[R-sig-ME] warnings in lmer estimation after mice
Raquel Guimaraes
raquelrguima at gmail.com
Sat Jun 30 19:59:25 CEST 2012
Thanks Robert. This is really a great advice. I was worried about the
reduced sample, but I will go ahead and try to explain what's going on.
Best,
Raquel
-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org
[mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of W Robert Long
Sent: Saturday, June 30, 2012 6:49 AM
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] warnings in lmer estimation after mice
Two things spring to mind:
1. I don't think your imputation respects the data hierarchy - see p21
here:
http://www.jstatsoft.org/v45/i03/paper
2. You have 58% missingness in "gain" and 81% missingness in "index", which
makes me wonder if you should be doing multiple imputation at all.
On 29/06/2012 10:20 PM, Raquel Guimaraes wrote:
> Hi all,
>
> I need help to understand convergence problems with my estimation.
>
> I am using lmer to estimate a mixed-effects model of an index of
> teacher quality on student achievement gains.
>
> The dataset for female students is available in the link above and
> contains original and imputed data (m=5 and m=50)
>
> <https://www.dropbox.com/sh/fqpjtkkuz9y9mzr/MhrMXwlrFp>
>
> I want to understand the error messages above and their implications
> for my parameter estimates.
>
> 1: In mer_finalize(ans) : false convergence (8)
> 2: In mer_finalize(ans) : singular convergence (7)
> 3: In mer_finalize(ans) : singular convergence (7)
> 4: In mer_finalize(ans) : false convergence (8)
> 5: In mer_finalize(ans) : false convergence (8)
>
> Below you may find the script for my procedures.
>
> Any help will be appreciated!
>
> Thanks,
>
> Raquel
>
> -----------------------------------------------------------------
>
> #Multiple imputation
>
> library("mice")
> md.pattern(data.f)
> md.pairs(data.f)
> # 5 imputations - Predictive mean matching
> imp.5.f<-mice(data.f,m=5,seed=23109)
> print(imp.5.f)
> # 50 imputations - Predictive mean matching
> imp.50.f<-mice(data.f,m=50,seed=23109)
> print(imp.50.f)
>
> #Running lmer
> library(lme4)
>
> #Imputed data
> #Females
> Uncpde.math.f.mi <- with(imp.50.f, lmer(gain ~ index +
> ( index | std_id), verbose=TRUE))
> print(pool(Uncpde.math.f.mi))
> round(summary(pool(Uncpde.math.f.mi)), 2)
>
>> Uncpde.math.f.mi <- with(imp.50.f, lmer(gain ~ index +
> + ( index | std_id), verbose=TRUE))
> 0: 91323.888: 0.666667 0.0912755 0.000000
> 1: 90950.102: 0.579295 0.000000 -0.298895
> 2: 88854.168: 0.305206 0.000000 -0.0962998
> 3: 88814.543: 0.288910 8.27185e-010 -0.126235
> 4: 88797.001: 0.261789 0.00137943 -0.146833
> 5: 88783.121: 0.229797 0.000000 -0.136191
> 6: 88765.032: 0.163649 0.000000 -0.170033
> 7: 88764.059: 0.101518 0.00683235 -0.129860
> 8: 88757.161: 0.0894730 0.000000 -0.151620
> 9: 88756.669: 0.0802386 0.000000 -0.161394
> 10: 88756.413: 0.0692536 0.000000 -0.169148
> 11: 88756.296: 0.0566213 0.000000 -0.173754
