[R-sig-ME] mixed-models problem

[Volker Dellwo]

Dear R people,

I am new to R and mixed models and receive rather strange and 
contradictory results for a data analysis. Below is some R code with 
detailed comments. The data file is attached to the mail.

I would be very grateful for any suggestions!! I appreciate that this is 
probably a longer question than typically found on this list but it is 
important to me. So if anyone wants to offer me some paid assistance 
with this problem (e.g via Skype) I am more than happy to receive your 
suggestions.

With many many thanks in advance for your help!
Yours,
Volker

### R code starts here ####

# In an experiment, 15 speakers read a text consisting of 7 sentences 
under 5 different intended tempo categories, so we have three factors: 
tempo, speaker and sentence. We measured the percentage over which 
speech is vocalic (%V) for each sentence so N = 525 (15 speakers x 5 
tempos x 7 sentences). I am now particularly interested in between 
speaker differences of %V. Here is what I did:

rm(list = ls())
bt.data <- read.table("/Users/Flok/Desktop/BonnTempoData.txt",header=TRUE);

speaker <- factor(bt.data$speaker);
sentence <- factor(bt.data$sentence);
tempo <- factor(bt.data$tempo);
percentV <- bt.data$percentV
percentV_zScore <- bt.data$percentV_zScore

summary(speaker)
summary(sentence)
summary(tempo)
length(percentV)

     # (1) Descriptives:

             # Descriptively the differences between speakers seem 
strong. Sentence differences also look strong but there seems to be no 
difference in %V between the five different tempo conditions as you can 
see in the following box-plots:
             boxplot(percentV ~ tempo)
             boxplot(percentV ~ speaker)
             boxplot(percentV ~ sentence)

     # (2) Inferentials:

             #Inferentially I first tried an ANOVA which basically 
confrims all the descriptive results above. But then, ANOVA does not 
seem to be an appropriate model here as the assumption of data 
independence is violated (each subject reads the same sentences 
repeatedly). So I have been recommended to use linear mixed models, 
which apparently gets more and more popular in my field. These models, 
however, tell me a very differnt story of what I can see in the data:

         # (2.1) Use lme4 package for analysis:
         library(lme4);
         library(languageR);

             # Surprisingly there is a strong effect of tempo:
             modelA <- lmer(percentV ~ tempo + (1|speaker) + 
(1|sentence), data=bt.data);
             pvals.modelA <- pvals.fnc(modelA);
             print(pvals.modelA)

             # Again, surprisingly there is no effect of seaker:
             modelB <- lmer(percentV ~ speaker + (1|tempo) + 
(1|sentence), data=bt.data);
             pvals.modelB <- pvals.fnc(modelB);
             print(pvals.modelB)

             # And again, no effect of sentence:
             modelC <- lmer(percentV ~ sentence + (1|tempo) + 
(1|sentence), data=bt.data);
             pvals.modelC <- pvals.fnc(modelC);
             print(pvals.modelC)

             # Comparing two models with and without speaker as a fixed 
effect comes to the same result:
             modelD <- lmer(percentV ~ 1 + (1|tempo) + (1|sentence), 
data=bt.data);
             anova.BvsD <- anova(modelB, modelD);
             print(anova.BvsD)

         # (2.2) Using the nlme package:
         library(nlme);

             # Now someone suggested to me to test the effects of 
speaker by adding speaker and sentence as a random factor in one model 
and excluding them consecutively in others and the comparing the models. 
When I do that, speaker is significant:

             modelA <- lme(percentV ~ tempo, 
random=list((~1|speaker),(~1|sentence)), data=bt.data, 
na.action=na.omit, method="ML");

             modelB <- lme(percentV ~ tempo, random=list((~1|sentence)), 
data=bt.data, na.action=na.omit, method="ML");

             modelC <- lme(percentV ~ tempo, random=list((~1|sentence)), 
data=bt.data, na.action=na.omit, method="ML");

             # Now we still get main effects for tempo:
             anova.modelA <- anova(modelA)
             print(anova.modelA)

             # But we also get effects for speaker and tempo when 
contrasting the models:
             comparison.AvsB <- anova(modelA, modelB);
             print(comparison.AvsB)
             comparison.AvsC <- anova(modelA, modelC);
             print(comparison.AvsC)

# So I have two questions:

     # (a) Why are the effects of the mixed-models so much different 
from what I can see descriptively? I would certainly not expect an 
effect of tempo here at all but possibly speaker and/or sentence.

