Thank you, I'll post there.

Regards,
Alan


On Wed, Aug 7, 2013 at 1:14 PM, Bert Gunter <gunter.berton@gene.com> wrote:

> I think it's fair to say that this should really be posted on the
> mixed-model specific (especially using lme4) list r-sig-mixed-models
> and not here.
>
> Cheers,
> Bert
>
> On Wed, Aug 7, 2013 at 9:01 AM, Alan Mishler <amishler@umd.edu> wrote:
> > Hi everyone,
> >
> > I'm writing with a question about lmer( ) in the lme4 package. I've
> > searched around for
> > answers and done quite a bit of experimentation with toy data sets to
> > figure out my issue, and I haven't been able to resolve it.
> >
> > I'm running linear mixed effects models on a large, sparse dataset in
> > which I'm regressing reaction time (a continuous variable) on several
> > categorical factors: Block (Block1/Block2/Block3), Group
> > (monolingual/bilingual), and Type (target/nontarget).
> >
> > As a way of examining simple effects, I am dummy-coding specific
> > factors, setting each level of a given factor as the reference level
> > in turn. For example, I generate three models with each of the three
> > levels of Block coded as the reference level, without changing the
> > codings of the other factors:
> >
> > ## Model with Block 1 as reference level
> > contrasts(nback.low$Group) <- c(1, 0) # monoling ref
> > contrasts(nback.low$Type) <- c(1, -1)
> > contrasts(nback.low$Block) <- matrix(c(0, 1, 0, 0, 0, 1), ncol=2) #B1
> > ref, B2: 1, B3: 2
> > glmerNL.RS.SI_RTgxb1 <- glmer(WinRTs~(Group*Block*Type) +
> > (1+Block+Type|Subject) + (1+Group+Block|Item),data=nback.low)
> >
> > ## Model with Block 2 as reference level
> > contrasts(nback.low$Group) <- c(1, 0) # monoling ref
> > contrasts(nback.low$Type) <- c(1, -1)
> > contrasts(nback.low$Block) <- matrix(c(1, 0, 0, 0, 0, 1), ncol=2) #B2
> > ref, B1: 1, B3: 2
> > glmerNL.RS.SI_RTgxb2 <- glmer(WinRTs~(Group*Block*Type) +
> > (1+Block+Type|Subject) + (1+Group+Block|Item),data=nback.low)
> >
> > ## Model with Block 3 as reference level
> > contrasts(nback.low$Group) <- c(1, -1) # monoling 'ref'
> > contrasts(nback.low$Type) <- c(1, 0) # target ref
> > contrasts(nback.low$Block) <- matrix(c(1, 0, 0, 0, 1, 0), ncol=2) #B3
> > ref, B1: 1, B2: 2
> > glmerNL.RS.SI_RTbxt3 <- glmer(WinRTs~(Group*Block*Type) +
> > (1+Block+Type|Subject) + (1+Group+Block|Item),data=nback.low)
> > summary(glmerNL.RS.SI_RTbxt3)
> >
> > The issue I'm having is that contrasts that I believe should be
> > identical are not. Below are summaries of the three
> > models. You can see that the estimate of the fixed effect of Block1
> > (the contrast between Block 1 and Block 2) is -117.98 in the first
> > model and 118.478 in the second model. To my understanding, they
> > should be identical except for the sign. Similar discrepancies can be
> > seen in the other Block contrasts.
> >
> > There are two subjects who have no data at Block 1, so I removed them
> > and re-ran the models, but the same issue occurred. Separately, I
> > removed the random effects for Item, without removing those two
> > subjects, and when I did that the discrepancies disappeared. I have a
> > feeling this means that my models are too complex for my data, but I'm
> > not sure what I should look at to (dis)confirm this hunch or how
> > exactly to proceed if that is the case. (As an example of the
> > sparseness of the data, items are repeated across subjects, but each
> > subject has only one data point per item, or zero data points per item
> > for trials where they didn't respond correctly. However, I didn't get
> > any warnings about model convergence, or any warnings at
> > all.)
> >
> > Any clues  as to why I'm getting this results would be
> > very much appreciated.
