Hi everyone,
I am working with GLMM, using call lmer() in lme4 library. The response variable (u_d) is binomial and differentiate used(1) and available(0) sites. Food, cover, and density of roads are measured at each of those sites. The dataset contains information on repeated measures taken on 18 individuals followed in two study periods (10840 lines for period 0, 11426 lines for period 1).

When I try to run this model:
Mod.0<-lmer(u_d~Food+Cover+Roads+(Roads|ANIMAL),data=dat[dat$Period==0,], family=binomial, verbose=TRUE, REML=TRUE, na.action=na.omit)
It works well for period 0, but fails to converge for period 1.
Mod.1<- lmer(u_d~Food+Cover+Roads+(Roads|ANIMAL),data=dat[dat$Period==1,], family=binomial, verbose=TRUE, REML=TRUE, na.action=na.omit)

I have read previous post on similar topics and have consequently tried several things (scaling of data, removing or adding some variables, specifying starting values, etc.) As soon as I enter the "Cover" variable, the model Mod.1 gives the following error message:
In mer_finalize(ans) : false convergence (8)
The range of values for Cover, Food, and Roads are similar for the two periods and both models converge well if I use other random effects structures (eg: (1|ANIMAL), (Cover|ANIMAL)). But the best random structure effects is to include (Roads|ANIMAL) as the animals are very variables in their selection for roads.
Does anyone has an idea of what can be happening???
Thanks if anyone can help with this matter,
Geraldine

Here are the outputs from the two models and also a summary of my dataset and sessionInfo():

> sessionInfo()
R version 2.14.0 (2011-10-31)
Platform: x86_64-pc-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=Norwegian (Bokmål)_Norway.1252  LC_CTYPE=Norwegian (Bokmål)_Norway.1252    LC_MONETARY=Norwegian (Bokmål)_Norway.1252 LC_NUMERIC=C                               LC_TIME=Norwegian (Bokmål)_Norway.1252

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

other attached packages:
[1] survival_2.36-10 lme4_0.999375-42 Matrix_1.0-1     lattice_0.20-6

loaded via a namespace (and not attached):
[1] grid_2.14.0   nlme_3.1-103  stats4_2.14.0

> summary(dat)
u_d            Food           Cover              Roads                 S_Food.V1                    S_Cover.V1                   S_Roads.V1              ANIMAL          Period
 0:11133   Min.   :1.000   Min.   :-2.56541   Min.   :0.0000   Min.   :-2.61242520684e+00   Min.   :-3.51573874957e+00   Min.   :-1.30702356682e+00   L402004: 1606   Min.   :0.0000
 1:11133   1st Qu.:3.000   1st Qu.:-0.18486   1st Qu.:0.2415   1st Qu.:-7.88482787843e-01   1st Qu.:-3.09042193553e-01   1st Qu.:-7.39150363180e-01   L222003: 1604   1st Qu.:0.0000
           Median :4.000   Median : 0.22000   Median :0.4426   Median : 1.23488421658e-01   Median : 2.36324773378e-01   Median :-2.66051892226e-01   L442005: 1596   Median :1.0000
           Mean   :3.865   Mean   : 0.04456   Mean   :0.5557   Mean   : 3.69386045293e-17   Mean   :-1.71441696237e-17   Mean   :-3.47907067051e-17   L402005: 1594   Mean   :0.5132
           3rd Qu.:5.000   3rd Qu.: 0.62487   3rd Qu.:0.7776   3rd Qu.: 1.03545963116e+00   3rd Qu.: 7.81690393268e-01   3rd Qu.: 5.21820195235e-01   L502005: 1548   3rd Qu.:1.0000
           Max.   :5.000   Max.   : 1.81514   Max.   :2.2732   Max.   : 1.03545963116e+00   Max.   : 2.38503934480e+00   Max.   : 4.03922235946e+00   L122004: 1436   Max.   :1.0000
                                                                                                                                                      (Other):12882