     # (b) Why do I get to categorically different results depending in 
the method I choose (2.1: speaker and sentence are not significant, 2.2 
speaker and sentence are significant)

# I'm extremely greatful for any comments and/or help!


-------------- next part --------------
speaker	tempo	sentence	percentV	percentV_zScore
10	1	1	38.90868389	-0.581041923
10	1	2	46.89079638	0.010657968
10	1	3	45.07412233	-0.572797337
10	1	4	42.79976052	0.346660179
10	1	5	43.63491409	-0.529863443
10	1	6	34.5906477	-0.795338428
10	1	7	43.2971423	0.094017374
11	1	1	38.67466301	-0.634629198
11	1	2	40.71810932	-1.205531824
11	1	3	47.32369919	-0.104662568
11	1	4	34.60527629	-1.161789475
11	1	5	42.16068757	-0.867815669
11	1	6	34.9158439	-0.70743823
11	1	7	41.38990857	-0.354609154
12	1	1	39.02359271	-0.554729526
12	1	2	29.17358286	-3.480122311
12	1	3	40.58417884	-1.507150235
12	1	4	32.93516835	-1.469224778
12	1	5	39.58336524	-1.458641984
12	1	6	34.80753081	-0.736715148
12	1	7	37.87122487	-1.182286867
13	1	1	42.69462396	0.285881601
13	1	2	40.78154473	-1.193033297
13	1	3	50.13424228	0.480208732
13	1	4	42.01176398	0.20160491
13	1	5	46.46312379	0.118476386
13	1	6	38.58510967	0.284360513
13	1	7	47.0012523	0.965311742
14	1	1	42.8129084	0.312966964
14	1	2	48.0707615	0.243143679
14	1	3	48.77469408	0.197288066
14	1	4	38.5214839	-0.440889678
14	1	5	46.24096508	0.067548643
14	1	6	33.13491189	-1.188822314
14	1	7	43.25311914	0.083662083
15	1	1	46.52104222	1.162074025
15	1	2	52.92729754	1.200015381
15	1	3	54.21750703	1.329932177
15	1	4	47.7835631	1.264084029
15	1	5	50.18060798	0.970673824
15	1	6	40.41895495	0.780046996
15	1	7	46.90765539	0.943295533
16	1	1	46.34613543	1.12202299
16	1	2	56.68248055	1.939890124
16	1	3	53.89094	1.261973893
16	1	4	42.13591365	0.224458519
16	1	5	39.90129401	-1.385759877
16	1	6	39.46997063	0.523537515
16	1	7	58.23507935	3.60777383
17	1	1	37.49588084	-0.904552644
17	1	2	49.2611641	0.477685861
17	1	3	40.40458695	-1.544523133
17	1	4	30.66076707	-1.887899063
17	1	5	42.94087871	-0.688964366
17	1	6	30.6927427	-1.848938121
17	1	7	38.03601603	-1.143524086
18	1	1	42.75660142	0.30007351
18	1	2	37.50192341	-1.839209256
18	1	3	44.27988005	-0.738078387
18	1	4	30.79265403	-1.863621167
18	1	5	40.65326721	-1.213377266
18	1	6	33.7055995	-1.03456604
18	1	7	40.98608657	-0.449597649
19	1	1	41.92621739	0.109928016
19	1	2	53.88913844	1.389524597
19	1	3	53.65088256	1.212018172
19	1	4	48.56991745	1.408837002
19	1	5	51.95226977	1.376810223
19	1	6	43.43645378	1.595673809
19	1	7	49.06236192	1.450133523
20	1	1	44.43762595	0.685002929
20	1	2	48.04944731	0.238944195
20	1	3	53.87776918	1.259233057
20	1	4	47.84846523	1.276031285
20	1	5	49.35131913	0.780567345
20	1	6	41.86015416	1.169601653
20	1	7	47.70362943	1.130527472
21	1	1	40.98264462	-0.106136005
21	1	2	46.52395216	-0.061620472
21	1	3	50.93643644	0.647144559
21	1	4	36.8403171	-0.750360709
21	1	5	46.26733929	0.073594675
21	1	6	39.4548309	0.519445263
21	1	7	47.36590281	1.051086176
22	1	1	41.68567373	0.054847123
22	1	2	43.45765766	-0.665765157
