White, Charles E WRAIR-Wash DC
2006-Jan-27 21:25 UTC
[R] lme4_0.995-2/Matrix_0.995-4 upgrade introduces error messages (change management)
I'll address two issues. The first is today's error message and the
other is change management for contributed packages on CRAN.
TODAY'S ERROR MESSAGE
I switched from the 0.995-1 versions of lme4 and Matrix to those referenced in
the subject line this afternoon. Prior to using these packages on anything else,
I applied them to code that 'worked' (provided numerical results with no
error messages) under the previous set of packages. Since I can't provide
the data, I realize this post may be of limited usefulness. Rightly or wrongly,
I've regressed my R installation back to the 0.995-1 versions of
lme4/Matrix... so I don't think that I continue to have a problem.
R version 2.2.1, 2005-12-20, i386-pc-mingw32
attached base packages:
[1] "methods" "stats" "graphics"
"grDevices" "utils" "datasets"
[7] "base"
other attached packages:
lme4 lattice Matrix
"0.995-2" "0.12-11" "0.995-4"
> options(show.signif.stars=FALSE)
>
m1a<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date),
+ family=binomial(link='probit'), method='Laplace')
Error in dev.resids(y, mu, weights) : argument wt must be a numeric vector of
length 1 or length 219> # probit doesn't converge
>
m1b<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date),
+ family=binomial, method='Laplace')
Error in dev.resids(y, mu, weights) : argument wt must be a numeric vector of
length 1 or length 219> # logit is overdispersed
>
m1<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date),
+ family=quasibinomial, method='Laplace')
Error in glm.fit(X, Y, weights = weights, offset = offset, family = family, :
NAs in V(mu)> m2<-lmer(cbind(prevented,control.count)~hour+(1|volunteer)+(1|date),
+ family=quasibinomial, method='Laplace')
Error in glm.fit(X, Y, weights = weights, offset = offset, family = family, :
NAs in V(mu)
CHANGE MANAGEMENT
Does CRAN keep old versions of contributed packages someplace? If not, the
strategy I've implemented today is to maintain my own repository of
contributed packages that I use. Stuff happens and change management is good.
Chuck
Charles E. White, Senior Biostatistician, MS
Walter Reed Army Institute of Research
503 Robert Grant Ave., Room 1w102
Silver Spring, MD 20910-1557
301 319-9781
Personal/Professional Site:??
http://users.starpower.net/cwhite571/professional/
Uwe Ligges
2006-Jan-27 21:32 UTC
[R] lme4_0.995-2/Matrix_0.995-4 upgrade introduces error messages (change management)
White, Charles E WRAIR-Wash DC wrote:> I'll address two issues. The first is today's error message and the other is change management for contributed packages on CRAN. > > TODAY'S ERROR MESSAGE > > I switched from the 0.995-1 versions of lme4 and Matrix to those referenced in the subject line this afternoon. Prior to using these packages on anything else, I applied them to code that 'worked' (provided numerical results with no error messages) under the previous set of packages. Since I can't provide the