##MODEL 0
> Mod.0<-lmer(u_d~Food+Cover+Roads+(Roads|ANIMAL),data=dat[dat$Period==0,], family=binomial, verbose=TRUE, REML=TRUE, na.action=na.omit)
  0:     14811.635: 0.0665436 0.0973772 0.000000 -0.819747 0.206481 0.115741 0.0413144
  1:     14533.282: 0.172069  1.08412 -0.0332653 -0.826242 0.284964 0.0902239 -0.0438463
  2:     14246.569: 0.844125  1.78276 -0.0624452 -0.870101 0.0573043 0.0565012 -0.110876
  3:     14179.213: 0.873637  1.78603 -0.0709471 -0.865052 0.268499 0.101458 -0.111741
  4:     14164.122: 0.990301  1.90391 -0.125533 -0.871091 0.185648 0.201359 -0.127180
  5:     14149.610:  1.11006  2.00227 -0.183456 -0.877154 0.211007 0.0622019 -0.140858
  6:     14137.411:  1.23048  2.11686 -0.293072 -0.892110 0.203558 0.147230 -0.162063
  7:     14131.888:  1.34386  2.16835 -0.457772 -0.917694 0.185891 0.0895825 -0.188165
  8:     14129.336:  1.35063  2.16764 -0.462607 -0.918185 0.214793 0.110842 -0.188823
  9:     14129.170:  1.36614  2.16599 -0.473633 -0.920635 0.188166 0.127424 -0.190572
10:     14127.532:  1.37766  2.15245 -0.496335 -0.925523 0.210049 0.123596 -0.193532
11:     14126.692:  1.39659  2.14077 -0.521653 -0.931241 0.197036 0.121030 -0.197138
12:     14121.246:  1.46288  1.94837 -0.771383 -0.990641 0.215155 0.0898077 -0.230448
13:     14115.273:  1.49343  1.66875 -0.930151 -1.03811 0.202395 0.133598 -0.262873
14:     14112.631:  1.54589  1.36249 -1.01924 -1.08923 0.221187 0.119319 -0.308247
15:     14112.278:  1.58713  1.28749 -1.03283 -1.16096 0.190752 0.122992 -0.400425
16:     14110.042:  1.66476  1.39349 -1.06643 -1.14164 0.208762 0.126483 -0.455126
17:     14109.599:  1.73936  1.35215 -1.05095 -1.03347 0.212004 0.123277 -0.401667
18:     14108.477:  1.97467  1.42094 -1.13317 -1.11425 0.218156 0.115004 -0.523629
19:     14108.457:  1.97554  1.42018 -1.13259 -1.11414 0.199216 0.124447 -0.523478
20:     14108.147:  1.97596  1.41991 -1.13226 -1.11371 0.209766 0.125114 -0.523161
21:     14108.125:  1.98295  1.41590 -1.12597 -1.11316 0.207504 0.125930 -0.521858
22:     14108.088:  1.99516  1.41266 -1.11199 -1.12217 0.210324 0.126030 -0.524199
23:     14108.020:  2.02749  1.37042 -1.13664 -1.12798 0.211451 0.128838 -0.476453
24:     14107.903:  2.08822  1.37258 -1.11376 -1.13872 0.207728 0.127783 -0.438873
25:     14107.899:  2.11338  1.35847 -1.10750 -1.08175 0.208827 0.122272 -0.478997
26:     14107.894:  2.12549  1.33244 -1.09433 -1.09193 0.208574 0.125184 -0.497152
27:     14107.883:  2.13924  1.35526 -1.09207 -1.10260 0.208764 0.127748 -0.472579
28:     14107.876:  2.12755  1.35830 -1.09642 -1.12532 0.208618 0.126621 -0.445061
29:     14107.876:  2.12124  1.36418 -1.10227 -1.10148 0.208345 0.125573 -0.476270
30:     14107.875:  2.12412  1.36111 -1.09979 -1.11311 0.208595 0.125708 -0.460340
31:     14107.875:  2.12432  1.36065 -1.09945 -1.11543 0.208564 0.125878 -0.457684
32:     14107.875:  2.12441  1.36066 -1.09944 -1.11520 0.208584 0.125832 -0.457831
33:     14107.875:  2.12439  1.36067 -1.09945 -1.11517 0.208583 0.125833 -0.457883
>
> summary(Mod.0)
Generalized linear mixed model fit by the Laplace approximation
Formula: u_d ~ Food + Cover + Roads + (Roads | ANIMAL)
   Data: dat[dat$Period == 0, ]
   AIC   BIC logLik deviance
14122 14173  -7054    14108
Random effects:
Groups Name        Variance Std.Dev. Corr
 ANIMAL (Intercept) 4.5130   2.1244
        Roads       7.3067   2.7031   -0.864
Number of obs: 10840, groups: ANIMAL, 18

