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 havenât 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 aâs by not such a small value 3) Removing the rows with zero aâs, 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 _________________________________________________________________ Jouw nieuws en entertainment, vind je op MSN.nl! http://nl.msn.com/ [[alternative HTML version deleted]]
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