warnings in lmer estimation after mice
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 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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 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-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 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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 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-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: 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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
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