[R-sig-ME] pwrssUpdate Error with new version of lme4
Steve Walker
steve.walker at utoronto.ca
Mon Sep 23 22:25:01 CEST 2013
Thanks for the reproducible example. Unfortunately, I can't reproduce
your "pwrssUdate did not converge..." error. Instead I get another error:
> library(lme4)
> mod <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf)
Error in (function (fr, X, reTrms, family, nAGQ = 1L, verbose = 0L,
control = glmerControl(), :
c++ exception (unknown reason)
"pwrssUpdate did not converge..." could be related to "c++ exception..."
- I don't know. Before I investigate, would you be able to run your
example and send the output and sessionInfo(). I'd like to confirm that
the example you sent actually generates the "pwrssUpdate did not
converge..." error? If it does, than I'm confused why I can't reproduce
your problem.
Steve
On 2013-09-23 11:37 AM, Johannes Radinger wrote:
> > Hi,
>>
>> I am building binomial (logit, binary response) models with the glmer
>> function of lme4.
>> Today I updated to the new version 1.0-4 and now I get following errors:
>> "pwrssUpdate
>> did not converge in 30 iterations". In the old version the model fit was
>> generally working.
>> So is what can I do to make the models working again?
>
> Sry for not editing the subject line in my last message.
>
> It would definitely help if you could provide more information about
> your model. Better yet, it would be good if you could provide a minimal
> example, including (possibly fake, reduced, or permuted) data, that
> reproduces your problem.
>
> Of course, in this case a minimal example can help. I reduced my dataset
> (however,
> it is probably still to large to be considered as "minimal"). This dataset
> contains now the data to reproduce my problem (which did not result in an
> error before,
> but there were warnings in the old version concerning fitted probabilities
> numerically 0 or 1 occurred).
> Maybe the problem is caused by the high amount of 0 in my predictor
> variable, or the generally
> very small numbers?
>
> Here a reduced (still not minimal) dataframe from dput():
> mydf <- structure(list(presabs = c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
> 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
> 0, 1, 1, 1, 1, 0, 0, 1,
> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1,
> 0, 0, 1, 1, 1, 1, 1, 0,
> 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0,
> 1, 1, 1, 1, 1, 1, 0, 0,
> 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
> 1, 1, 1, 1, 1, 1, 0, 1,
> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
> 1, 1, 1, 0, 1, 1, 1, 1,
> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
> 0, 1, 0, 0, 1, 1, 1, 0,
> 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
> 1, 0, 1, 1, 1, 1, 1, 1,
> 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
> 1, 1, 0, 1, 1, 0, 1, 1,
> 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1),
> species = structure(c(15L,
>
> 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
>
> 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
>
> 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
>
> 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
>
> 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L,
>
> 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
>
> 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
>
> 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
>
> 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
>
> 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 14L, 14L, 14L,
>
> 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
>
> 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
>
> 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
>
> 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
>
> 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L), .Label = c("Anguilla",
>
> "Blicrkna", "Cobienia", "Esoxcius", "Gastatus", "Gobiobio", "Gymnrnua",
>
> "Lampilis", "Lampneri", "Leucatus", "Leucscus", "Percilis", "Phoxinus",
>
> "Pungtius", "Rutiilus", "Salmario", "Tincinca"), class = "factor"),
