Hi all,

I'd like to follow up an issue which had already been discussed some weeks
ago
about the pwrss error in the new version of lme4:
https://github.com/lme4/lme4/issues/134
Thanks to Ben Bolker who had worked to solve that problem and implemented
solutions in the release branch in github.

I tried that version from github (lme4_1.0-5) but run again/still in
problems (which I didn't have with the older lm4 version). Again, I can
provide some data (see below) to reproduce following error:
Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in
pwrssUpdate

The error occurs only when I include both predictors and all species:
mod <- glmer(presabs~pred1+pred2+(1|species),family=binomial,data=mydf)

Models with single predictors work:
mod1 <- glmer(presabs~pred1+(1|species),family=binomial,data=mydf)
mod2 <- glmer(presabs~pred2+(1|species),family=binomial,data=mydf)

when I remove species "Rutiilus" the model also works:
mod3 <-
glmer(presabs~pred1+pred2+(1|species),family=binomial,data=mydf[mydf$species!="Rutiilus",])

So there seems and issue with the combinations of that single species and
the two predictors.
However that was working in lme4_0.999999-2 (except for warnings: "glm.fit:
fitted probabilities numerically 0 or 1 occurred ").

I have tried and produced that errors with following setup (sessionInfo()):
R version 3.0.2 (2013-09-25)
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
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
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_1.0-5      Matrix_1.0-14   lattice_0.20-24
loaded via a namespace (and not attached):
[1] grid_3.0.2    MASS_7.3-29   minqa_1.2.1   nlme_3.1-111  Rcpp_0.10.4
splines_3.0.2 tools_3.0.2


