> Today's Topics:
>
>    1. Re: pwrssUpdate Error with new version of lme4 (Steve Walker)
>
>
>
Hi,


> 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.
>

Here the output from a fresh R session (incl. sessionInfo()):

> library(lme4)
Loading required package: lattice
Loading required package: Matrix
> 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")
> mod <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf)
Error: pwrssUpdate did not converge in 30 iterations
> sessionInfo()
R version 3.0.1 (2013-05-16)
Platform: i686-pc-linux-gnu (32-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
LC_MONETARY=en_US.UTF-8
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=C
LC_NAME=C                  LC_ADDRESS=C
LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base

other attached packages:
[1] lme4_1.0-4      Matrix_1.0-14   lattice_0.20-23

loaded via a namespace (and not attached):
[1] grid_3.0.1    MASS_7.3-29   minqa_1.2.1   nlme_3.1-111  Rcpp_0.10.4
splines_3.0.1 tools_3.0.1


I hope that helps!

Best regards,
Johannes



>
> Steve
>
>

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