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.11517    0.51056  -2.184   0.0289 *
Food         0.20858    0.01950  10.697  < 2e-16 ***
Cover        0.12583    0.02871   4.382 1.17e-05 ***
Roads       -0.45844    0.64701  -0.709   0.4786
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr) Food   Cover
Food  -0.136
Cover -0.015 -0.044
Roads -0.850 -0.010  0.023

##MODEL 1
> Mod.1<-lmer(u_d~Food+Cover+Roads+(Roads|ANIMAL),data=dat[dat$Period==1,], family=binomial, verbose=TRUE, REML=TRUE, na.action=na.omit)
  0:     15090.911: 0.0648147 0.0906584 0.000000 -1.81064 0.440158 0.290782 0.117974
  1:     13720.786: 0.303583  1.05567 -0.0243967 -1.84249 0.364316 0.262529 0.0581768
  2:     13318.275:  1.24104  1.19029 -0.0784673 -1.81083 0.677665 0.233561 0.0481290
  3:     13138.203:  1.27168  1.29539 -0.128926 -1.81946 0.401675 0.276267 0.0417452
  4:     13059.562:  1.34599  1.52839 -0.278995 -1.82679 0.500652 0.259051 0.0290759
  5:     13032.950:  1.39464  1.62224 -0.346505 -1.83608 0.351422 0.281003 0.0210316
  6:     12996.286:  1.46504  1.67078 -0.467307 -1.86163 0.475994 0.265618 0.00313766
  7:     12953.378:  1.65259  1.67744 -0.773184 -1.96013 0.365922 0.262846 -0.0595510
  8:     12939.555:  1.71974  1.70472 -0.857367 -2.00461 0.484634 0.324617 -0.0879240
  9:     12925.408:  1.77892  1.74154 -0.897353 -2.06953 0.415213 0.198840 -0.129387
10:     12909.803:  1.83737  1.83193 -0.966885 -2.08439 0.450827 0.321121 -0.142281
11:     12905.833:  1.90161  1.92310 -1.02991 -2.10582 0.361071 0.232549 -0.158382
12:     12903.257:  1.96747  2.00291 -1.06269 -2.13447 0.489285 0.172315 -0.177383
13:     12887.657:  1.97813  2.01753 -1.07144 -2.13910 0.414931 0.220204 -0.180615
14:     12882.553:  2.00852  2.05507 -1.09717 -2.14796 0.455819 0.278692 -0.187469
15:     12878.414:  2.05582  2.09986 -1.10959 -2.17905 0.406358 0.272827 -0.206649
16:     12872.882:  2.10672  2.15075 -1.11858 -2.20933 0.445119 0.259294 -0.225337
17:     12865.580:  2.22176  2.24884 -1.12045 -2.29030 0.413352 0.252447 -0.274130
18:     12857.596:  2.46256  2.32613 -1.06561 -2.50445 0.455254 0.230351 -0.401920
19:     12854.002:  2.57797  2.20402 -1.38532 -2.48844 0.434432 0.245050 -0.409308
20:     12852.181:  2.59427  2.24174 -1.38585 -2.49486 0.447291 0.294331 -0.414076
21:     12851.158:  2.61224  2.28596 -1.38503 -2.50135 0.419258 0.259490 -0.419214
22:     12849.119:  2.61241  2.33653 -1.38217 -2.47239 0.446470 0.266604 -0.406905
23:     12847.710:  2.62469  2.39301 -1.36764 -2.45493 0.428880 0.279292 -0.402520
24:     12842.745:  2.66773  2.63496 -1.29349 -2.34739 0.446041 0.292039 -0.378976
25:     12837.734:  2.83766  2.71647 -1.27420 -2.51245 0.448135 0.286772 -0.502639
26:     12836.313:  3.45109  3.21999 -1.34689 -1.81539 0.415650 0.211993 -0.230428
27:     12824.350:  4.27616  3.52822 -1.01106 -2.13442 0.432715 0.275703 -0.685488