This is a subdataset related to the last dataset I provided:
mydf <- structure(list(presabs = c(0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 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, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,
0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,
1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,
1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1), pred1 = c(0, 0, 0,
0, 0, 0, 0, -1.06581410413646e-14, -3.90798504668055e-14, 0,
0, -7.55503272961012e-07, -1.23918653116561e-11, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.66287020335925e-09,
-3.40949791466372e-06,
-6.51330841416725e-14, 0, -2.51450781227902e-05, -8.49098569628712e-13,
0, 0, 0, 0, -9.37369840304629e-13, 31.6318019449206, -1.34415221373042e-05,
-5.11965285219315e-11, -8.13571432824163e-13, 0, 0, -0.0418383037314029,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.504906220938094, 0, 0, 0,
0, 0, -0.000710960113554128, 0, 0, 0, -4.36384262059164e-11,
0, 0, 0, -0.00012072043623973, 0, 0, 0, -4.9631410092843e-12,
0, 0, 0, 0, 0, 0, -7.99238453569578e-09, 13.1926565589372,
-3.40727321849101e-05,
0, 0, 0, 0, 0, 0, 0, -4.44089209850063e-16, -1.32344135650442e-10,
0, 0, -0.000706590307913757, -4.44089210056858e-16, 0, 0, 0,
0, 0, 250.890162494749, -3.46695772777959e-06, -1.03620815679933e-15,
-0.00019606437070874, 0, 0, -0.000480881718964432, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, -0.0333953155798061, 0, 0, 0, 0, 0,
-1.13514187443406e-06,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, -4.01993669019163e-07, 0, -0.000242423728919139, 0, 0,
0, -0.00600591067794465, -9.17736109329326e-10, 0, 0, -7.9844091436243e-06,
-2.15473225373408e-08, -1.9471091405876e-11, 0, 0, 0,
-1.08336450921342e-10,
-0.000220179239376839, 6.00607030684856, -0.00904488403084565,
0, 0, 0, 0, 0, 0, 0, -2.32006312807231e-07, -1.14903686911916e-05,
-6.96381619036401e-10, -3.29869465076627e-12, -0.0109656761716169,
-4.51611525963868e-07, -1.0831335828243e-11, -5.9240806704608e-16,
-1.56211377166926e-10, -1.92415527955347e-14, -9.95973654660531e-08,
2.57455504232716, -0.000208385062152872, -5.93894011388204e-07,
-1.06146348803686e-05, 0, 0, -0.00469194790770366, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, -0.0279245591230761, 0, 0, 0, 0, 8.88178419700125e-16,
-0.000240084274916619, 0, -1.19904086659517e-13, 0, -1.98151944630354e-07,
0, 0, 0, 0, 0, 0, 0, -3.08114911362622e-08, -9.85878045867139e-14,
0, 0, 0, 0, 0, 0, 1.25119783968191, -7.76499226784859e-05, 0,
0, 0, 0, 1.77635683940025e-15, 0, 0, -5.3854920523122e-11,
-2.08027475689931e-07,
0, 0, -0.00672302362720156, -6.70237199386212e-11, 0, 0, 0, 0,
-7.72826247441571e-13, 3.64044358307754, -3.23570893243641e-05,
-7.08197944732092e-11, -0.000455165017106296, 0, 0, -0.0110568963087674,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.254961187249355, 0, 0, 0,
0, 0, -0.000107560915736826), pred2 = c(0, 0, 0.0014525042669731,
0, 7.51525014849321e-05, 0.11107845542179, 1.23744888527069e-06,
1.06581410413646e-14, 0.0903210839328692, 4.52010164619473e-07,
1.41220368798081e-13, 8.2544080076265e-07, 0.00683509679302535,
0.0437399331685944, 0.0128151409171, 4.6746232637564e-17, 0,
0, 0, 0, 0, 0.00446593912302912, 4.44089210056858e-16, 0,
0.00316294553344445,
4.06162226729686e-06, 2.57671162783512, 1.42917005909131,
3.75052031964316e-05,
0.0261751461619117, 6.51330841416725e-14, 0, 0.188802615259352,
8.49098569628712e-13, 0, 0, 0, 0, 9.37369840304629e-13, 0.325468329977678,
1.34415221373042e-05, 5.11965285219315e-11, 8.13571432824163e-13,
0.0100614542954544, 2.51372720311764e-06, 0.0418383037366662,
0.00664029091139595, 4.34056031056444, 0.114499926777513,
0.0543755854149297,
5.55027738130783e-05, 0.572270768297005, 0.722335808556501,
0.000630562212683291,
0.00425318456296784, 0.399675214316342, 1.69203686025139,
0.504906220938094,
0.0105276326711548, 0.984089845041504, 0.0121640256044393,
3.48477563842409e-06,
7.83302836792889e-05, 0.000710960113554128, 1.49184658642745e-06,
0.000483858404202854, 1.00457800053324, 0.01275433757027,
0.158355376371002,
1.645176921789, 0.273568225808851, 0.00636774296647358, 0.207179548984774,
0.0355174152934303, 0.000629968936453094, 0.248755953079858,
0.147889840197777, 0.0920928535123762, 0.02306095696526,
0.00956800048208712,
0.000206955916841571, 7.95474797514347e-09, 7.99238453569578e-09,
0.349582451830059, 3.40727321849101e-05, 0.171234910895596, 0,
0, 0.0210005220715321, 7.15149910562207e-07, 6.39222612039094,
0.0969974429247413, 0.0262521528920125, 0.0224144769337775,
1.28424915206555e-09,
0, 10.3659985117947, 4.44089210056858e-16, 0.00117302087639759,
2.54275640661662e-08, 0.00613070398583964, 6.25150234181645e-06,
0, 6.39475030570469, 3.46695772777959e-06, 1.03620815679933e-15,
0.00019606437070874, 4.98206844899943e-08, 0.147774502554352,
0.0119886826931573, 0.00342059441341787, 0.218511021677141,
0.176173897555737,
0.0935961734062554, 0.14266974633562, 0.19105399746099, 0.156705819021765,
0.0291978234109503, 0.178588271319476, 0.223006307587294,
0.261002450484439,
0.0454245049810824, 0.303159380127293, 0.233924485484663,