> >
> > Thanks in advance,
> >
> > Alan Mishler
> > Research Assistant
> > University of Maryland
> > --
> > ## Model 1 output: Block 1 as reference level ##
> >> glmerNL.RS.SI_RTgxb1
> > Linear mixed model fit by REML
> > Formula: WinRTs ~ (Group * Block * Type) + (1 + Block + Type |
> > Subject) +      (1 + Group + Block | Item)
> >    Data: nback.low
> >     AIC    BIC logLik deviance REMLdev
> >  181965 182210 -90949   181990  181899
> >
> > Random effects:
> >  Groups   Name        Variance  Std.Dev. Corr
> >  Item     (Intercept)   6134.64  78.324
> >           Group1        2336.31  48.335  -1.000
> >           Block1        1759.56  41.947   1.000 -1.000
> >           Block2         846.06  29.087   0.771 -0.771  0.771
> >  Subject  (Intercept) 132462.22 363.954
> >           Block1        6011.83  77.536  -0.034
> >           Block2       10883.42 104.324  -0.127  0.915
> >           Type1        13653.97 116.850  -0.194  0.260  0.109
> >  Residual              97048.54 311.526
> > Number of obs: 12640, groups: Item, 288; Subject, 52
> >
> > Fixed effects:
> >                     Estimate Std. Error t value
> > (Intercept)          1016.73      73.39  13.855
> > Group1                 12.39     101.67   0.122
> > Block1               -117.98      18.97  -6.220
> > Block2               -175.96      23.60  -7.455
> > Type1                -136.36      24.96  -5.463
> > Group1:Block1          46.43      26.05   1.782
> > Group1:Block2          76.70      32.53   2.358
> > Group1:Type1           20.55      34.15   0.602
> > Block1:Type1           62.16      10.47   5.934
> > Block2:Type1           96.66      10.46   9.243
> > Group1:Block1:Type1   -18.39      14.05  -1.309
> > Group1:Block2:Type1   -39.94      14.13  -2.826
> >
> > Correlation of Fixed Effects:
> >             (Intr) Group1 Block1 Block2 Type1  Gr1:B1 Gr1:B2 Gr1:T1
> > Bl1:T1 Bl2:T1 G1:B1:
> > Group1      -0.721
> > Block1      -0.065  0.049
> > Block2      -0.146  0.106  0.815
> > Type1       -0.186  0.134  0.219  0.106
> > Grop1:Blck1  0.053 -0.074 -0.715 -0.587 -0.160
> > Grop1:Blck2  0.108 -0.150 -0.586 -0.721 -0.077  0.818
> > Group1:Typ1  0.136 -0.189 -0.160 -0.078 -0.721  0.226  0.110
> > Block1:Typ1  0.012 -0.009 -0.086 -0.038 -0.163  0.063  0.028  0.131
> > Block2:Typ1  0.013 -0.009 -0.051 -0.070 -0.189  0.037  0.051  0.144
> > 0.533
> > Grp1:Bl1:T1 -0.009  0.013  0.064  0.028  0.153 -0.090 -0.043 -0.216
> > -0.702 -0.371
> > Grp1:Bl2:T1 -0.010  0.014  0.038  0.052  0.156 -0.055 -0.072 -0.218
> > -0.371 -0.717  0.527
> >
> > ## Model 2 output: Block 2 as reference level ##
> >> glmerNL.RS.SI_RTgxb2 [Block 2 as reference level]
> > Linear mixed model fit by REML
> > Formula: WinRTs ~ (Group * Block * Type) + (1 + Block + Type |
> > Subject) +      (1 + Group + Block | Item)
> >    Data: nback.low
> >     AIC    BIC logLik deviance REMLdev
> >  181931 182177 -90933   181957  181865
> >
> > Random effects:
> >  Groups   Name        Variance  Std.Dev. Corr
> >  Item     (Intercept)  14663.50 121.093
> >           Group1        2400.09  48.991  -1.000
> >           Block1        6561.73  81.004  -0.582  0.582
> >           Block2         590.58  24.302  -0.677  0.677 -0.205
> >  Subject  (Intercept) 136638.85 369.647
> >           Block1        5868.05  76.603  -0.172
> >           Block2        2128.70  46.138  -0.146 -0.388
> >           Type1        13788.61 117.425  -0.140 -0.259 -0.188
> >  Residual              95743.04 309.424
> > Number of obs: 12640, groups: Item, 288; Subject, 52
> >
> > Fixed effects:
> >                     Estimate Std. Error t value
> > (Intercept)          898.925     74.610  12.048
> > Group1                58.779    103.098   0.570
> > Block1               118.478     19.267   6.149
> > Block2               -58.059     13.680  -4.244
> > Type1                -74.323     25.553  -2.909