22	1	3	44.04044034	-0.787905558
22	1	4	38.60152009	-0.426156529
22	1	5	43.1317074	-0.645218729
22	1	6	31.0583696	-1.750109547
22	1	7	40.41968015	-0.582829851
23	1	1	33.70000224	-1.773751944
23	1	2	39.44211244	-1.456938445
23	1	3	47.62971097	-0.040981813
23	1	4	39.20669486	-0.314755292
23	1	5	43.14575774	-0.641997823
23	1	6	37.46279979	-0.018998687
23	1	7	45.17027309	0.534622082
24	1	1	40.74892025	-0.159655382
24	1	2	39.00559153	-1.542945117
24	1	3	46.53480493	-0.268830701
24	1	4	36.59664158	-0.795216766
24	1	5	42.93288387	-0.690797107
24	1	6	35.9294384	-0.433464685
24	1	7	40.0175229	-0.677426757
10	2	1	40.61593487	-0.190107041
10	2	2	48.90090604	0.406705076
10	2	3	45.78686163	-0.424476979
10	2	4	38.17144245	-0.505325693
10	2	5	49.92442719	0.911946847
10	2	6	32.88943368	-1.255174818
10	2	7	43.94697113	0.246872498
11	2	1	37.06278539	-1.003725011
11	2	2	41.50464241	-1.050563088
11	2	3	48.75643158	0.193487657
11	2	4	39.77709313	-0.209755753
11	2	5	44.26694402	-0.384976669
11	2	6	35.73153805	-0.486956946
11	2	7	38.26654645	-1.089297874
12	2	1	38.36162898	-0.706309297
12	2	2	43.87322264	-0.583887381
12	2	3	40.70057494	-1.482928318
12	2	4	35.23153482	-1.046507117
12	2	5	39.47231161	-1.484099961
12	2	6	34.48706438	-0.823336891
12	2	7	40.9343592	-0.461765152
13	2	1	37.13684856	-0.986765655
13	2	2	46.00701363	-0.163471636
13	2	3	51.14176536	0.68987331
13	2	4	40.97237614	0.010273255
13	2	5	46.41551545	0.107562633
13	2	6	38.42606318	0.241370412
13	2	7	47.7483321	1.141042598
14	2	1	35.08426879	-1.456775654
14	2	2	48.65456255	0.358168607
14	2	3	41.7295252	-1.268804763
14	2	4	38.11688181	-0.515369276
14	2	5	44.71619888	-0.281989319
14	2	6	34.81606418	-0.734408588
14	2	7	44.46330003	0.368325281
15	2	1	49.22592826	1.78145238
15	2	2	46.16399411	-0.132542147
15	2	3	57.25149972	1.96130312
15	2	4	45.11197175	0.772294561
15	2	5	54.68057104	2.002247006
15	2	6	38.06616592	0.144090555
15	2	7	48.1591487	1.237676389
16	2	1	47.86379265	1.469543746
16	2	2	47.10840744	0.053533357
16	2	3	49.80432767	0.41155382
16	2	4	37.75029327	-0.582851296
16	2	5	45.61035639	-0.077012325
16	2	6	31.24365964	-1.700025841
16	2	7	47.8504308	1.16505863
17	2	1	36.62722943	-1.103460796
17	2	2	44.24219911	-0.511188828
17	2	3	42.2432044	-1.161908619
17	2	4	30.54025358	-1.91008332
17	2	5	42.96862513	-0.682603768
17	2	6	34.55079063	-0.806111752
17	2	7	37.06146441	-1.3727617
18	2	1	37.72604128	-0.851849354
18	2	2	38.13167509	-1.715130786
18	2	3	45.24463637	-0.53731353
18	2	4	34.51455433	-1.178489673
18	2	5	39.65049437	-1.44325328
18	2	6	34.01121183	-0.951959344
18	2	7	40.17003543	-0.641552198
19	2	1	42.61893148	0.268549157
19	2	2	52.90346681	1.195320068
19	2	3	51.95715269	0.859554625
19	2	4	47.98563207	1.301281107
19	2	5	53.81823345	1.804564421
19	2	6	41.19799375	0.990620379
19	2	7	47.06651322	0.980662656
20	2	1	41.46750524	0.004889809
20	2	2	50.30337067	0.683029335
20	2	3	50.31834647	0.518520636
20	2	4	46.3506281	1.000307782
20	2	5	48.86323046	0.668677719
20	2	6	40.20762372	0.722924383
20	2	7	43.3100392	0.09705103
21	2	1	41.1510144	-0.06758185