data, I realize this post may be of limited usefulness. Rightly or wrongly, I've regressed my R installation back to the 0.995-1 versions of lme4/Matrix... so I don't think that I continue to have a problem. > > R version 2.2.1, 2005-12-20, i386-pc-mingw32 > > attached base packages: > [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" > [7] "base" > > other attached packages: > lme4 lattice Matrix > "0.995-2" "0.12-11" "0.995-4" > > >>options(show.signif.stars=FALSE) >>m1a<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date), > > + family=binomial(link='probit'), method='Laplace') > Error in dev.resids(y, mu, weights) : argument wt must be a numeric vector of length 1 or length 219 > >># probit doesn't converge >>m1b<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date), > > + family=binomial, method='Laplace') > Error in dev.resids(y, mu, weights) : argument wt must be a numeric vector of length 1 or length 219 > >># logit is overdispersed >>m1<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1|date), > > + family=quasibinomial, method='Laplace') > Error in glm.fit(X, Y, weights = weights, offset = offset, family = family, : > NAs in V(mu) > >>m2<-lmer(cbind(prevented,control.count)~hour+(1|volunteer)+(1|date), > > + family=quasibinomial, method='Laplace') > Error in glm.fit(X, Y, weights = weights, offset = offset, family = family, : > NAs in V(mu) > > CHANGE MANAGEMENT > > Does CRAN keep old versions of contributed packages someplace? If not, the strategy I've implemented today is to maintain my own repository of contributed packages that I use. Stuff happens and change management is good.Yes, old packages are in CRAN/src/contrib/Archive/ You have to compile them from source yourself, though. Uwe Ligges> Chuck > > Charles E. White, Senior Biostatistician, MS > Walter Reed Army Institute of Research > 503 Robert Grant Ave., Room 1w102 > Silver Spring, MD 20910-1557 > 301 319-9781 > Personal/Professional Site: > http://users.starpower.net/cwhite571/professional/ > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
White, Charles E WRAIR-Wash DC
2006-Jan-30 14:03 UTC
[R] lme4_0.995-2/Matrix_0.995-4 upgrade introduces error messages (change management)
I hope I'm not making your life unnecessarily difficult. As I will demonstrate below my signature, my original straight application of lme4_0.995-2/Matrix_0.995-4 is failing without providing any optimization information. For reference, I've provided optimization output from lme4_0.995-1/Matrix_0.995-1. Including the lmer command control=list(PQLmaxIt=0) or control=list(PQLmaxIt=10) produces exactly the same error as when the commands are not included. Chuck Charles E. White, Senior Biostatistician, MS Walter Reed Army Institute of Research 503 Robert Grant Ave., Room 1w102 Silver Spring, MD 20910-1557 301 319-9781 Personal/Professional Site: http://users.starpower.net/cwhite571/professional/> sessionInfo()R version 2.2.1, 2005-12-20, i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" [7] "base" other attached packages: lme4 