> 12: 88756.276: 0.0473775 0.000000 -0.172026
> 13: 88756.268: 0.0454115 0.000000 -0.177962
> 14: 88756.268: 0.0445268 0.000000 -0.177518
> 15: 88756.268: 0.0445255 6.95905e-007 -0.177518
> 16: 88756.268: 0.0443349 1.91179e-007 -0.177519
> 17: 88756.268: 0.0443349 1.91179e-007 -0.177519
> 0: 90892.401: 0.666667 0.0901811 0.000000
> 1: 90565.986: 0.582914 0.000000 -0.301351
> 2: 88348.591: 0.262114 0.000000 -0.107499
> 3: 88300.308: 0.000000 0.000000 -0.259898
> 4: 88300.308: 0.000000 0.000000 -0.259898
> 0: 92116.635: 0.666667 0.0860717 0.000000
> 1: 91920.069: 0.589035 0.000000 -0.302245
> 2: 89502.995: 0.208420 0.000000 -0.118175
> 3: 89484.310: 0.000000 0.000000 -0.217057
> 4: 89484.310: 0.000000 0.000000 -0.217057
> 0: 90508.355: 0.666667 0.0907223 0.000000
> 1: 90069.269: 0.575010 0.000000 -0.303351
> 2: 87914.618: 0.289817 0.000000 -0.0984479
> 3: 87811.950: 0.000000 0.000000 -0.262459
> 4: 87811.950: 0.000000 0.000000 -0.262459
> 0: 90728.496: 0.666667 0.0893763 0.000000
> 1: 90385.062: 0.582005 0.000000 -0.301492
> 2: 88044.313: 0.230444 0.000000 -0.108661
> 3: 88004.837: 0.000000 0.000000 -0.234827
> 4: 88004.837: 0.000000 0.000000 -0.234827
> 0: 90569.541: 0.666667 0.0904525 0.000000
> 1: 90248.550: 0.585924 0.000000 -0.296967
> 2: 87912.085: 0.229608 0.000000 -0.106161
> 3: 87890.585: 0.000000 0.000000 -0.222132
> 4: 87890.585: 0.000000 0.000000 -0.222132
> 0: 90567.799: 0.666667 0.0887506 0.000000
> 1: 90302.451: 0.587353 0.000000 -0.300203
> 2: 88030.944: 0.255280 0.000000 -0.110269
> 3: 87995.243: 0.000000 0.000000 -0.246234
> 4: 87995.243: 0.000000 0.000000 -0.246234
> 0: 90630.186: 0.666667 0.0905161 0.000000
> 1: 90245.654: 0.581825 0.000000 -0.299051
> 2: 88024.455: 0.278519 0.000000 -0.100122
> 3: 87959.404: 0.000000 0.000000 -0.269139
> 4: 87959.404: 0.000000 0.000000 -0.269139
> 0: 91431.304: 0.666667 0.0910478 0.000000
> 1: 91060.176: 0.577973 0.000000 -0.299692
> 2: 88879.745: 0.273428 0.000000 -0.0991786
> 3: 88804.068: 0.000000 0.000000 -0.272971
> 4: 88804.068: 0.000000 0.000000 -0.272971
> 0: 90853.326: 0.666667 0.0899358 0.000000
> 1: 90519.641: 0.585512 0.000000 -0.298258
> 2: 88288.745: 0.275120 0.000000 -0.102956
> 3: 88246.430: 0.000000 0.000000 -0.258963
> 4: 88246.430: 0.000000 0.000000 -0.258963
> 0: 90697.216: 0.666667 0.0856334 0.000000
> 1: 90578.775: 0.593759 0.000000 -0.298316
> 2: 88081.238: 0.184499 0.000000 -0.119253
> 3: 88078.012: 0.000000 0.000000 -0.196102
> 4: 88078.012: 0.000000 0.000000 -0.196102
> 0: 90639.439: 0.666667 0.0902601 0.000000
> 1: 90295.074: 0.583471 0.000000 -0.299601
> 2: 88125.514: 0.286629 0.000000 -0.102261
> 3: 88067.467: 0.000000 0.000000 -0.268202
> 4: 88067.467: 0.000000 0.000000 -0.268202
> 0: 90573.744: 0.666667 0.0917234 0.000000
> 1: 90156.547: 0.577930 0.000000 -0.300638
> 2: 88068.528: 0.306034 0.000000 -0.0965273
> 3: 87975.584: 0.000000 0.000000 -0.238077
> 4: 87975.584: 0.000000 0.000000 -0.238077
> 0: 89721.612: 0.666667 0.0913660 0.000000
> 1: 89274.384: 0.577867 0.000000 -0.302115