21	2	2	49.56332308	0.537219524
21	2	3	52.75585918	1.025764673
21	2	4	42.93992713	0.372462202
21	2	5	45.44190073	-0.115629161
21	2	6	39.76252862	0.602615637
21	2	7	47.27001628	1.028531394
22	2	1	40.36274437	-0.248083871
22	2	2	46.44787779	-0.076609222
22	2	3	42.03580596	-1.205068034
22	2	4	35.91343466	-0.920982247
22	2	5	42.85608329	-0.7084029
22	2	6	35.12614063	-0.650595242
22	2	7	41.19896057	-0.399524644
23	2	1	34.48721641	-1.593491698
23	2	2	47.36714683	0.104512162
23	2	3	48.57905002	0.156574726
23	2	4	38.21041225	-0.49815209
23	2	5	45.38553188	-0.128551178
23	2	6	34.16434614	-0.9105673
23	2	7	46.21530536	0.780438414
24	2	1	36.13480982	-1.216217505
24	2	2	42.68335359	-0.818324438
24	2	3	42.524971	-1.103273263
24	2	4	37.07422767	-0.707302195
24	2	5	43.41114767	-0.581159739
24	2	6	32.33720783	-1.404440896
24	2	7	40.64684279	-0.529395819
10	3	1	41.60401651	0.036148843
10	3	2	47.78801377	0.187434569
10	3	3	48.71615153	0.185105418
10	3	4	40.30255334	-0.113028461
10	3	5	50.03537458	0.937380466
10	3	6	35.55761296	-0.533968719
10	3	7	41.79220635	-0.259979193
11	3	1	40.20427112	-0.284371868
11	3	2	51.13403253	0.846692658
11	3	3	48.10545938	0.058020971
11	3	4	42.73956871	0.335580005
11	3	5	47.38783101	0.330456609
11	3	6	36.76742443	-0.206957928
11	3	7	41.34687347	-0.36473203
12	3	1	36.82352642	-1.058511727
12	3	2	45.54286113	-0.254922495
12	3	3	40.5950885	-1.504879944
12	3	4	34.23831665	-1.229339808
12	3	5	45.41526087	-0.121736093
12	3	6	35.20932005	-0.628111931
12	3	7	36.68148884	-1.46214095
13	3	1	39.12927049	-0.530530898
13	3	2	46.0497498	-0.15505143
13	3	3	46.01292904	-0.377432569
13	3	4	41.06228917	0.026824545
13	3	5	45.79679272	-0.034273592
13	3	6	38.43230879	0.243058593
13	3	7	45.42833892	0.595325275
14	3	1	38.80750883	-0.604209495
14	3	2	45.37650959	-0.287698342
14	3	3	45.37131723	-0.510951365
14	3	4	32.97599949	-1.461708538
14	3	5	46.89851182	0.218284898
14	3	6	33.35101572	-1.13040967
14	3	7	40.30358057	-0.610139219
15	3	1	52.30806873	2.487216346
15	3	2	57.61333685	2.123294517
15	3	3	52.29655698	0.930184328
15	3	4	47.92218784	1.289602225
15	3	5	55.33797459	2.152950437
15	3	6	40.98619758	0.933372092
15	3	7	46.64572848	0.881684122
16	3	1	44.93007909	0.797767315
16	3	2	48.37466705	0.303021464
16	3	3	52.4178145	0.955417901
16	3	4	41.98524757	0.196723741
16	3	5	45.50432277	-0.101319512
16	3	6	39.70561346	0.587231528
16	3	7	42.36967972	-0.124143783
17	3	1	40.43492165	-0.231556355
17	3	2	41.84095754	-0.98429972
17	3	3	45.03022104	-0.581933154
17	3	4	37.19672449	-0.684752846
17	3	5	40.47425433	-1.254414243
17	3	6	36.35019691	-0.319734096
17	3	7	34.90373158	-1.880311547
18	3	1	32.24685639	-2.106500561
18	3	2	40.45859567	-1.25666318
18	3	3	43.68961816	-0.860911316
18	3	4	36.66671219	-0.782318091
18	3	5	41.89772556	-0.928097176
18	3	6	32.60373024	-1.332400163
18	3	7	40.88012554	-0.474522194
19	3	1	42.01104394	0.129352023
19	3	2	52.95841525	1.206146429
19	3	3	49.2727081	0.3009243
19	3	4	43.77066145	0.525384689
19	3	5	50.20742648	0.976821708
19	3	6	38.74729003	0.328197696