lattice Matrix "0.995-2" "0.12-11" "0.995-4">m1<-lmer(cbind(Treat.Landed,Control.Landed)~Repellant+Hour.After.Applica tion+(1|Volunteer)+(1|Date), + family=quasibinomial, method='Laplace') Error in glm.fit(X, Y, weights = weights, offset = offset, family family, : NAs in V(mu) ######################################################################## ##> sessionInfo()R version 2.2.1, 2005-12-20, i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" [7] "base" other attached packages: lme4 lattice Matrix "0.995-1" "0.12-11" "0.995-1">m1<-lmer(cbind(Treat.Landed,Control.Landed)~Repellant+Hour.After.Applica tion+(1|Volunteer)+(1|Date), + family=quasibinomial, method='Laplace') EM iterations 0 1643.816 ( 273.179: -0.0214) ( 90.3079: -0.0282) 1 1640.765 ( 366.319: -0.0102) ( 248.902:-0.00169) 2 1640.187 ( 437.442:-0.00561) ( 278.171:-0.000393) 3 1639.953 ( 489.655:-0.00341) ( 285.979:-0.000160) 4 1639.846 ( 527.983:-0.00221) ( 289.280:-9.04e-005) 5 1639.793 ( 556.263:-0.00150) ( 291.184:-5.89e-005) 6 1639.767 ( 577.229:-0.00105) ( 292.439:-4.07e-005) 7 1639.753 ( 592.835:-0.000748) ( 293.311:-2.89e-005) 8 1639.745 ( 604.485:-0.000541) ( 293.934:-2.09e-005) 9 1639.741 ( 613.202:-0.000395) ( 294.386:-1.53e-005) 10 1639.739 ( 619.736:-0.000291) ( 294.717:-1.12e-005) 11 1639.738 ( 624.639:-0.000216) ( 294.961:-8.32e-006) 12 1639.737 ( 628.322:-0.000161) ( 295.142:-6.19e-006) 13 1639.736 ( 631.090:-0.000120) ( 295.277:-4.62e-006) 14 1639.736 ( 633.172:-8.97e-005) ( 295.378:-3.46e-006) 15 1639.736 ( 634.739:-6.72e-005) ( 295.453:-2.59e-006) 0 1639.74: 0.00157545 0.00338463 1 1639.74: 0.00156384 0.00338453 2 1639.74: 0.00156308 0.00338224 3 1639.74: 0.00156394 0.00338218 4 1639.74: 0.00156367 0.00338136 5 1639.74: 0.00156374 0.00338222 6 1639.74: 0.00156367 0.00338219 7 1639.74: 0.00156371 0.00338211 8 1639.74: 0.00156366 0.00338206 9 1639.74: 0.00156370 0.00338199 10 1639.74: 0.00156370 0.00338199 EM iterations 0 1601.856 ( 639.508: 0.00108) ( 295.684: 0.00140) 1 1601.814 ( 620.816:0.000875) ( 267.871:0.000262) 2 1601.802 ( 606.495:0.000663) ( 263.248:6.65e-005) 0 1601.80: 0.00164882 0.00379870 1 1601.79: 0.00181161 0.00380177 2 1601.79: 0.00176152 0.00395670 3 1601.79: 0.00174046 0.00388111 4 1601.79: 0.00176162 0.00387505 5 1601.79: 0.00175271 0.00385493 6 1601.79: 0.00176027 0.00385121 7 1601.79: 0.00175618 0.00385019 8 1601.79: 0.00175420 0.00384200 9 1601.79: 0.00175615 0.00384174 10 1601.79: 0.00175577 0.00383981 11 1601.79: 0.00175577 0.00383981 EM iterations 0 1608.593 ( 569.550:-0.000323) ( 260.429:-0.000384) 1 1608.591 ( 574.148:-0.000245) ( 267.114:-6.56e-005) 2 1608.590 ( 577.686:-0.000179) ( 268.289:-1.70e-005) 0 1608.59: 0.00173104 0.00372732 1 1608.59: 0.00168995 0.00372648 2 1608.59: 0.00170234 0.00368730 3 1608.59: 0.00170328 0.00372838 4 1608.59: 0.00170194 0.00372450 5 1608.59: 0.00170465 0.00372141 6 1608.59: 0.00170173 0.00371852 7 1608.59: 0.00170315 0.00371466 8 1608.59: 0.00170267 0.00371666 9 1608.59: 0.00170246 0.00371667 10 1608.59: 0.00170255 0.00371648 EM iterations 0 1608.661 ( 