0.902624187981687,
0.0760721523656995, 0.00020196745593151, 6.25992370913497e-05,
0, 0, 0, 0, 0, 0.0463503432472789, 0, 0, 2.68418148483846, 0,
0, 1.08710598873249, 0.0862497313374693, 0.471784332386804,
0.148508374477398,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.235892166193388, 0.28832639990565,
0, 0, 0, 0, 0.0223320863875749, 0, 0, 0, 0.0286640280608017,
0, 0, 0, 0, 0, 0, 0, 0, 0.0510079056296746, 0, 1.53757794226149,
0.202087366219562, 0, 0.051562339386531, 3.36756063191996e-08,
1.07684669122003, 0, 0.272040685295554, 0.0847095575635919,
0.354083946574598,
0, 0, 0.399466295790354, 0.103508163185616, 0, 0, 0, 0.00184189728608703,
0.020281568507022, 0.0481857701029478, 0.0246234064078223,
0.240124960042198,
0.0308650361565908, 0.0542121826870412, 0.0318003152789748,
0.0424832898402261,
7.54899302975595e-07, 4.30093354242705e-08, 0.0423012533451277,
0.0834277585347481, 0.0282832714864924, 9.13831285051575e-05,
0.018509755613216, 0.00211996974510939, 0.00659648825714954,
0.000220179239376839, 0.073867989618166, 0.00904488403084565,
0.0408133131715578, 0.0818439209508925, 3.86912724262037e-13,
0.055376198385191, 0.0266441090152101, 0.00487480778455547,
0.0259775733269443,
0.0108004383502749, 0.0663612506835003, 0.223034748570409,
0.0865763472789576,
0.0109656761716169, 4.51611525963868e-07, 0.0590067257008817,
0.00517172604641031, 0.135478763193069, 0.0354219123320718,
0.0133920575242363,
0.0249046871884699, 0.00511876963533897, 5.93894011388204e-07,
1.06146348803686e-05, 1.13227650269977e-06, 0.00195510831958525,
0.0645401034932647, 0.01961048925328, 0.107170307671407,
0.0662793412107673,
0.0780139246258765, 0.25984186821029, 0.0830799972489795,
0.107715192685316,
0.05278549270302, 0.110979289286166, 0.110279337931321, 0.122972451046,
0.0898880806340212, 0.0550182853807459, 0.155717102808187,
0.191805001107261,
0.0618654445364688, 0.0558853904357468, 0.0300981170690902,
0.118331528829454,
0.10490360124789, 0.0012672720961717, 0.134465320589729, 2.726677250178,
0.300043347743973, 0.00082565229604981, 0.0405023747804814,
0.443881726103925,
2.10672674070669e-07, 3.46389583844349e-13, 0.129860290454466,
0.36210457941163, 0.254781128657661, 0.0431366824617401,
0.0296534993628898,
0.132298731704989, 0.0113634376031001, 0, 0.031436635476557,
7.76499226784859e-05, 0.138859370305761, 0.359452308979279,
0.0248607288175478,
0.0720161511988082, 0.151582294418064, 0.000280147526609893,
0.0369209001879534, 0.0248874874898277, 0.0431188345539861,
0.101492841864772,
9.01743532065756e-05, 0.052127525808193, 6.70237199386212e-11,
0.164450497509432, 1.47042312190397e-05, 0.187242450375132,
0.00199499263917846,
0.00784259583203495, 0.081703712703856, 0.405494561565501,
0.0672702074594795,
0.167229220834752, 2.42643831087248e-06, 0.039135028571601,
0.0444871996246505,
0.0300459375618211, 1.0928162538353, 0.28675053380727, 0.951598940296678,
0.726447264705584, 1.78969415215579, 1.04434670455016, 0.15708469738796,
0.458441181438786, 1.29822674736941, 1.857354353469, 0.282726296441298,
0.209720837868035, 1.22087493143776, 0.296177269230267, 0.205011113548231,
0.00225037394007444, 0.206162632105601), species = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label =
c("Anguilla",
"Blicrkna", "Cobienia", "Esoxcius", "Rutiilus"), class = "factor")), .Names
= c("presabs",
"pred1", "pred2", "species"), class = "data.frame", row.names = c(83L,
84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L,
97L, 98L, 99L, 100L, 101L, 102L, 103L, 104L, 105L, 106L, 107L,
108L, 109L, 110L, 111L, 112L, 113L, 114L, 115L, 116L, 117L, 118L,
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131L, 133L, 136L, 138L, 140L, 143L, 144L, 147L, 148L, 150L, 151L,
152L, 154L, 155L, 157L, 161L, 162L, 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,
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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, 245L, 246L, 247L, 248L, 249L, 250L, 251L,
252L, 253L, 254L, 255L, 256L, 257L, 258L, 259L, 260L, 261L, 262L,
263L, 264L, 265L, 266L, 267L, 268L, 269L, 270L, 271L, 272L, 273L,
274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L,
285L, 286L, 287L, 288L, 289L, 290L, 292L, 293L, 295L, 298L, 300L,
302L, 305L, 306L, 309L, 310L, 312L, 313L, 314L, 316L, 317L, 319L,
323L, 324L, 893L, 894L, 895L, 896L, 897L, 898L, 899L, 900L, 901L,
902L, 903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L, 911L, 912L,
913L, 914L, 915L, 916L, 917L, 918L, 919L, 920L, 921L, 922L, 923L,
924L, 925L, 926L, 927L, 928L, 929L, 930L, 931L, 932L, 933L, 934L,
935L, 936L, 937L, 938L, 940L, 941L, 943L, 946L, 948L, 950L, 953L,
954L, 957L, 958L, 960L, 961L, 962L, 964L, 965L, 967L, 971L, 972L,
1217L, 1218L, 1219L, 1220L, 1221L, 1222L, 1223L, 1224L, 1225L,
1226L, 1227L, 1228L, 1229L, 1230L, 1231L, 1232L, 1233L, 1234L,
1235L, 1236L, 1237L, 1238L, 1239L, 1240L, 1241L, 1242L, 1243L,
1244L, 1245L, 1246L, 1247L, 1248L, 1249L, 1250L, 1251L, 1252L,
1253L, 1254L, 1255L, 1256L, 1257L, 1258L, 1259L, 1260L, 1261L,
1262L, 1264L, 1265L, 1267L, 1270L, 1272L, 1274L, 1277L, 1278L,
1281L, 1282L, 1284L, 1285L, 1286L, 1288L, 1289L, 1291L, 1295L,
1296L))