28:     12823.351:  4.23066  3.57854 -1.04670 -2.19321 0.443216 0.275445 -0.736273
29:     12822.020:  4.15504  3.69340 -1.11262 -2.31186 0.438306 0.277260 -0.839758
30:     12821.678:  4.16766  3.72833 -1.12054 -2.34894 0.442539 0.277696 -0.847782
31:     12820.076:  4.29083  4.01835 -1.17555 -2.63167 0.447016 0.280682 -0.905456
32:     12819.989:  4.31022  4.04524 -1.17746 -2.64689 0.446212 0.280222 -0.911716
33:     12819.974:  4.31379  4.05043 -1.17904 -2.64912 0.445031 0.277870 -0.912707
34:     12819.970:  4.31413  4.05110 -1.17944 -2.64937 0.446089 0.278385 -0.912874
35:     12819.969:  4.31423  4.05128 -1.17955 -2.64945 0.445976 0.278489 -0.912922
36:     12819.964:  4.31506  4.05291 -1.18050 -2.65009 0.445436 0.279207 -0.913336
37:     12819.951:  4.31866  4.05748 -1.18120 -2.65199 0.446557 0.278888 -0.914367
38:     12819.951:  4.31870  4.05757 -1.18125 -2.65202 0.446418 0.278909 -0.914391
39:     12819.950:  4.31881  4.05778 -1.18136 -2.65211 0.446170 0.278947 -0.914445
40:     12819.949:  4.31886  4.05788 -1.18142 -2.65215 0.446104 0.278956 -0.914472
41:     12819.949:  4.31897  4.05809 -1.18154 -2.65223 0.446001 0.278971 -0.914527
42:     12819.949:  4.31897  4.05810 -1.18154 -2.65224 0.445999 0.278971 -0.914529
43:     12819.949:  4.31898  4.05812 -1.18155 -2.65224 0.445994 0.278972 -0.914534
44:     12819.949:  4.31899  4.05813 -1.18155 -2.65224 0.445993 0.278972 -0.914536
45:     12819.949:  4.31899  4.05814 -1.18156 -2.65225 0.445990 0.278972 -0.914539
46:     12819.949:  4.31901  4.05816 -1.18156 -2.65226 0.445993 0.278972 -0.914551
47:     12819.949:  4.31901  4.05816 -1.18156 -2.65226 0.445993 0.278972 -0.914552
48:     12819.499:  4.57993  4.37399 -1.20349 -2.91607 0.445993 0.280809 -0.673595
49:     12819.476:  4.63159  4.42798 -1.19319 -2.90559 0.445993 0.278979 -0.676002
50:     12819.467:  4.65039  4.46164 -1.18855 -2.88381 0.445993 0.278870 -0.698913
51:     12819.457:  4.65195  4.49731 -1.18581 -2.84332 0.445993 0.278916 -0.753767
52:     12819.455:  4.64991  4.50005 -1.18586 -2.83834 0.445993 0.278930 -0.762154
53:     12819.452:  4.64451  4.50347 -1.18621 -2.82943 0.445993 0.278961 -0.779680
54:     12819.451:  4.64223  4.50429 -1.18639 -2.82623 0.445993 0.278975 -0.786906
55:     12819.448:  4.63828  4.50557 -1.18665 -2.82012 0.445993 0.278993 -0.801700
56:     12819.431:  4.59994  4.51143 -1.18978 -2.76341 0.445993 0.279288 -0.953305
57:     12819.431:  4.59950  4.51129 -1.18983 -2.76213 0.445993 0.279291 -0.957853
58:     12819.431:  4.59954  4.51119 -1.18984 -2.76194 0.445993 0.279291 -0.958775
59:     12819.430:  4.59980  4.51091 -1.18984 -2.76159 0.445993 0.279291 -0.960600
60:     12819.430:  4.59997  4.51076 -1.18983 -2.76148 0.445993 0.279290 -0.961316
61:     12819.430:  4.60002  4.51072 -1.18983 -2.76146 0.445993 0.279290 -0.961456