587.354:-6.52e-006) ( 269.072:-3.50e-006) 1 1608.661 ( 587.452:-4.87e-006) ( 269.135:-7.21e-007) 2 1608.661 ( 587.525:-3.58e-006) ( 269.148:-2.53e-007) 0 1608.66: 0.00170206 0.00371543 1 1608.66: 0.00170148 0.00371542 2 1608.66: 0.00170148 0.00371524 3 1608.66: 0.00170148 0.00371524 4 1608.66: 0.00170148 0.00371524 EM iterations 0 1608.660 ( 587.724:-1.09e-008) ( 269.162:5.68e-008) 1 1608.660 ( 587.724:-5.92e-009) ( 269.161:8.25e-009) 2 1608.660 ( 587.724:-4.02e-009) ( 269.161:1.07e-009) 0 1608.66: 0.00170148 0.00371525 1 1608.66: 0.00170148 0.00371525 2 1608.66: 0.00170148 0.00371525 EM iterations 0 1608.660 ( 587.725:2.30e-010) ( 269.161:4.40e-010) 1 1608.660 ( 587.725:1.83e-010) ( 269.161:7.32e-011) 2 1608.660 ( 587.725:1.36e-010) ( 269.161:1.65e-011) 0 1608.66: 0.00170148 0.00371525 1 1608.66: 0.00170148 0.00371525 0 11444.3: -1.57468 -0.114374 0.0891461 0.295675 0.322676 -0.0819240 0.0613226 -0.278625 0.252676 0.297048 0.00170148 0.00371525 1 10461.4: -1.57468 -0.114375 0.0891456 0.295675 0.322677 -0.0819245 0.0613221 -0.278625 0.252676 0.297048 0.991395 0.146916 2 10453.7: -1.57501 -0.118004 0.0914816 0.325860 0.316566 -0.101131 0.0995624 -0.273603 0.254018 0.290755 0.987977 0.148760 3 10452.4: -1.57627 -0.106030 0.110693 0.344082 0.324971 -0.0605686 0.106017 -0.267820 0.245485 0.293816 0.976769 0.154694 4 10451.5: -1.57797 -0.0856623 0.117621 0.334970 0.344039 -0.0620508 0.146502 -0.274762 0.257380 0.289187 0.968650 0.161734 5 10450.2: -1.57831 -0.0912595 0.116721 0.344484 0.342080 -0.0541054 0.139780 -0.273456 0.253567 0.291741 0.966484 0.162502 6 10450.1: -1.58093 -0.0960249 0.120939 0.348483 0.333461 -0.0497757 0.138781 -0.271218 0.250089 0.293405 0.960659 0.169695 7 10449.8: -1.58338 -0.0947018 0.111567 0.349242 0.340198 -0.0491439 0.142989 -0.272130 0.253299 0.291556 0.953538 0.175865 8 10449.7: -1.58601 -0.0918766 0.121701 0.342860 0.342149 -0.0469333 0.143516 -0.272566 0.251350 0.294516 0.946432 0.181555 9 10449.6: -1.58943 -0.0910486 0.119831 0.352018 0.337230 -0.0454451 0.140744 -0.272584 0.256178 0.290521 0.939746 0.188275 10 10449.5: -1.59166 -0.0935204 0.116089 0.350666 0.341477 -0.0510304 0.145357 -0.270167 0.247932 0.296975 0.933589 0.191757 11 10449.4: -1.59447 -0.0957850 0.120865 0.343099 0.343630 -0.0473610 0.143548 -0.269864 0.255472 0.290163 0.927840 0.195228 12 10449.1: -1.59658 -0.0901759 0.115450 0.350433 0.337106 -0.0458197 0.142501 -0.272300 0.252706 0.296086 0.921275 0.197706 13 10449.0: -1.60106 -0.0990970 0.119617 0.350897 0.341253 -0.0521281 0.143267 -0.269335 0.253346 0.294103 0.914170 0.202652 14 10448.9: -1.60360 -0.0884343 0.118272 0.344260 0.339332 -0.0487273 0.139916 -0.268830 0.255000 0.292972 0.906302 0.204724 15 10448.8: -1.60708 -0.0952676 0.116544 0.350083 0.341797 -0.0438318 0.142868 -0.273987 0.255999 0.298785 0.898871 0.208172 16 10448.6: -1.61004 -0.0936384 0.119330 0.347368 0.338683 -0.0502022 0.147287 -0.265043 0.253930 0.293356 0.891785 0.209803 17 10448.4: -1.61572 -0.0922092 0.119692 0.348542 0.342165 -0.0453877 0.138443 -0.265999 0.256307 0.294703 