> predictor = c(1.55459194409222e-06,
> 0.000502333635632635,
> 0, 0, 0, 0, 0.1894962852409, 0, 0, 0,
> 0.000659479921760526,
> 0, 0.139124671977887,
> 0.0731043736035024, 0, 0.009861090746453,
> 0.000214361651891381,
> 8.45898610810779e-09, 8.53544080257065e-09,
> 0, 3.63492128382521e-05, 0,
> 2.96059473371239e-16, 0, 0.0217793996630233,
> 7.5504282582359e-07, 0, 0, 0, 0,
> 1.40835236871551e-09, 0,
> 0, 6.66133815085287e-16,
> 0.00121169750422759, 2.66500284187042e-08,
> 0.00632362770597892,
> 6.50176161124065e-06, 0, 0, 3.6235695051029e-06,
> 1.03620815679933e-15,
> 0.000204976977881918, 5.31823284917628e-08,
> 0, 0.00437266480400345,
> 0.00353879191578555, 0.224832272693897,
> 0.0143667660409079,
> 0.0312212883022758, 0.0999519762519778,
> 0.123662785065311, 0.126072484270801,
> 0.0301337497388396,
> 0.104835220043489, 0.220908714863071,
> 0.18283041155356, 0.0376791196516955,
> 0.312811327061535, 0.240930700934294,
> 0, 0.00761386697236066,
> 0.00021042116578503,
> 6.48205649876751e-05, 0, 0, 0.00110404591663634,
> 0, 0, 0, 0, 0, 0, 0, 0, 0,
> 0.0821150787817579, 0, 0, 0, 0,
> 0, 0, 0, 1.39053213453003e-11, 0, 0,
> 0, 0, 0, 0, 0, 0, 0,
> 2.30926389229566e-14, 0, 0, 0,
> 2.46913600791613e-13, 0, 5.20921972467215e-10,
> 0, 0, 0, 0, 0, 0, 0, 0,
> 0.0010232110152657, 0, 10.4576506875045,
> 0.00413430943155879, 0,
> 0.0269606212027149, 1.42449263002115,
> 4.74551126491593, 0,
> 0.00809544622856606, 2.42681001102513,
> 11.7912865367613, 0.471403099494996,
> 0, 7.79870222642009,
> 0.0129004068089746, 0, 0, 0,
> 0.179611241767248, 0.00512551530859895,
> 0, 0.00311446242707802, 0, 0,
> 0.00114408823853829, 0.00578400165037607,
> 0.0073290285873, 0, 0.183602602695487,
> 0, 0, 0.000942190314064284,
> 0.158057892754982, 0.0024325890448047,
> 0.0150108543495762,
> 0.0487833002331968, 0,
> 0.0265024174880821, 0.0250885544057269,
> 0.00115439533977479,
> 0.424414712029375, 0.103385034404454,
> 0, 0.00605585795996255,
> 0.000628126876048185, 0.00776014791574653,
> 0.0827080275228127,
> 0.0227942603086149, 0.0180785171452129,
> 0.254648827217011, 0.0693236371732553,
> 0, 0.0270094556702531,
> 0.109269153481364,
> 0.00385738346698616, 0.0595728752978175,
> 0.291369347756927,
> 0.00109762425524984, 0.00562884233459116,
> 0, 0, 0.0050984546694437,
> 0.00294569845317838, 0, 0, 0.699686935205101,
> 0, 0, 0, 0, 0.175614680655762, 0, 0,
> 0.00318543944793789,
> 0.0777185091864965, 0, 0,
> 0.22448158312514, 0, 0, 0.0759315294701963,
> 0.0044759638424301)), .Names =
> c("presabs", "species", "predictor"
> ), row.names = c(164L, 165L, 166L,
> 167L, 168L, 169L, 170L, 171L,
> 172L, 173L, 174L,
> 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L,
> 183L, 184L, 185L,
> 186L, 187L, 188L, 189L, 190L, 191L, 192L, 193L,
> 194L, 195L, 196L,
> 197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L,
> 205L, 206L, 207L,
> 208L, 209L, 211L, 212L, 214L, 217L, 219L, 221L,
> 224L, 225L, 228L,
> 229L, 231L, 232L, 233L, 235L, 236L, 238L, 242L,
> 243L, 731L, 732L,
> 733L, 734L, 735L, 736L, 737L, 738L, 739L, 740L,
> 741L, 742L, 743L,
> 744L, 745L, 746L, 747L, 748L, 749L, 750L, 751L,
> 752L, 753L, 754L,
> 755L, 756L, 757L, 758L, 759L, 760L, 761L, 762L,
> 763L, 764L, 765L,
> 766L, 767L, 768L, 769L, 770L, 771L, 772L, 773L,
> 774L, 775L, 776L,
> 778L, 779L, 781L, 784L, 786L, 788L, 791L, 792L,
> 795L, 796L, 798L,
> 799L, 800L, 802L, 803L, 805L, 809L, 810L, 1055L,
> 1056L, 1057L, 1058L,
> 1059L, 1060L, 1061L, 1062L, 1063L, 1064L,
> 1065L, 1066L, 1067L,
> 1068L, 1069L, 1070L, 1071L, 1072L, 1073L,
> 1074L, 1075L, 1076L,
> 1077L, 1078L, 1079L, 1080L, 1081L, 1082L,
> 1083L, 1084L, 1085L,
> 1086L, 1087L, 1088L, 1089L, 1090L, 1091L,
> 1092L, 1093L, 1094L,
> 1095L, 1096L, 1097L, 1098L, 1099L, 1100L,
> 1102L, 1103L, 1105L,
> 1108L, 1110L, 1112L, 1115L, 1116L, 1119L,
> 1120L, 1122L, 1123L,
> 1124L, 1126L, 1127L, 1129L, 1133L, 1134L
> ), class = "data.frame")
>
>
> And the model:
> mod <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf)
>
>
> /johannes
>
> [[alternative HTML version deleted]]
>
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