> 2: 87025.514: 0.258058 0.000000 -0.102274
> 3: 86960.255: 0.000000 0.000000 -0.264012
> 4: 86960.255: 0.000000 0.000000 -0.264012
> 0: 89773.327: 0.666667 0.0914180 0.000000
> 1: 89431.386: 0.579968 0.000000 -0.297710
> 2: 87172.239: 0.245863 0.000000 -0.102001
> 3: 87121.156: 0.000000 0.000000 -0.244505
> 4: 87121.156: 0.000000 2.95447e-009 -0.244505
> 5: 87121.156: 0.000000 1.89955e-009 -0.244505
> 0: 91307.998: 0.666667 0.0881736 0.000000
> 1: 90963.328: 0.582168 0.000000 -0.305197
> 2: 88585.039: 0.218209 0.000000 -0.114601
> 3: 88552.517: 0.000000 0.000000 -0.232242
> 4: 88552.517: 0.000000 0.000000 -0.232242
> 0: 90098.405: 0.666667 0.0912658 0.000000
> 1: 89625.050: 0.577676 0.000000 -0.303420
> 2: 87381.866: 0.264236 0.000000 -0.102601
> 3: 87313.537: 0.000000 0.000000 -0.270530
> 4: 87313.537: 0.000000 0.000000 -0.270530
> 0: 90946.093: 0.666667 0.0900592 0.000000
> 1: 90583.090: 0.581315 0.000000 -0.300756
> 2: 88388.898: 0.278531 0.000000 -0.102687
> 3: 88324.595: 0.000000 0.000000 -0.273038
> 4: 88324.595: 0.000000 0.000000 -0.273038
> 0: 91333.939: 0.666667 0.0913058 0.000000
> 1: 90904.541: 0.577003 0.000000 -0.299602
> 2: 88679.234: 0.272647 0.000000 -0.0969097
> 3: 88596.130: 0.000000 0.000000 -0.272223
> 4: 88596.130: 0.000000 0.000000 -0.272223
> 0: 90152.037: 0.666667 0.0900727 0.000000
> 1: 89702.620: 0.575074 0.000000 -0.306728
> 2: 87596.979: 0.302713 0.000000 -0.101807
> 3: 87484.248: 0.000000 0.000000 -0.248542
> 4: 87484.248: 0.000000 0.000000 -0.248542
> 0: 91117.801: 0.666667 0.0908513 0.000000
> 1: 90757.634: 0.580554 0.000000 -0.299360
> 2: 88603.505: 0.283831 0.000000 -0.0999435
> 3: 88574.096: 0.267794 0.000000 -0.130461
> 4: 88562.206: 0.238612 0.000000 -0.148816
> 5: 88553.044: 0.208478 0.000595092 -0.132080
> 6: 88546.351: 0.181458 0.000000 -0.153290
> 7: 88542.081: 0.150185 0.000000 -0.138783
> 8: 88539.627: 0.123847 1.01894e-009 -0.161027
> 9: 88538.734: 0.0942445 0.000744265 -0.143374
> 10: 88538.275: 0.0808928 0.000000 -0.152300
> 11: 88538.188: 0.0721130 0.000000 -0.159438
> 12: 88538.184: 0.0695887 0.000000 -0.160606
> 13: 88538.183: 0.0680936 0.000000 -0.160978
> 14: 88538.183: 0.0677477 0.000000 -0.160916
> 15: 88538.183: 0.0676509 0.000000 -0.160808
> 16: 88538.183: 0.0676691 0.000000 -0.160770
> 0: 90816.807: 0.666667 0.0906318 0.000000
> 1: 90424.195: 0.578291 0.000000 -0.302393
> 2: 88300.640: 0.297094 0.000000 -0.100293
> 3: 88204.488: 0.000000 0.000000 -0.253910
> 4: 88204.488: 0.000000 0.000000 -0.253910
> 0: 91513.066: 0.666667 0.0905136 0.000000
> 1: 91059.392: 0.572808 0.000000 -0.304419
> 2: 88963.498: 0.308436 0.000000 -0.0961059
> 3: 88828.230: 0.00365434 0.000000 -0.238915
> 4: 88828.230: 0.00353298 2.48295e-007 -0.238904
> 5: 88828.228: 0.000809467 8.53595e-006 -0.238663
> 6: 88828.227: 0.000000 0.000000 -0.238628
> 7: 88828.227: 0.000000 0.000000 -0.238628
> 0: 91150.802: 0.666667 0.0891040 0.000000
> 1: 90889.912: 0.587635 0.000000 -0.295390
