*I tried feeding starting values derived from the fit of the model without
tree but I think I have the form of the starting values wrong. In any case
they seem to be ignored. I have a few other attempts fooling around with
different list formats that I will spare the list. Here are some verbose
fits:

> f0128bi = lmer(fincr ~ icfac + (1|gap) + (1|box)+ (1|gap:box), family =
binomial,
+                 verbose = TRUE)
  0:     698.01832:  1.15470 0.222222 0.182574 -3.22238  1.44743 -2.20257
1.02515
  1:     680.00309: 0.912653 0.304161 0.190988 -3.63159  1.36544 -2.21683
1.02120
  2:     677.96306:  1.01866 0.482008 0.223459 -3.58125  1.42239 -2.22011
1.01943
  3:     676.83159: 0.942935 0.550703 0.186273 -3.77127  1.45497 -2.24391
1.00749
  4:     676.20261:  1.02092 0.616975 0.271931 -3.79002  1.61798 -2.30232
0.967926
  5:     672.86909:  1.60046 0.435169 0.191816 -4.15178  1.61731 -2.71823
0.627024
  6:     636.86637:  4.08026  0.00000  0.00000 -5.97073  1.10575 -3.69529
-0.354067
  7:     635.32860:  6.62531  0.00000  0.00000 -8.98730  1.00334 -4.54318
-0.942187
  8:     624.96107:  5.25510  0.00000 0.00848043 -7.87111  1.70422 -3.96107
-0.484424
  9:     612.32636:  5.71473 2.39396e-10 0.0101177 -7.44968  1.95302
-3.94374 -0.460900
 10:     606.45168:  6.74884 0.191878  0.00000 -8.55710  2.61929 -3.88942
-0.117241
 11:     605.57560:  6.99552 0.407366  0.00000 -8.57848  2.47805 -3.92108
0.203281
 12:     605.32783:  7.31754 0.160150  0.00000 -8.43360  2.62282 -4.02908
0.317333
 13:     603.71565:  7.43439 0.122381  0.00000 -8.67426  2.59932 -4.31632
0.590438
 14:     603.55507:  7.47544 0.111819 0.0411083 -8.76026  2.53284 -4.27292
0.692152
 15:     603.17998:  7.59875 0.0492753  0.00000 -8.78885  2.64321 -4.44958
0.842062
 16:     602.00979:  8.20061 0.0607085  0.00000 -9.18744  2.74198 -5.06373
2.18339
 17:     601.92079:  8.27558 0.0294396 0.0200822 -9.16980  2.75901
-4.93217  2.29786
 18:     601.82987:  8.28503  0.00000  0.00000 -9.27682  2.77941 -4.89224
2.43203
 19:     601.74144:  8.38112  0.00000  0.00000 -9.33323  2.83137 -4.83132
2.86585
 20:     601.73398:  8.34288  0.00000 0.000963770 -9.31097  2.82761
-4.77369  2.90017
 21:     601.73370:  8.33634 0.00681790 0.00571026 -9.30717  2.81760
-4.77335  2.90966
 22:     601.73274:  8.32420 0.000446870  0.00000 -9.29978  2.81733
-4.76865  2.90739
 23:     601.73256:  8.31195 0.00169251  0.00000 -9.28918  2.81617
-4.76306  2.91183
 24:     601.73238:  8.29728 0.000353630  0.00000 -9.28247  2.81157
-4.75729  2.91129
 25:     601.73237:  8.29938 0.000158474  0.00000 -9.28394  2.81277
-4.75920  2.91087
 26:     601.73237:  8.29935 3.13336e-05  0.00000 -9.28405  2.81298
-4.75938  2.91079
 27:     601.73237:  8.29931 2.56368e-06  0.00000 -9.28405  2.81300
-4.75935  2.91080
> summary(f0128bi)
Generalized linear mixed model fit by the Laplace approximation
Formula: fincr ~ icfac + (1 | gap) + (1 | box) + (1 | gap:box)
   AIC   BIC logLik deviance
 615.7 655.5 -300.9    601.7
Random effects:
 Groups  Name        Variance   Std.Dev.
 gap:box (Intercept) 6.8878e+01 8.2993e+00
 box     (Intercept) 6.5724e-12 2.5637e-06
 gap     (Intercept) 0.0000e+00 0.0000e+00
Number of obs: 2160, groups: gap:box, 1080; box, 40; gap, 27