/Johannes


> >
>> > Hi,
>> >
>> > > Thanks for the reproducible example.  Unfortunately, I can't reproduce
>> > > your "pwrssUdate did not converge..." error.
>> > Instead I get another error:
>> > >
>>
>>   I was able to reproduce this.  While the data initially seemed to
>> me to be a bit on the edge (only three species present, nearly complete
>> separation in one of them), I did check -- lme4.0, glmmML, and
>> glmmADMB are all able to handle this case without too much difficulty
>> (although they do give warnings of "glm.fit: fitted probabilities of
>> 0 or 1 occurred").  We will definitely see if we can figure out what
>> the problem is and make lme4 more robust for this case.
>>
>>   Are you willing to have your data available as a test case
>> (in which case they would be available on Github, although not
>> in a very prominent place)?
>>
>
>
>> library(lme4)
>> ## source("radinger_dat.R") ## get data
>> mydf <- transform(mydf,species=droplevels(species))
>> mod <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf)
>> mod2 <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf,
>>               nAGQ=0)
>> mod2 <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf,
>>               nAGQ=0,
>>               control=glmerControl(optimizer="bobyqa"),
>>               verbose=100)
>>
>> library(ggplot2)
>> ggplot(mydf,aes(x=predictor,y=presabs,colour=species))+geom_point()
>> with(mydf,table(species))
>> with(mydf,table(presabs,species,predictor>0))
>> minz <- with(mydf,min(predictor[predictor>0]))
>> library("mgcv")
>> ggplot(mydf,aes(x=log10(predictor+minz/2),y=presabs,colour=species))+
>>     geom_point()+geom_smooth(method="gam",family=binomial)+
>>     facet_wrap(~species)
>>
>> ggplot(mydf,aes(x=predictor,y=presabs,colour=species))+
>>     geom_point()+geom_smooth(method="glm",family=binomial)+
>>     facet_wrap(~species,scale="free_x")
>>
>> lmList(presabs~predictor|species,family=binomial,data=mydf)
>> ## Coefficients:
>> ##         (Intercept)  predictor
>> ## Pungtius -0.39436885 959.554470
>> ## Rutiilus -0.02894044   2.704616
>> ## Salmario  0.94294617   2.866079
>>
>> g1 <- glm(presabs~species+predictor:species-1,family=binomial,data=mydf)
>> summary(g1)
>>
>> library(lme4.0)
>> mod3 <- glmer(presabs~predictor+(1|species),family=binomial,data=mydf)
>>
>> library(glmmADMB)
>> mod4 <-
>> glmmadmb(presabs~predictor+(1|species),family="binomial",data=mydf)
>> summary(mod4)
>>
>> library(glmmML)
>> mod5 <-
>> glmmML(presabs~predictor,cluster=species,family=binomial,data=mydf)
>>
>

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