637        0.432
>
> poly(lon,
> 3)3:poly(lat, 3)2 -0.851
> 0.935        0.245
>
> poly(lon,
> 3)1:poly(lat, 3)3  0.642       -0.482       -0.894       -0.410
>
> poly(lon,
> 3)2:poly(lat, 3)3  0.609       -0.822        0.007       -0.847
>
> poly(lon,
> 3)3:poly(lat, 3)3  0.551       -0.327       -0.637       -0.291
>
>                             p(,3)1:(,3)3
> p(,3)2:(,3)3
>
> poly(lon,
> 3)1
>
> poly(lon,
> 3)2
>
> poly(lon,
> 3)3
>
> poly(lat,
> 3)1
>
> poly(lat,
> 3)2
>
> poly(lat,
> 3)3
>
> poly(lon,
> 3)1:poly(lat, 3)1
>
> poly(lon,
> 3)2:poly(lat, 3)1
>
> poly(lon,
> 3)3:poly(lat, 3)1
>
> poly(lon,
> 3)1:poly(lat, 3)2
>
> poly(lon,
> 3)2:poly(lat, 3)2
>
> poly(lon,
> 3)3:poly(lat, 3)2
>
> poly(lon,
> 3)1:poly(lat, 3)3
>
> poly(lon,
> 3)3:poly(lat, 3)1
>
> poly(lon,
> 3)1:poly(lat, 3)2
>
> poly(lon,
> 3)2:poly(lat, 3)2
>
> poly(lon,
> 3)3:poly(lat, 3)2
>
> poly(lon,
> 3)1:poly(lat, 3)3
>
> poly(lon,
> 3)2:poly(lat, 3)3  0.080
>
> poly(lon,
> 3)3:poly(lat, 3)3  0.684       -0.180
>
>
>
> Standardized
> Within-Group Residuals:
>
>          Min           Q1          Med           Q3          Max
>
> -0.504980771 -0.000866948
> 0.028470924  0.078583094
> 33.247831244
>
>
>
> Number
> of Observations: 2113
>
> Number
> of Groups: 74
>
>
>
>
>
>
>
> gamm3<-try(gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=
>~1),family="quasipoisson", niterPQL=200))
>
>
>
>
> summary(gamm3$gam)
>
>
>
> Family:
> quasipoisson
>
> Link
> function: log
>
>
>
> Formula:
>
> count
> ~ offset(offsetter) + s(lon, lat)
>
>
>
> Parametric
> coefficients:
>
>   Estimate Std. Error t value Pr(>|t|)
>
> X  1.31370
> 0.09854   13.33
>
>
>
>
> summary(gamm3$lme)
>
> Linear
> mixed-effects model fit by maximum likelihood
>
>  Data: data
>
>        AIC
> BIC    logLik
>
>   2808.398 2837.845 -1398.199
>
>
>
> Random
> effects:
>
>  Formula: ~Xr.1 - 1 | g.1
>
>  Structure: pdIdnot
>
>            Xr.11    Xr.12
> Xr.13    Xr.14    Xr.15
> Xr.16    Xr.17    Xr.18
>
> StdDev:
> 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623
>
>            Xr.19   Xr.110
> Xr.111   Xr.112   Xr.113
> Xr.114   Xr.115   Xr.116
>
> StdDev:
> 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623
>
>           Xr.117   Xr.118
> Xr.119   Xr.120   Xr.121
> Xr.122   Xr.123   Xr.124
>
> StdDev:
> 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623 12.49623
>
>           Xr.125   Xr.126
> Xr.127
>
> StdDev:
> 12.49623 12.49623 12.49623
>
>
>
>  Formula: ~1 | code_tripnr %in% g.1
>
>         (Intercept) Residual
>
> StdDev:   0.8132693 5.077804
>
>
>
> Variance
> function:
>
>  Structure: fixed weights
>
>  Formula: ~invwt
>
> Fixed
> effects: list(fixed)
>
>                     Value  Std.Error
> DF   t-value p-value
>
> XX              1.3137042 0.09863463 923
> 13.318894  0.0000
>
> Xs(lon,lat)Fx1
> -0.4406352 0.23114503 923 -1.906315
> 0.0569
>
> Xs(lon,lat)Fx2
> -0.6217519 0.24918031 923 -2.495189  0.0128
>
>  Correlation:
>
>                XX     X(,)F1
>
> Xs(lon,lat)Fx1  0.015
>
> Xs(lon,lat)Fx2
> -0.009 -0.148
>
>
>
> Standardized
> Within-Group Residuals:
>
>         Min          Q1         Med          Q3         Max
>
> -3.42951750 -0.37448354
> 0.06432438  0.53690322  8.62026552
>
>
>
> Number
> of Observations: 1000
>
> Number
> of Groups:
>
>                  g.1 code_tripnr %in% g.1
>
>                    1                   75
>
>
>
>
>
>
>
> ----------------------------------------
>
> > From: s.wood at bath.ac.uk
> > To: r-help at r-project.org
> > Date: Fri, 23 Jan 2009 11:32:21 +0000
> > Subject: Re: [R] convergence problem gamm / lme
> >
> > Geert,
> >
> > Can you get a simpler model with, say, a quadratic dependence on lon, lat
> > to converge, using glmmPQL? The answer might give a clue about whether
> > the issue is related to using a smoother, or is something more basic.