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -9.2840     0.8284 -11.207  < 2e-16 ***
icfacmale     2.8130     0.6544   4.299 1.72e-05 ***
icfacfem     -4.7594     1.6017  -2.971  0.00296 **
icfacmix      2.9102     3.3212   0.876  0.38091
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr) icfcml icfcfm
icfacmale -0.274
icfacfem  -0.052  0.035
icfacmix  -0.057  0.016  0.003
> f0128bit = lmer(fincr ~ icfac + (1|gap) + (1|box)+ (1|gap:box) +
(1|tree), family = binomial,
+                  verbose = TRUE)
  0:     696.35176:  1.15470 0.222222 0.182574 0.157135 -3.22238  1.44743
-2.20257  1.02515
  1:     687.88422: 0.646498 0.349241 0.199295 0.375671 -4.02981  1.29061
-2.23330  1.01588
  2:     674.84480: 0.803688 0.508187 0.336745 0.667441 -3.74286  1.40748
-2.23247  1.01298
  3:     673.11211: 0.560216 0.230894 0.409007 0.604929 -3.59643  1.67450
-2.32331 0.952101
  4:     672.43437: 0.475142 0.268879 0.288703 0.720495 -3.63146  1.54138
-2.33935 0.929209
  5:     671.98938: 0.362818 0.280682 0.430705 0.719274 -3.51244  1.47740
-2.37930 0.870382
  6:     671.50213: 0.219560 0.285982 0.315720 0.670600 -3.41458  1.49586
-2.43852 0.788706
  7:     671.35505: 0.0793705 0.282448 0.390830 0.724623 -3.53149  1.44634
-2.49043 0.692800
  8:     671.29820:  0.00000 0.271261 0.350087 0.759492 -3.40165  1.52609
-2.52437 0.588663
  9:     671.08937:  0.00000 0.272578 0.334061 0.697810 -3.44406  1.46124
-2.53189 0.556962
 10:     671.05911:  0.00000 0.300527 0.368607 0.692208 -3.42125  1.44927
-2.55140 0.467085
 11:     671.02271: 0.0170699 0.268741 0.358526 0.715800 -3.43386  1.45847
-2.57225 0.374939
 12:     671.01834: 0.0151291 0.273943 0.348921 0.713119 -3.43709  1.45538
-2.57209 0.372730
 13:     671.01670: 0.0118066 0.282872 0.354271 0.712944 -3.43312  1.45545
-2.57167 0.368286
 14:     671.01455: 0.0100917 0.285561 0.350083 0.710790 -3.43700  1.45693
-2.57101 0.358038
 15:     671.01371: 0.00496597 0.287303 0.353339 0.710154 -3.43356  1.45239
-2.57085 0.348963
 16:     671.01230: 0.00559602 0.286097 0.349183 0.710651 -3.42956  1.45004
-2.56887 0.324972
 17:     671.01104: 0.00125948 0.288659 0.350873 0.709928 -3.43745  1.45623
-2.56990 0.302838
 18:     671.01100:  0.00000 0.288388 0.349377 0.713108 -3.43331  1.45307
-2.57477 0.298363
 19:     671.01085:  0.00000 0.286400 0.349936 0.710571 -3.43448  1.45184
-2.57656 0.296295
 20:     671.01079: 0.00117919 0.287734 0.350363 0.710208 -3.43469  1.45369
-2.57442 0.293187
 21:     671.01077: 0.000362717 0.287853 0.350079 0.711252 -3.43490
1.45355 -2.57345 0.293379
 22:     671.01076:  0.00000 0.287668 0.350258 0.710864 -3.43480  1.45345
-2.57468 0.294227
 23:     671.01076:  0.00000 0.287669 0.350193 0.711002 -3.43482  1.45346
-2.57393 0.293998
 24:     671.01076: 1.67465e-05 0.287734 0.350135 0.711099 -3.43481
1.45347 -2.57400 0.293974
 25:     671.01076:  0.00000 0.287724 0.350165 0.711061 -3.43483  1.45346
-2.57399 0.293950
 26:     671.01076:  0.00000 0.287718 0.350162 0.711060 -3.43482  1.45346
-2.57397 0.293937
 27:     671.01076:  0.00000 0.287721 0.350163 0.711061 -3.43482  1.45346
-2.57398 0.293936
> summary(f0128bit)
Generalized linear mixed model fit by the Laplace approximation
Formula: fincr ~ icfac + (1 | gap) + (1 | box) + (1 | gap:box) + (1 |
tree)
 AIC   BIC logLik deviance
 687 732.4 -335.5      671
Random effects:
 Groups  Name        Variance Std.Dev.
 gap:box (Intercept) 0.000000 0.00000
 box     (Intercept) 0.082784 0.28772
 gap     (Intercept) 0.122614 0.35016
 tree    (Intercept) 0.505608 0.71106
Number of obs: 2160, groups: gap:box, 1080; box, 40; gap, 27; tree, 20