0.883089 0.215247 18 10448.4: -1.61897 -0.0915042 0.119826 0.346438 0.340360 -0.0538914 0.143168 -0.273599 0.260039 0.300747 0.876663 0.215492 19 10448.1: -1.62170 -0.0959069 0.114654 0.350710 0.339778 -0.0497085 0.142583 -0.264006 0.254848 0.298294 0.869750 0.215881 20 10448.0: -1.62425 -0.0925439 0.121244 0.342900 0.337405 -0.0463290 0.142340 -0.266442 0.261394 0.295969 0.861491 0.216977 21 10447.8: -1.63033 -0.0931746 0.119288 0.346826 0.344990 -0.0511664 0.144844 -0.264393 0.258442 0.302008 0.853196 0.217943 22 10447.7: -1.63145 -0.0916493 0.118219 0.352653 0.337064 -0.0455886 0.138990 -0.264261 0.263362 0.297954 0.845762 0.217530 23 10447.4: -1.63584 -0.0963003 0.117473 0.344293 0.334583 -0.0471378 0.145940 -0.261730 0.260632 0.302341 0.838119 0.219307 24 10447.3: -1.63779 -0.0939463 0.112834 0.349643 0.342489 -0.0464460 0.141193 -0.262403 0.264623 0.301302 0.828109 0.218743 25 10447.1: -1.64064 -0.0902682 0.123597 0.349856 0.343063 -0.0531242 0.139255 -0.260173 0.263578 0.303512 0.820044 0.219590 26 10446.8: -1.64322 -0.0935995 0.115354 0.350843 0.338844 -0.0474707 0.141749 -0.260037 0.264904 0.305199 0.809290 0.219342 27 10446.6: -1.64619 -0.0945572 0.119069 0.342510 0.338353 -0.0456821 0.147251 -0.257505 0.267071 0.304463 0.798134 0.219737 28 10446.3: -1.64999 -0.0947331 0.118412 0.349323 0.341641 -0.0476797 0.140415 -0.256079 0.268139 0.307440 0.786893 0.221035 29 10446.2: -1.65084 -0.0886278 0.118513 0.347099 0.337841 -0.0542178 0.146136 -0.256303 0.268253 0.309310 0.775711 0.219820 30 10445.8: -1.65368 -0.0922684 0.119903 0.347534 0.339643 -0.0467803 0.141972 -0.253972 0.271075 0.309212 0.763495 0.220808 31 10445.7: -1.65432 -0.0934434 0.114649 0.351249 0.341609 -0.0511498 0.147058 -0.254179 0.267805 0.313519 0.751625 0.219946 32 10445.3: -1.65655 -0.0926586 0.119584 0.348001 0.339997 -0.0492817 0.143213 -0.252695 0.272859 0.310855 0.738776 0.221235 33 10445.1: -1.65724 -0.0930273 0.115031 0.351075 0.341181 -0.0465371 0.141509 -0.252119 0.268540 0.315546 0.725421 0.220247 34 10444.7: -1.65927 -0.0927543 0.118641 0.348636 0.340453 -0.0497414 0.144647 -0.251144 0.273531 0.312746 0.711868 0.221569 35 10444.4: -1.66014 -0.0927254 0.117734 0.349532 0.340062 -0.0444686 0.139415 -0.250910 0.272214 0.315254 0.697846 0.220846 36 10441.9: -1.69136 -0.104869 0.114555 0.361175 0.357164 -0.0599348 0.140898 -0.232009 0.280566 0.334682 0.511023 0.248880 37 10436.0: -1.70375 -0.0828958 0.116911 0.349359 0.350304 -0.0512119 0.156769 -0.216600 0.296166 0.352977 0.320550 0.255465 38 10434.6: -1.70384 -0.0880833 0.121505 0.352662 0.345165 -0.0429543 0.146445 -0.218435 0.306303 0.345228 0.316458 0.254844 39 10434.2: -1.70422 -0.0934200 0.122745 0.347648 0.341108 -0.0496339 0.146789 -0.218789 0.304552 0.349584 0.299201 0.252209 40 10433.7: -1.70444 -0.0925859 0.115720 0.350862 0.340392 -0.0469562 0.141048 -0.216638 0.310348 0.347099 0.282092 0.249917 41 10433.3: -1.70446 -0.0931099 0.118574 0.346919 0.339547 -0.0516847 0.143506 -0.213742 0.309185 0.352231 0.263546 0.246707 42 10432.6: -1.70471 -0.0941059 0.113513 