> >
> > How confident are you that the Poisson assumption is reasonable?
> >
> > Can the model be fitted to a random subsample of the data, or does it
> > always fail? PQL can fail to converge, but it's usually not as obstinate
> > as it seems to be in this case, if the model structure is reasonable and
> > identifiable.
> >
> > best,
> > Simon
> >
> > On Thursday 22 January 2009 15:52, geert aarts wrote:
> >> Hope one of you could help with the following question/problem:
> >> We would like to explain the spatial
> >> distribution of juvenile fish. We have 2135 records, from 75 vessels
> >> (code_tripnr) and 7 to 39 observations for each vessel, hence the random
> >> effect for code_tripnr. The offset (�offsetter�) accounts for the haul
> >> duration and sub sampling factor. There are no extreme outliers in
> >> lat/lon. The model we try to fit is:
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson", niterPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >> We tried several things. We added some
> >> noise to lon and lat, modelled the density instead of using a count with
> >> model offset, and we normalized the explanatory variables. We also
> >> changed several settings (see models below).
> >>
> >>
> >>
> >> Interestingly, we do manage to fit a more
> >> complex model:
> >>
> >> gamm2<-gamm(count~offset(offsetter)+
> >> s(lat,lon,year,dayofyear), random=list(code_tripnr=~1),family="poisson",
> >> correlation = corGaus(0.1, form=~lat + lon))
> >>
> >>
> >>
> >> The models are fitted using mgcv 1.4-1 and
> >> R 2.7.1 on a 64Bits Debian OS.
> >>
> >>
> >>
> >> So there seems to be a convergence problem, correct? And does someone
> >> have an idea what might cause this? Secondly are there some
> >> tricks/solutions. E.g. perhaps we could use the results from the more
> >> complex model (gamm2 above), but I do not know exactly how. All
> >> help/advice would be greatly appreciated.
> >>
> >>
> >>
> >> Kind regards, Geert
> >>
> >>
> >>
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),
> >> random=list(code_tripnr=~1),family="poisson", correlation = corExp(1,
> >> form=~X + Y),nite
> >>
> >> rPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in recalc.corSpatial(object[[i]],
> >> conLin) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>> gamm3<-gamm(count~offset(offsetter)+s(lon,lat,k=c(1,1)),random=list(cod
> >>>e_ tripnr=~1),family="poisson",
> >>
> >> niterPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in lme.formula(fixed = fixed, random
> >> = random, data = data, correlation = correlation, :
> >>
> >> nlminb
> >> problem, convergence error code = 1
> >>
> >>
> >> message = false convergence (8)
> >>
> >> In addition: Warning messages:
> >>
> >> 1: In if (k < M + 1) { :
> >>
> >> the
> >> condition has length> 1 and only the first element will be used
> >>
> >>
> >>
> >>
> >>
> >> .Options$mgcv.vc.logrange=0.001 # we also
> >> tried higher settings
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson", niterPQL=200, control=lmeControl(opt="optim"))
> >>
> >>
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in optim(c(coef(lmeSt)),
> >> function(lmePars) -logLik(lmeSt, lmePars),
> >>
> >>
> >>
> >> initial value in 'vmmin' is not finite
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson", niterPQL=200,control=lmeControl(minAbsParApV
> >>
> >> ar=0.0000000000001))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in recalc.corSpatial(object[[i]],
> >> conLin) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson", niterPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat,k=c(1,1)),random=list(code
> >>_tr ipnr=~1),family="poisson", niterPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in lme.formula(fixed = fixed, random
> >> = random, data = data, correlation = correlation, :
> >>
> >>
> >> nlminb problem, convergence
> >> error code = 1
> >>
> >>
> >> message = false convergence (8)
> >>
> >> In addition: Warning messages:
> >>
> >> 1: In if (k < M + 1) { :
> >>
> >> the
> >> condition has length> 1 and only the first element will be used
> >>
> >> 2: In smooth.construct.tp.smooth.spec(object,
> >> dk$data, dk$knots) :
> >>
> >>
> >> basis dimension, k, increased to minimum possible
> >>
> >>
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat,k=c(8,8)),random=list(code