> 2: 88556.237: 0.240279 0.000000 -0.104882
> 3: 88549.670: 0.235780 0.000000 -0.114362
> 4: 88542.519: 0.221856 0.000000 -0.136892
> 5: 88538.276: 0.196911 0.000536413 -0.128009
> 6: 88535.760: 0.176898 0.000000 -0.145077
> 7: 88534.303: 0.157582 0.000000 -0.126956
> 8: 88533.996: 0.156218 2.29140e-010 -0.129226
> 9: 88533.534: 0.153150 3.03087e-005 -0.133544
> 10: 88532.859: 0.140751 0.000000 -0.142165
> 11: 88532.514: 0.126963 0.000000 -0.135997
> 12: 88532.299: 0.114249 0.000000 -0.140115
> 13: 88532.276: 0.109185 0.000000 -0.141707
> 14: 88532.275: 0.107867 0.000000 -0.142196
> 15: 88532.275: 0.107761 0.000000 -0.142270
> 16: 88532.275: 0.107763 2.96078e-010 -0.142274
> 17: 88532.275: 0.107767 0.000000 -0.142285
> 18: 88532.275: 0.107776 0.000000 -0.142284
> 0: 90591.670: 0.666667 0.0902163 0.000000
> 1: 90253.650: 0.582677 0.000000 -0.296733
> 2: 87913.750: 0.234039 0.000000 -0.102682
> 3: 87877.999: 0.000000 0.000000 -0.227078
> 4: 87877.999: 0.000000 0.000000 -0.227078
> 0: 90673.897: 0.666667 0.0896992 0.000000
> 1: 90337.823: 0.585673 0.000000 -0.300345
> 2: 88000.465: 0.229841 0.000000 -0.109921
> 3: 87976.655: 0.000000 0.000000 -0.229344
> 4: 87976.655: 0.000000 0.000000 -0.229344
> 0: 91695.842: 0.666667 0.0899055 0.000000
> 1: 91404.328: 0.584642 0.000000 -0.297876
> 2: 89214.414: 0.281201 0.000000 -0.102582
> 3: 89188.190: 0.265626 0.000000 -0.133284
> 4: 89178.234: 0.236801 0.000000 -0.152108
> 5: 89170.640: 0.208346 0.00119384 -0.132766
> 6: 89165.050: 0.184852 0.000000 -0.157320
> 7: 89161.506: 0.155593 0.000000 -0.139179
> 8: 89159.386: 0.133551 4.59756e-009 -0.165624
> 9: 89159.213: 0.130015 0.00119127 -0.162734
> 10: 89158.808: 0.126412 0.000000 -0.160222
> 11: 89158.328: 0.109125 0.000000 -0.154874
> 12: 89158.186: 0.0975572 0.000000 -0.168789
> 13: 89158.113: 0.0883497 0.000000 -0.164147
> 14: 89158.112: 0.0893647 3.36482e-007 -0.164147
> 15: 89158.112: 0.0894556 0.000000 -0.164102
> 16: 89158.111: 0.0901974 2.92690e-006 -0.163772
> 17: 89158.111: 0.0904597 0.000000 -0.163769
> 18: 89158.111: 0.0904597 0.000000 -0.163769
> 0: 92026.232: 0.666667 0.0894992 0.000000
> 1: 91735.788: 0.581037 0.000000 -0.298447
> 2: 89530.291: 0.274498 0.000000 -0.101211
> 3: 89465.691: 0.000000 0.000000 -0.265849
> 4: 89465.691: 0.000000 0.000000 -0.265849
> 0: 89155.666: 0.666667 0.0892915 0.000000
> 1: 88806.417: 0.582861 0.000000 -0.303162
> 2: 86512.523: 0.245630 0.000000 -0.109990
> 3: 86472.497: 0.000000 0.000000 -0.248275
> 4: 86472.497: 0.000000 0.000000 -0.248275
> 0: 91625.651: 0.666667 0.0892096 0.000000
> 1: 91305.690: 0.580685 0.000000 -0.300005
> 2: 89053.463: 0.263647 0.000000 -0.103131
> 3: 88986.561: 0.000000 0.000000 -0.261116
> 4: 88986.561: 0.000000 0.000000 -0.261116
> 0: 90869.702: 0.666667 0.0892723 0.000000
> 1: 90555.120: 0.579552 0.000000 -0.300675
> 2: 88410.128: 0.295998 0.000000 -0.100232
> 3: 88314.598: 0.000000 0.000000 -0.255183
> 4: 88314.598: 0.000000 3.86364e-009 -0.255183