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept)  -3.4348     0.2315 -14.835  < 2e-16 ***
icfacmale     1.4535     0.2738   5.308 1.11e-07 ***
icfacfem     -2.5740     0.7480  -3.441 0.000579 ***
icfacmix      0.2935     1.1049   0.266 0.790517
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
          (Intr) icfcml icfcfm
icfacmale -0.357
icfacfem  -0.114  0.122
icfacmix  -0.065  0.066  0.032
> initpar = c(0, 8.29931, 2.56368e-06,  0.00000, -9.28405,  2.81300,
-4.75935,  2.91080)
> f0128bit = lmer(fincr ~ icfac + (1|gap) + (1|box)+ (1|gap:box) +
(1|tree), family = binomial,
+                  verbose = TRUE, start = initpar)
  0:     696.35176:  1.15470 0.222222 0.182574 0.157135 -3.22238  1.44743
-2.20257  1.02515
  1:     687.88422: 0.646498 0.349241 0.199295 0.375671 -4.02981  1.29061
-2.23330  1.01588
  2:     674.84480: 0.803688 0.508187 0.336745 0.667441 -3.74286  1.40748
-2.23247  1.01298
  3:     673.11211: 0.560216 0.230894 0.409007 0.604929 -3.59643  1.67450
-2.32331 0.952101
  4:     672.43437: 0.475142 0.268879 0.288703 0.720495 -3.63146  1.54138
-2.33935 0.929209
  5:     671.98938: 0.362818 0.280682 0.430705 0.719274 -3.51244  1.47740
-2.37930 0.870382
  6:     671.50213: 0.219560 0.285982 0.315720 0.670600 -3.41458  1.49586
-2.43852 0.788706
  7:     671.35505: 0.0793705 0.282448 0.390830 0.724623 -3.53149  1.44634
-2.49043 0.692800
  8:     671.29820:  0.00000 0.271261 0.350087 0.759492 -3.40165  1.52609
-2.52437 0.588663
  9:     671.08937:  0.00000 0.272578 0.334061 0.697810 -3.44406  1.46124
-2.53189 0.556962
 10:     671.05911:  0.00000 0.300527 0.368607 0.692208 -3.42125  1.44927
-2.55140 0.467085
 11:     671.02271: 0.0170699 0.268741 0.358526 0.715800 -3.43386  1.45847
-2.57225 0.374939
 12:     671.01834: 0.0151291 0.273943 0.348921 0.713119 -3.43709  1.45538
-2.57209 0.372730
 13:     671.01670: 0.0118066 0.282872 0.354271 0.712944 -3.43312  1.45545
-2.57167 0.368286
 14:     671.01455: 0.0100917 0.285561 0.350083 0.710790 -3.43700  1.45693
-2.57101 0.358038
 15:     671.01371: 0.00496597 0.287303 0.353339 0.710154 -3.43356  1.45239
-2.57085 0.348963
 16:     671.01230: 0.00559602 0.286097 0.349183 0.710651 -3.42956  1.45004
-2.56887 0.324972
 17:     671.01104: 0.00125948 0.288659 0.350873 0.709928 -3.43745  1.45623
-2.56990 0.302838
 18:     671.01100:  0.00000 0.288388 0.349377 0.713108 -3.43331  1.45307
-2.57477 0.298363
 19:     671.01085:  0.00000 0.286400 0.349936 0.710571 -3.43448  1.45184
-2.57656 0.296295
 20:     671.01079: 0.00117919 0.287734 0.350363 0.710208 -3.43469  1.45369
-2.57442 0.293187
 21:     671.01077: 0.000362717 0.287853 0.350079 0.711252 -3.43490
1.45355 -2.57345 0.293379
 22:     671.01076:  0.00000 0.287668 0.350258 0.710864 -3.43480  1.45345
-2.57468 0.294227
 23:     671.01076:  0.00000 0.287669 0.350193 0.711002 -3.43482  1.45346
-2.57393 0.293998
 24:     671.01076: 1.67465e-05 0.287734 0.350135 0.711099 -3.43481
1.45347 -2.57400 0.293974
 25:     671.01076:  0.00000 0.287724 0.350165 0.711061 -3.43483  1.45346
-2.57399 0.293950
 26:     671.01076:  0.00000 0.287718 0.350162 0.711060 -3.43482  1.45346
-2.57397 0.293937
 27:     671.01076:  0.00000 0.287721 0.350163 0.711061 -3.43482  1.45346
-2.57398 0.293936


Thanks to David and Douglas for help so far.  Murray
*
On 17 May 2012 16:15, Murray Jorgensen <maj@waikato.ac.nz> wrote:

> The verbose parameter of lmer seems to take
>
>
> On 17/05/12 3:58 PM, Douglas Bates wrote:
>
>> As with all such cases, I would start with setting verbose=2 or
>> something like that to see where the iterations are headed.  The model
>> is probably converging to a local optimum on the boundary.
>>
>
> --
> Dr Murray Jorgensen      http://www.stats.waikato.ac.**nz/Staff/maj.html<http://www.stats.waikato.ac.nz/Staff/maj.html>
> Department of Statistics, University of Waikato, Hamilton, New Zealand
> Email: maj@waikato.ac.nz      majmurr@gmail.com         Fax 7 838 4155
>
> Phone  +64 7 838 4773 wk    Home +64 7 825 0441   Mobile 021 0200 8350
>



-- 
Dr Murray Jorgensen      http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj@waikato.ac.nz    majorgensen@ihug.co.nz      Fax 7 838 4155
Phone  +64 7 838 4773 wk    Home +64 7 825 0441   Mobile 021 0200 8350

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