0.351775 0.338769 -0.0495017 0.139931 -0.208146 0.319561 0.356341 0.225173 0.237935 43 10432.4: -1.70797 -0.0937163 0.121158 0.343195 0.343718 -0.0530556 0.140490 -0.193316 0.328469 0.374068 0.205060 0.213949 44 10432.2: -1.71930 -0.0944377 0.117711 0.350098 0.336539 -0.0507862 0.141410 -0.175205 0.348005 0.387963 0.197098 0.190250 45 10432.1: -1.72151 -0.0949748 0.116203 0.344533 0.342755 -0.0494401 0.137980 -0.173972 0.351927 0.392206 0.197160 0.187444 46 10432.1: -1.72269 -0.0948574 0.117125 0.346960 0.339561 -0.0515517 0.139812 -0.171871 0.353255 0.393072 0.196681 0.187603 47 10432.1: -1.72279 -0.0949262 0.117043 0.347170 0.339564 -0.0509965 0.139315 -0.171544 0.353063 0.393329 0.196471 0.187595 48 10432.1: -1.72306 -0.0947929 0.117129 0.347058 0.339491 -0.0511342 0.139607 -0.171074 0.353431 0.393525 0.195959 0.187574 49 10432.1: -1.72379 -0.0949714 0.116977 0.347264 0.339587 -0.0507304 0.139303 -0.170283 0.354277 0.394605 0.195653 0.187622 50 10432.1: -1.73745 -0.0949212 0.117176 0.346877 0.338784 -0.0514425 0.139079 -0.154894 0.371388 0.410965 0.196248 0.187693 51 10432.0: -1.74347 -0.0950768 0.116443 0.347248 0.340567 -0.0494151 0.141337 -0.145484 0.376406 0.418066 0.196017 0.187831 52 10432.0: -1.74954 -0.0940385 0.118101 0.347600 0.340126 -0.0478509 0.143164 -0.139359 0.385729 0.424524 0.196150 0.187119 53 10432.0: -1.75110 -0.0936333 0.117473 0.347457 0.340236 -0.0495394 0.140504 -0.138277 0.385992 0.426828 0.195005 0.188405 54 10432.0: -1.75362 -0.0954679 0.116741 0.346808 0.339022 -0.0500229 0.140646 -0.136244 0.388144 0.427714 0.194715 0.189040 55 10432.0: -1.75554 -0.0949921 0.117023 0.347058 0.339582 -0.0496439 0.141035 -0.134474 0.389739 0.430382 0.194467 0.186809 56 10432.0: -1.75591 -0.0944204 0.117272 0.347378 0.339771 -0.0498677 0.140480 -0.133556 0.391030 0.430849 0.194460 0.186355 57 10432.0: -1.75622 -0.0945095 0.117389 0.347319 0.339824 -0.0499044 0.140630 -0.132562 0.391734 0.432105 0.194408 0.187217 58 10432.0: -1.75686 -0.0947003 0.117059 0.347200 0.339619 -0.0495625 0.140879 -0.131663 0.392896 0.432997 0.194223 0.187654 59 10432.0: -1.75778 -0.0947472 0.117253 0.347206 0.339628 -0.0495707 0.140869 -0.130626 0.393636 0.433921 0.194453 0.186924 60 10432.0: -1.75877 -0.0944993 0.117209 0.347352 0.339856 -0.0498309 0.140826 -0.129775 0.394661 0.434882 0.194274 0.186688 61 10432.0: -1.75948 -0.0944810 0.117426 0.347291 0.339689 -0.0496809 0.140634 -0.128936 0.395592 0.435697 0.194265 0.187723 62 10432.0: -1.75997 -0.0947607 0.117194 0.347261 0.339698 -0.0495161 0.140802 -0.127874 0.396307 0.436611 0.194448 0.186709 63 10432.0: -1.75997 -0.0946737 0.117224 0.347231 0.339640 -0.0496096 0.140894 -0.127978 0.396412 0.436636 0.194408 0.186713 64 10432.0: -1.76005 -0.0946805 0.117222 0.347294 0.339677 -0.0495418 0.140844 -0.127949 0.396448 0.436763 0.194315 0.186808 65 10432.0: -1.76015 -0.0946524 0.117252 0.347272 0.339682 -0.0495778 0.140898 -0.127841 0.396564 0.436825 0.194276 0.186895 66 10432.0: -1.76026 -0.0946463 0.117233 0.347280 0.339677 -0.0495408 0.140861 -0.127736 0.396679 