> 5: 88314.598: 0.000000 3.86364e-009 -0.255183
> 0: 91108.704: 0.666667 0.0906011 0.000000
> 1: 90804.467: 0.586207 0.000000 -0.293469
> 2: 88408.714: 0.209336 0.000000 -0.104199
> 3: 88390.555: 0.000000 0.000000 -0.233786
> 4: 88390.555: 0.000000 4.74440e-009 -0.233786
> 5: 88390.555: 0.000000 4.74440e-009 -0.233786
> 0: 91771.237: 0.666667 0.0903370 0.000000
> 1: 91413.484: 0.577979 0.000000 -0.300203
> 2: 89218.035: 0.273200 0.000000 -0.100290
> 3: 89189.934: 0.254171 1.16058e-009 -0.128658
> 4: 89174.483: 0.222469 0.00144656 -0.141297
> 5: 89151.646: 0.136126 0.000000 -0.129612
> 6: 89144.537: 0.0652530 0.000000 -0.181235
> 7: 89143.147: 0.000000 0.000000 -0.132500
> 8: 89143.147: 0.000000 0.000000 -0.132500
> 0: 92075.541: 0.666667 0.0889713 0.000000
> 1: 91754.396: 0.582594 0.000000 -0.299314
> 2: 89493.502: 0.269474 0.000000 -0.102596
> 3: 89431.022: 0.000000 0.000000 -0.259856
> 4: 89431.022: 0.000000 0.000000 -0.259856
> 0: 90741.014: 0.666667 0.0910750 0.000000
> 1: 90297.019: 0.577011 0.000000 -0.305139
> 2: 88152.539: 0.285117 0.000000 -0.103300
> 3: 88068.757: 0.000000 0.000000 -0.272904
> 4: 88068.757: 0.000000 0.000000 -0.272904
> 0: 90254.977: 0.666667 0.0906367 0.000000
> 1: 89853.808: 0.580116 0.000000 -0.303049
> 2: 87726.880: 0.295568 0.000000 -0.102663
> 3: 87649.217: 0.000000 0.000000 -0.257359
> 4: 87649.217: 0.000000 0.000000 -0.257359
> 0: 90214.862: 0.666667 0.0899011 0.000000
> 1: 89935.462: 0.586403 0.000000 -0.298133
> 2: 87667.921: 0.252502 0.000000 -0.107255
> 3: 87649.405: 0.231383 0.00404239 -0.138625
> 4: 87640.356: 0.197140 0.000000 -0.132754
> 5: 87636.308: 0.171573 0.000000 -0.160910
> 6: 87633.747: 0.143169 0.000414332 -0.135621
> 7: 87631.943: 0.109135 0.00175782 -0.152542
> 8: 87631.413: 0.100984 0.000000 -0.155398
> 9: 87631.389: 0.0970080 0.000000 -0.156461
> 10: 87631.383: 0.0953971 0.000000 -0.160248
> 11: 87631.379: 0.0917818 4.22655e-005 -0.158282
> 12: 87631.379: 0.0936796 0.000000 -0.159862
> 13: 87631.378: 0.0935501 0.000000 -0.158634
> 14: 87631.377: 0.0923555 0.000000 -0.158946
> 15: 87631.377: 0.0929711 0.000000 -0.158993
> 16: 87631.377: 0.0929886 1.09932e-006 -0.158978
> 0: 90450.512: 0.666667 0.0891198 0.000000
> 1: 90145.789: 0.582078 0.000000 -0.299052
> 2: 87955.609: 0.286906 0.000000 -0.100895
> 3: 87880.498: 0.000000 0.000000 -0.267268
> 4: 87880.498: 0.000000 0.000000 -0.267268
> 0: 89654.427: 0.666667 0.0897456 0.000000
> 1: 89249.342: 0.581870 0.000000 -0.303489
> 2: 86983.487: 0.260490 0.000000 -0.107015
> 3: 86936.154: 0.000000 0.000000 -0.262106
> 4: 86936.154: 0.000000 0.000000 -0.262106
> 0: 90672.682: 0.666667 0.0899628 0.000000
> 1: 90295.782: 0.579048 0.000000 -0.302737
> 2: 88163.587: 0.295266 0.000000 -0.102005
> 3: 88073.489: 0.000000 0.000000 -0.257767
> 4: 88073.489: 0.000000 0.000000 -0.257767
> 0: 90937.235: 0.666667 0.0908255 0.000000
> 1: 90560.743: 0.576783 0.000000 -0.302200
> 2: 88512.443: 0.313763 0.000000 -0.0979954
> 3: 88397.731: 0.0116791 0.000000 -0.238082