> > Group1:Block1        -46.515     25.799  -1.803
> > Group1:Block2         30.232     18.734   1.614
> > Group1:Type1           2.166     34.141   0.063
> > Block1:Type1         -64.291     11.246  -5.717
> > Block2:Type1          33.892     10.045   3.374
> > Group1:Block1:Type1   19.392     13.995   1.386
> > Group1:Block2:Type1  -21.160     13.634  -1.552
> >
> > Correlation of Fixed Effects:
> >             (Intr) Group1 Block1 Block2 Type1  Gr1:B1 Gr1:B2 Gr1:T1
> > Bl1:T1 Bl2:T1 G1:B1:
> > Group1      -0.720
> > Block1      -0.183  0.126
> > Block2      -0.152  0.108 -0.033
> > Type1       -0.132  0.096 -0.174 -0.095
> > Grop1:Blck1  0.126 -0.176 -0.699  0.019  0.130
> > Grop1:Blck2  0.106 -0.147  0.021 -0.722  0.070 -0.030
> > Group1:Typ1  0.099 -0.137  0.130  0.071 -0.714 -0.188 -0.102
> > Block1:Typ1  0.009 -0.007 -0.079 -0.049 -0.239  0.059  0.036  0.148
> > Block2:Typ1  0.010 -0.008 -0.036 -0.107 -0.219  0.027  0.079  0.152
> > 0.416
> > Grp1:Bl1:T1 -0.008  0.010  0.063  0.040  0.137 -0.092 -0.050 -0.194
> > -0.655 -0.345
> > Grp1:Bl2:T1 -0.008  0.010  0.026  0.079  0.141 -0.036 -0.101 -0.200
> > -0.315 -0.722  0.482
> >
> > ## Model 3 output: Block 3 as reference level ##
> >> glmerNL.RS.SI_RTgxb3 [Block 3 as reference level]
> > Linear mixed model fit by REML
> > Formula: WinRTs ~ (Group * Block * Type) + (1 + Block + Type |
> > Subject) +      (1 + Group + Block | Item)
> >    Data: nback.low
> >     AIC    BIC logLik deviance REMLdev
> >  181932 182177 -90933   181958  181866
> >
> > Random effects:
> >  Groups   Name        Variance  Std.Dev. Corr
> >  Item     (Intercept)  11296.69 106.286
> >           Group1        2419.00  49.183  -1.000
> >           Block1        7418.92  86.133  -0.522  0.522
> >           Block2         494.56  22.239   0.596 -0.596  0.373
> >  Subject  (Intercept) 133759.20 365.731
> >           Block1       10713.51 103.506  -0.153
> >           Block2        2123.58  46.082   0.021  0.733
> >           Type1        13764.21 117.321  -0.165 -0.107  0.189
> >  Residual              95762.39 309.455
> > Number of obs: 12640, groups: Item, 288; Subject, 52
> >
> > Fixed effects:
> >                     Estimate Std. Error t value
> > (Intercept)           840.86      73.76  11.399
> > Group1                 89.07     102.02   0.873
> > Block1                176.58      23.92   7.381
> > Block2                 58.07      13.66   4.251
> > Type1                 -40.40      25.30  -1.597
> > Group1:Block1         -76.71      32.29  -2.376
> > Group1:Block2         -30.30      18.72  -1.618
> > Group1:Type1          -18.88      34.08  -0.554
> > Block1:Type1          -98.00      11.46  -8.555
> > Block2:Type1          -33.93      10.03  -3.382
> > Group1:Block1:Type1    40.12      14.06   2.853
> > Group1:Block2:Type1    21.06      13.63   1.545
> >
> > Correlation of Fixed Effects:
> >             (Intr) Group1 Block1 Block2 Type1  Gr1:B1 Gr1:B2 Gr1:T1
> > Bl1:T1 Bl2:T1 G1:B1:
> > Group1      -0.720
> > Block1      -0.170  0.119
> > Block2      -0.031  0.024  0.595
> > Type1       -0.156  0.113 -0.073  0.138
> > Grop1:Blck1  0.119 -0.164 -0.706 -0.434  0.054
> > Grop1:Blck2  0.026 -0.034 -0.429 -0.723 -0.101  0.603
> > Group1:Typ1  0.116 -0.161  0.054 -0.102 -0.717 -0.079  0.142
> > Block1:Typ1  0.009 -0.006 -0.063 -0.045 -0.231  0.047  0.034  0.148
> > Block2:Typ1  0.009 -0.007 -0.032 -0.107 -0.177  0.024  0.079  0.138
> > 0.461
> > Grp1:Bl1:T1 -0.007  0.009  0.052  0.037  0.141 -0.073 -0.048 -0.195
> > -0.651 -0.357
> > Grp1:Bl2:T1 -0.007  0.009  0.025  0.079  0.144 -0.030 -0.101 -0.200
> > -0.324 -0.723  0.490
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
> Internal Contact Info:
> Phone: 467-7374
> Website:
>
> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
>

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