0.436936 0.194229 0.186908 67 10432.0: -1.76037 -0.0946545 0.117245 0.347265 0.339681 -0.0495587 0.140902 -0.127618 0.396777 0.437052 0.194202 0.186926 68 10432.0: -1.76046 -0.0946495 0.117251 0.347297 0.339682 -0.0495406 0.140898 -0.127506 0.396928 0.437150 0.194192 0.186917 69 10432.0: -1.76055 -0.0946422 0.117237 0.347264 0.339676 -0.0495445 0.140893 -0.127380 0.397023 0.437290 0.194186 0.186930 70 10432.0: -1.76066 -0.0946661 0.117261 0.347292 0.339685 -0.0495541 0.140922 -0.127271 0.397146 0.437395 0.194167 0.186931 71 10432.0: -1.76090 -0.0946560 0.117228 0.347271 0.339666 -0.0495355 0.140895 -0.127036 0.397390 0.437615 0.194173 0.186938 72 10432.0: -1.76094 -0.0946313 0.117272 0.347291 0.339721 -0.0495014 0.140922 -0.126762 0.397597 0.437908 0.194168 0.187017 73 10432.0: -1.76117 -0.0946427 0.117274 0.347272 0.339696 -0.0495377 0.140953 -0.126576 0.397825 0.438082 0.194202 0.186803 74 10432.0: -1.76158 -0.0946616 0.117253 0.347316 0.339677 -0.0495179 0.140918 -0.126503 0.397935 0.438176 0.194094 0.186901 75 10432.0: -1.76140 -0.0946345 0.117251 0.347299 0.339693 -0.0495395 0.140916 -0.126245 0.398162 0.438420 0.194118 0.186986 76 10432.0: -1.76141 -0.0946363 0.117267 0.347278 0.339688 -0.0495374 0.140918 -0.126244 0.398163 0.438431 0.194115 0.186983 77 10432.0: -1.76143 -0.0946396 0.117255 0.347286 0.339689 -0.0495327 0.140920 -0.126237 0.398173 0.438435 0.194114 0.186975 78 10432.0: -1.76147 -0.0946394 0.117259 0.347277 0.339683 -0.0495314 0.140928 -0.126219 0.398188 0.438453 0.194112 0.186960 79 10432.0: -1.76169 -0.0946427 0.117253 0.347281 0.339694 -0.0495116 0.140935 -0.126132 0.398263 0.438534 0.194134 0.186893 80 10432.0: -1.76166 -0.0946452 0.117262 0.347287 0.339683 -0.0495108 0.140937 -0.125985 0.398428 0.438681 0.194128 0.186879 81 10432.0: -1.76189 -0.0946304 0.117263 0.347296 0.339691 -0.0495325 0.140936 -0.125929 0.398483 0.438741 0.194137 0.186972 82 10432.0: -1.76190 -0.0946413 0.117247 0.347281 0.339689 -0.0495284 0.140929 -0.125781 0.398617 0.438904 0.194099 0.186915 83 10432.0: -1.76201 -0.0946397 0.117254 0.347278 0.339693 -0.0494968 0.140946 -0.125642 0.398758 0.439026 0.194118 0.186973 84 10432.0: -1.76218 -0.0946350 0.117265 0.347288 0.339688 -0.0495205 0.140928 -0.125534 0.398871 0.439121 0.194086 0.186879 85 10432.0: -1.76226 -0.0946434 0.117254 0.347285 0.339675 -0.0495004 0.140968 -0.125402 0.399021 0.439273 0.194094 0.186884 86 10432.0: -1.76230 -0.0946341 0.117261 0.347287 0.339686 -0.0494978 0.140955 -0.125331 0.399077 0.439348 0.194119 0.186884 87 10432.0: -1.76230 -0.0946341 0.117261 0.347287 0.339686 -0.0494978 0.140955 -0.125331 0.399077 0.439348 0.194119 0.186884>-----Original Message----- From: Douglas Bates [mailto:dmbates at gmail.com] Sent: Friday, January 27, 2006 6:33 PM To: White, Charles E WRAIR-Wash DC Subject: Re: lme4_0.995-2/Matrix_0.995-4 upgrade introduces error messages (change management) Sorry to hear of the difficulties, Charles. One thing to try is to turn on the verbose output so fit your models after setting options(verbose=TRUE) Another