> 4: 88397.731: 0.0116793 2.13555e-006 -0.238082
> 5: 88397.731: 0.0116802 1.58534e-006 -0.238082
> 6: 88397.707: 0.0199269 2.58593e-006 -0.240413
> 7: 88397.698: 0.0233275 1.25248e-006 -0.240845
> 8: 88397.681: 0.0360058 0.000000 -0.238725
> 9: 88397.642: 0.0377094 0.000000 -0.233764
> 10: 88397.488: 0.0443160 0.000000 -0.213853
> 11: 88397.429: 0.0505245 0.000000 -0.206634
> 12: 88397.386: 0.0624587 0.000000 -0.191795
> 13: 88397.384: 0.0605955 1.43867e-007 -0.197179
> 14: 88397.382: 0.0592088 1.94955e-008 -0.196263
> 15: 88397.382: 0.0603454 2.87004e-008 -0.195051
> 16: 88397.382: 0.0603722 3.24738e-008 -0.195107
> 0: 90982.636: 0.666667 0.0900408 0.000000
> 1: 90686.775: 0.589304 0.000000 -0.297633
> 2: 88417.031: 0.251443 0.000000 -0.107854
> 3: 88408.132: 0.000000 0.000000 -0.236649
> 4: 88408.132: 0.000000 0.000000 -0.236649
> 0: 90869.412: 0.666667 0.0910546 0.000000
> 1: 90447.155: 0.576076 0.000000 -0.301419
> 2: 88292.494: 0.290024 0.000000 -0.0973598
> 3: 88189.600: 0.000000 0.000000 -0.261628
> 4: 88189.600: 0.000000 0.000000 -0.261628
> 0: 91555.700: 0.666667 0.0894430 0.000000
> 1: 91205.211: 0.583675 0.000000 -0.300239
> 2: 88819.778: 0.217977 0.000000 -0.109167
> 3: 88792.163: 0.000000 0.000000 -0.221989
> 4: 88792.163: 0.000000 0.000000 -0.221989
> 0: 90699.353: 0.666667 0.0934181 0.000000
> 1: 90253.805: 0.585847 0.000000 -0.298291
> 2: 88159.371: 0.298780 0.000000 -0.100183
> 3: 88136.845: 0.000000 0.000000 -0.249850
> 4: 88136.845: 0.000000 0.000000 -0.249850
> 0: 91038.775: 0.666667 0.0912136 0.000000
> 1: 90656.405: 0.581786 0.000000 -0.298768
> 2: 88476.176: 0.280341 0.000000 -0.100177
> 3: 88413.623: 0.000000 0.000000 -0.270528
> 4: 88413.623: 0.000000 4.32879e-009 -0.270528
> 5: 88413.623: 0.000000 4.32879e-009 -0.270528
> 0: 90534.064: 0.666667 0.0912434 0.000000
> 1: 90158.703: 0.581799 0.000000 -0.299923
> 2: 88007.169: 0.285653 0.000000 -0.101348
> 3: 87975.548: 0.270763 9.94850e-010 -0.132543
> 4: 87963.471: 0.241261 0.00166749 -0.150478
> 5: 87955.484: 0.212149 0.000000 -0.133227
> 6: 87948.984: 0.188039 0.000000 -0.157996
> 7: 87945.106: 0.157972 2.87461e-009 -0.140942
> 8: 87942.894: 0.135918 0.000655211 -0.167551
> 9: 87942.121: 0.110783 0.000000 -0.144511
> 10: 87941.407: 0.0820679 0.000000 -0.163754
> 11: 87941.374: 0.0874460 0.000000 -0.164379
> 12: 87941.366: 0.0924083 0.000000 -0.165031
> 13: 87941.366: 0.0921629 2.75824e-006 -0.164807
> 14: 87941.365: 0.0918365 0.000000 -0.164754
> 15: 87941.365: 0.0918121 1.05293e-009 -0.164776
> 16: 87941.365: 0.0916407 1.25385e-007 -0.164902
> 17: 87941.365: 0.0916280 0.000000 -0.164885
> 18: 87941.365: 0.0915976 1.78900e-007 -0.164855
> 19: 87941.365: 0.0915710 0.000000 -0.164868
> 20: 87941.365: 0.0915709 6.77374e-008 -0.164868
> 21: 87941.365: 0.0915707 0.000000 -0.164868
> 22: 87941.365: 0.0915706 0.000000 -0.164869
> 23: 87941.365: 0.0915706 0.000000 -0.164869
> 0: 90538.952: 0.666667 0.0906664 0.000000
> 1: 90188.780: 0.580152 0.000000 -0.298077