62:     12819.430:  4.60012  4.51064 -1.18983 -2.76143 0.445993 0.279290 -0.961729
63:     12819.430:  4.60016  4.51061 -1.18983 -2.76142 0.445993 0.279290 -0.961836
64:     12819.430:  4.60017  4.51060 -1.18983 -2.76142 0.445993 0.279290 -0.961856
65:     12819.430:  4.60018  4.51059 -1.18983 -2.76140 0.445993 0.279290 -0.961870
66:     12819.430:  4.60018  4.51059 -1.18983 -2.76140 0.445993 0.279290 -0.961872
67:     12819.430:  4.60228  4.50830 -1.18969 -2.76140 0.445993 0.279286 -0.961872
68:     12819.430:  4.60322  4.50748 -1.18966 -2.76140 0.445993 0.279284 -0.961872
69:     12819.430:  4.60516  4.50593 -1.18963 -2.76140 0.445993 0.279279 -0.961871
70:     12819.429:  4.60596  4.50533 -1.18963 -2.76140 0.445993 0.279278 -0.961870
71:     12819.429:  4.60611  4.50521 -1.18963 -2.76140 0.445993 0.279277 -0.961870
72:     12819.429:  4.60643  4.50498 -1.18962 -2.76140 0.445993 0.279277 -0.961870
73:     12819.429:  4.60656  4.50488 -1.18962 -2.76140 0.445993 0.279276 -0.961870
74:     12819.429:  4.60659  4.50486 -1.18962 -2.76140 0.445993 0.279277 -0.961870
75:     12819.429:  4.60679  4.50471 -1.18962 -2.76140 0.445993 0.279279 -0.961869
76:     12819.429:  4.60656  4.50431 -1.18983 -2.76140 0.445993 0.279279 -0.961869
77:     12819.429:  4.60678  4.50418 -1.18982 -2.76140 0.445993 0.279281 -0.961869
78:     12819.429:  4.60687  4.50412 -1.18981 -2.76140 0.445993 0.279282 -0.961869
79:     12819.429:  4.60691  4.50407 -1.18974 -2.76140 0.445993 0.279281 -0.961869
80:     12819.429:  4.60693  4.50404 -1.18972 -2.76140 0.445993 0.279281 -0.961869
81:     12819.429:  4.60694  4.50404 -1.18972 -2.76140 0.445993 0.279281 -0.961869
82:     12819.429:  4.60695  4.50403 -1.18971 -2.76140 0.445993 0.279280 -0.961869
83:     12819.429:  4.60695  4.50403 -1.18971 -2.76140 0.445993 0.279280 -0.961869
84:     12819.429:  4.60695  4.50403 -1.18971 -2.76140 0.445993 0.279280 -0.961869
Warning message:
In mer_finalize(ans) : false convergence (8)

> summary(Mod.1)
Generalized linear mixed model fit by the Laplace approximation
Formula: u_d ~ Food + Cover + Roads + (Roads | ANIMAL)
   Data: dat[dat$Period == 1, ]
   AIC   BIC logLik deviance
12833 12885  -6410    12819
Random effects:
Groups Name        Variance Std.Dev. Corr
 ANIMAL (Intercept) 21.224   4.6070
        Roads       50.327   7.0942   -0.773
Number of obs: 11426, groups: ANIMAL, 18

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.76140    1.09527  -2.521   0.0117 *
Food         0.44599    0.02355  18.938  < 2e-16 ***
Cover        0.27928    0.03709   7.529 5.11e-14 ***
Roads       -0.96187    1.67964  -0.573   0.5669
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Correlation of Fixed Effects:
      (Intr) Food   Cover
Food  -0.081
Cover -0.024  0.161
Roads -0.769 -0.006  0.006



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