> >>_tr ipnr=~1),family="poisson", niterPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in lme.formula(fixed = fixed, random
> >> = random, data = data, correlation = correlation, :
> >>
> >>
> >> nlminb problem, convergence
> >> error code = 1
> >>
> >>
> >> message = false convergence (8)
> >>
> >> In addition: Warning messages:
> >>
> >> 1: In if (k < M + 1) { :
> >>
> >> the
> >> condition has length> 1 and only the first element will be used
> >>
> >> 2: In 1:UZ.len : numerical expression has 2
> >> elements: only the first used
> >>
> >> 3: In if (p.rank> ncol(XZ)) p.rank
> >> <- ncol(XZ) :
> >>
> >> the
> >> condition has length> 1 and only the first element will be used
> >>
> >> 4: In 1:p.rank : numerical expression has 2
> >> elements: only the first used
> >>
> >> 5: In if (p.rank < k - j) Xf <- XZU[,
> >> (p.rank + 1):(k - j), drop = FALSE] else Xf <- matrix(0, :
> >>
> >> the
> >> condition has length> 1 and only the first element will be used
> >>
> >> 6: In (p.rank + 1):(k - j) :
> >>
> >>
> >> numerical expression has 2 elements: only the first used
> >>
> >> 7: In 1:p.rank : numerical expression has 2
> >> elements: only the first used
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat,k=c(4,4),fx=T),random=list
> >>(co de_tripnr=~1),family="poisson", niterPQL=200)
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >> In addition: Warning messages:
> >>
> >> 1: In if (k < M + 1) { :
> >>
> >> the
> >> condition has length> 1 and only the first element will be used
> >>
> >> 2: In 1:UZ.len : numerical expression has 2
> >> elements: only the first used
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+te(lon,lat),random=list(code_tripnr=
> >>~1) ,family="poisson", niterPQL=200,control=lmeControl(opt="opti
> >>
> >> m"))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in optim(c(coef(lmeSt)),
> >> function(lmePars) -logLik(lmeSt, lmePars),
> >>
> >>
> >>
> >> initial value in 'vmmin' is not finite
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson", niterPQL=200,control=lmeControl(tolerance=
> >>
> >> 0.00000000000001))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=
> >>>~1 ),family="poisson",
> >>
> >> niterPQL=200,control=lmeControl(niterEM=200))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson",
> >> niterPQL=200,control=lmeControl(msTol=0.00000000000000001))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson",
> >> niterPQL=200,control=lmeControl(.relStep=0.00000000000000000001))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson",
> >> niterPQL=200,control=lmeControl(nlmStepMax=0.00000000000000000001))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson",
> >> niterPQL=200,control=lmeControl(minAbsParApVar=0.0000000000001))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> NA/NaN/Inf in foreign function call (arg 1)
> >>
> >>
> >>
> >>
> >> gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~
> >>1), family="poisson", niterPQL=200, control=lmeControl(returnObject=T))
> >>
> >> Maximum number of PQL iterations: 200
> >>
> >> iteration 1
> >>
> >> iteration 2
> >>
> >> Error in MEestimate(lmeSt, grps) :
> >>
> >>
> >> Singularity in backsolve at level 0, block 1
> >>
> >> In addition: Warning messages:
> >>
> >> 1: In logLik.reStruct(object, conLin) :
> >>
> >>
> >> Singular precision matrix in level -1, block 1
> >>
> >> 2: In logLik.reStruct(object, conLin) :
> >>
> >>
> >> Singular precision matrix in level -1, block 1
> >>
> >> 3: In logLik.reStruct(object, conLin) :
> >>
> >>
> >> Singular precision matrix in level -1, block 1
> >>
> >> 4: In logLik.reStruct(object, conLin) :
> >>
> >>
> >> Singular precision matrix in level -1, block 1
> >>
> >> 5: In logLik.reStruct(object, conLin) :
> >>
> >>
> >> Singular precision matrix in level -1, block 1
> >>
> >> 6: In MEestimate(lmeSt, grps) :
> >>
> >>
> >> Singular precision matrix in level -1, block 1
> >>
> >>
> >> _________________________________________________________________
> >>
> >>
> >> [[alternative HTML version deleted]]
> >
> > --
> >
> >> Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> >> +44 1225 386603 www.maths.bath.ac.uk/~sw283
>
>
> Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> +44 1225 386603  www.maths.bath.ac.uk/~sw283 



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