> 2: 87995.424: 0.280946 0.000000 -0.0982630
> 3: 87965.709: 0.263221 1.10254e-009 -0.127679
> 4: 87950.953: 0.232057 0.00156023 -0.142027
> 5: 87932.225: 0.160075 0.000000 -0.123961
> 6: 87924.828: 0.107481 0.000000 -0.177222
> 7: 87923.913: 0.101794 1.32194e-009 -0.172355
> 8: 87922.762: 0.0898806 0.000000 -0.163290
> 9: 87922.603: 0.0870883 2.97543e-007 -0.162210
> 10: 87921.808: 0.0643480 0.000000 -0.154688
> 11: 87921.756: 0.0619822 1.91616e-007 -0.155062
> 12: 87921.461: 0.0430376 0.000000 -0.157938
> 13: 87921.329: 0.0246316 0.000000 -0.160343
> 14: 87921.290: 0.0115580 0.000000 -0.161904
> 15: 87921.281: 0.00286298 0.000000 -0.162911
> 16: 87921.281: 0.00189741 3.55755e-005 -0.162906
> 17: 87921.280: 0.000934638 0.000000 -0.162938
> 18: 87921.280: 0.000000 0.000000 -0.162968
> 19: 87921.280: 0.000000 0.000000 -0.162968
> 0: 89913.444: 0.666667 0.0906915 0.000000
> 1: 89490.109: 0.578190 0.000000 -0.300666
> 2: 87265.611: 0.273646 0.000000 -0.0992267
> 3: 87184.839: 0.000000 0.000000 -0.273871
> 4: 87184.839: 0.000000 0.000000 -0.273871
> 0: 91445.647: 0.666667 0.0887779 0.000000
> 1: 91205.368: 0.586436 0.000000 -0.295702
> 2: 88923.840: 0.258811 0.000000 -0.103445
> 3: 88907.893: 0.242519 0.000000 -0.128815
> 4: 88901.918: 0.218662 0.000000 -0.147252
> 5: 88895.042: 0.195226 0.000965137 -0.128307
> 6: 88890.006: 0.169408 0.000000 -0.143102
> 7: 88886.667: 0.140591 0.000000 -0.134234
> 8: 88884.703: 0.113739 4.18354e-009 -0.147949
> 9: 88883.819: 0.0890199 0.000000 -0.150225
> 10: 88883.509: 0.0702513 0.000000 -0.151673
> 11: 88883.409: 0.0564583 0.000000 -0.152518
> 12: 88883.380: 0.0469538 0.000000 -0.152913
> 13: 88883.374: 0.0411088 0.000000 -0.152998
> 14: 88883.372: 0.0382539 0.000000 -0.152898
> 15: 88883.372: 0.0371689 0.000000 -0.152722
> 16: 88883.372: 0.0365899 0.000000 -0.152428
> 17: 88883.372: 0.0362285 0.000000 -0.151744
> 18: 88883.371: 0.0366140 0.000000 -0.150915
> 19: 88883.371: 0.0373644 0.000000 -0.150538
> 20: 88883.371: 0.0373648 4.72818e-007 -0.150539
> 21: 88883.371: 0.0375860 0.000000 -0.150728
> 22: 88883.371: 0.0378454 0.000000 -0.150594
> Warning messages:
> 1: In mer_finalize(ans) : false convergence (8)
> 2: In mer_finalize(ans) : singular convergence (7)
> 3: In mer_finalize(ans) : singular convergence (7)
> 4: In mer_finalize(ans) : false convergence (8)
> 5: In mer_finalize(ans) : false convergence (8)
>> print(pool(Uncpde.math.f.mi))
> Call: pool(object = Uncpde.math.f.mi)
>
> Pooled coefficients:
> (Intercept) index
> 1.90889735 0.02891385
>
> Fraction of information about the coefficients missing due to nonresponse:
> (Intercept) index
> 0.9464698 0.9361655
>> round(summary(pool(Uncpde.math.f.mi)), 2)
> est se t df Pr(>|t|) lo 95 hi 95 nmis fmi lambda
> (Intercept) 1.91 0.27 6.98 49.87 0.0 1.36 2.46 NA 0.95 0.94
> index 0.03 0.03 0.85 51.68 0.4 -0.04 0.10 11776 0.94 0.93
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
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