thing that may be interesting to try is to go to optimization of the Laplace approximation deviance directly without doing any PQL iterations. My theory has been that the PQL iterations help to stabilize the optimization process but it appears that sometimes they do more harm than good. Can you let me know what the verbose output shows? The thing to watch for is what I call "ping-ponging" of the PQL iterations. One set of iterations converges to one optimum that determines weights that send it to another optimum that determines weights that sends it back to the original optimum. On 1/27/06, White, Charles E WRAIR-Wash DC <charles.edwin.white at us.army.mil> wrote:> I'll address two issues. The first is today's error message and theother is change management for contributed packages on CRAN.> > TODAY'S ERROR MESSAGE > > I switched from the 0.995-1 versions of lme4 and Matrix to thosereferenced in the subject line this afternoon. Prior to using these packages on anything else, I applied them to code that 'worked' (provided numerical results with no error messages) under the previous set of packages. Since I can't provide the data, I realize this post may be of limited usefulness. Rightly or wrongly, I've regressed my R installation back to the 0.995-1 versions of lme4/Matrix... so I don't think that I continue to have a problem.> > R version 2.2.1, 2005-12-20, i386-pc-mingw32 > > attached base packages: > [1] "methods" "stats" "graphics" "grDevices" "utils""datasets"> [7] "base" > > other attached packages: > lme4 lattice Matrix > "0.995-2" "0.12-11" "0.995-4" > > > options(show.signif.stars=FALSE) > >m1a<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1 |date),> + family=binomial(link='probit'), method='Laplace') > Error in dev.resids(y, mu, weights) : argument wt must be a numericvector of length 1 or length 219> > # probit doesn't converge > >m1b<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1 |date),> + family=binomial, method='Laplace') > Error in dev.resids(y, mu, weights) : argument wt must be a numericvector of length 1 or length 219> > # logit is overdispersed > >m1<-lmer(cbind(prevented,control.count)~repellant+hour+(1|volunteer)+(1| date),> + family=quasibinomial, method='Laplace') > Error in glm.fit(X, Y, weights = weights, offset = offset, family family, : > NAs in V(mu) > > m2<-lmer(cbind(prevented,control.count)~hour+(1|volunteer)+(1|date), > + family=quasibinomial, method='Laplace') > Error in glm.fit(X, Y, weights = weights, offset = offset, family family, : > NAs in V(mu) > > CHANGE MANAGEMENT > > Does CRAN keep old versions of contributed packages someplace? If not,the strategy I've implemented today is to maintain my own repository of contributed packages that I use. Stuff happens and change management is good.> > Chuck > > Charles E. White, Senior Biostatistician, MS > Walter Reed Army Institute of Research > 503 Robert Grant Ave., Room 1w102 > Silver Spring, MD 20910-1557 > 301 319-9781 > Personal/Professional Site: > http://users.starpower.net/cwhite571/professional/ > >