19	3	7	39.99347344	-0.683083759
20	3	1	42.02080408	0.131586949
20	3	2	44.55145484	-0.450256909
20	3	3	55.30997409	1.557273514
20	3	4	46.81359192	1.08553067
20	3	5	52.19313581	1.432026441
20	3	6	38.74432976	0.327397539
20	3	7	44.97075369	0.487690395
21	3	1	43.5613332	0.484345024
21	3	2	49.42870689	0.510696417
21	3	3	53.9742568	1.279312039
21	3	4	44.65460526	0.688102036
21	3	5	45.27539708	-0.153798519
21	3	6	41.99749968	1.206726005
21	3	7	46.93612591	0.949992473
22	3	1	40.75444294	-0.15839077
22	3	2	47.68951023	0.168026652
22	3	3	43.35066375	-0.931447401
22	3	4	38.13753868	-0.511566737
22	3	5	43.40914222	-0.581619467
22	3	6	35.71808341	-0.490593721
22	3	7	42.55764775	-0.079929253
23	3	1	40.06442369	-0.316394835
23	3	2	49.1488156	0.455550104
23	3	3	51.90064498	0.847795425
23	3	4	44.83359977	0.721051541
23	3	5	48.95085962	0.688765859
23	3	6	42.17319055	1.254215064
23	3	7	44.43427933	0.361498925
24	3	1	44.59802164	0.721731138
24	3	2	45.66201795	-0.23144531
24	3	3	48.26683992	0.091604107
24	3	4	45.17983688	0.784787248
24	3	5	44.57489842	-0.314381089
24	3	6	38.06660941	0.144210429
24	3	7	37.99402653	-1.153401009
10	4	1	42.19785906	0.172129885
10	4	2	48.79755694	0.38634245
10	4	3	46.49533194	-0.277044991
10	4	4	42.26207788	0.247682968
10	4	5	48.96483251	0.691969011
10	4	6	38.44936291	0.247668305
10	4	7	40.33791401	-0.602063181
11	4	1	38.04948749	-0.777785019
11	4	2	44.17964214	-0.523514277
11	4	3	50.56095808	0.569007877
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10	5	1	40.54202887	-0.207030407
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13	5	6	41.15455459	0.978878818
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14	5	1	39.61716537	-0.418810286
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14	5	5	46.4782587	0.121945919
14	5	6	42.00962347	1.210003053
14	5	7	39.01070487	-0.914254195
15	5	1	56.60055566	3.47013151
15	5	2	52.79120294	1.173200987
15	5	3	52.65467384	1.004708101
15	5	4	50.46742951	1.758133106
15	5	5	50.67547283	1.084116824
15	5	6	43.13359362	1.513811022
15	5	7	48.92550143	1.417940695
16	5	1	48.14918748	1.534894882
16	5	2	51.42680327	0.904376577
16	5	3	53.78497894	1.239923499
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16	5	6	33.49768939	-1.09076393
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18	5	5	33.73332255	-2.799707916
18	5	6	34.9313835	-0.703237895
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19	5	1	45.85808008	1.010265632
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19	5	5	47.81329087	0.427989182
19	5	6	36.96674361	-0.15308216
19	5	7	42.93002443	0.007662558
20	5	1	47.25757523	1.330729042
20	5	2	59.81540954	2.557163643
20	5	3	57.3047808	1.972390861
20	5	4	50.14569091	1.698907111
20	5	5	53.89149702	1.821359389
20	5	6	47.66387406	2.738341141
20	5	7	50.18801982	1.714914911
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23	5	3	53.67596873	1.21723858
23	5	4	51.56184091	1.959593557
23	5	5	51.38640545	1.247091279
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24	5	7	40.03392548	-0.673568482