[R-lang] Re: Investigating random slope variance
T. Florian Jaeger
tiflo@csli.stanford.edu
Thu Apr 3 21:10:31 PDT 2014
Hi Titus,
just a quick comment in reply to:
On Thu, Apr 3, 2014 at 2:31 PM, Titus von der Malsburg
<malsburg@posteo.de>wrote:
>
> It seems that the fixed-effects model is more consistent with the
> descriptive stats:
>
I would be careful making anything out of this. The BLUP estimates of the
random effects (and, I assume, their distribution) are affected by
shrinkage, which is often a desirable (conservative) feature, although it
will make differences appear smaller. So, it's not surprising that the
fixed effect model mirrors the empirical means more closely. That doesn't
mean though that it's the better model to draw conclusion from (about those
differences).
Florian
>
> > with(d, tapply(trt, list(item, cond), mean, na.rm=T))
> A B
> 1 1165.3636 1128.5652
> 2 992.5455 1144.6087
> 3 602.1818 583.0909
> 4 613.9048 719.3913
> 5 599.8182 646.8696
> 6 406.9048 489.2174
> 7 620.0000 589.0435
> 8 644.5000 763.8696
> 9 576.3182 631.8696
> 10 596.3182 600.7826
> 11 806.8182 660.3913 * signf. in fixef-mode
> 12 442.9524 552.4783
> 13 1084.0000 1008.9130
> 14 994.4091 878.1739
> 15 898.4545 797.3913
> 16 1037.9545 1113.6087
> 17 1186.4545 1162.0435
> 18 608.6818 786.2174
> 19 582.6818 647.2727
> 20 617.4545 618.2609
> 21 434.7727 642.8095
> 22 1179.8182 1031.2609
> 23 528.2727 721.2609 * signf. in fixef-mode
> 24 571.5455 600.5909
> 25 319.6190 386.0435 * signf. in dotplot
> 26 851.6364 713.3913
> 27 1528.5909 1486.6957
> 28 720.3182 603.8261
> 29 726.9091 773.9565
> 30 381.8095 452.8636
> 31 846.6818 976.2273
> 32 634.2273 878.5652
> 33 740.1818 748.4348
> 34 713.7727 879.3913
> 35 720.8182 1052.8696 * signf. in fixef-mode
> 36 1216.5909 921.2174 * signf. in fixef-mode and dotplot
> 37 594.8636 588.9565
> 38 459.5909 624.8261
> 39 690.1818 885.2727
> 40 449.6818 628.0870
>
> > One last thing -- I would recommend that you double-check all your
> > analyses using lme4.0. People have been reporting odd and
> > contradictory results with the newest version of lme4, especially when
> > using the default optimizer.
>
> I reran the models using the bobyqa and optimx (method="nlminb") and got
> the same results.
>
> Titus
>
>
> Summary of model using sum-coded items as fixed effect:
>
> Linear mixed model fit by maximum likelihood ['lmerMod']
> Formula: log(trt) ~ item * cond + (1 | subj)
> Data: d
> Control: lmerControl(optimizer = "bobyqa")
>
> AIC BIC logLik deviance df.resid
> 2311.5 2761.5 -1073.7 2147.5 1705
>
> Scaled residuals:
> Min 1Q Median 3Q Max
> -3.8821 -0.6375 -0.0172 0.6402 3.5181
>
> Random effects:
> Groups Name Variance Std.Dev.
> subj (Intercept) 0.07732 0.2781
> Residual 0.18107 0.4255
> Number of obs: 1787, groups: subj, 45
>
> Fixed effects:
> Estimate Std. Error t value
> (Intercept) 6.446746 0.042668 151.09
> item1 0.481033 0.062657 7.68
> item2 0.381353 0.062657 6.09
> item3 0.214172 0.063347 -3.38
> item4 0.065494 0.063414 -1.03
> item5 0.164766 0.062657 -2.63
> item6 0.489277 0.063414 -7.72
> item7 0.103389 0.062657 -1.65
> item8 0.007107 0.062657 0.11
> item9 0.151968 0.062657 -2.43
> item10 0.187189 0.062657 -2.99
> item11 0.022083 0.062657 0.35
> item12 0.342824 0.063414 -5.41
> item13 0.421248 0.062657 6.72
> item14 0.265824 0.062657 4.24
> item15 0.095553 0.062657 1.53
> item16 0.428998 0.062657 6.85
> item17 0.508100 0.062657 8.11
> item18 0.104577 0.062657 -1.67
> item19 0.189842 0.063348 -3.00
> item20 0.176217 0.062657 -2.81
> item21 0.346756 0.064095 -5.41
> item22 0.464940 0.062657 7.42
> item23 0.211371 0.062657 -3.37
> item24 0.239727 0.063348 -3.78
> item25 0.702424 0.063414 -11.08
> item26 0.074705 0.062657 1.19
> item27 0.778773 0.062657 12.43
> item28 0.065349 0.062657 -1.04
> item29 0.076321 0.062657 1.22
> item30 0.529148 0.064096 -8.26
> item31 0.253587 0.063347 4.00
> item32 0.028338 0.062657 -0.45
> item33 0.062202 0.062657 0.99
> item34 0.110623 0.062657 1.77
> item35 0.205651 0.062657 3.28
> item36 0.411933 0.062657 6.57
> item37 0.237561 0.062657 -3.79
> item38 0.293674 0.062657 -4.69
> item39 0.104801 0.063348 1.65
> condB-A 0.062645 0.085337 0.73
> item1:condB-A -0.099294 0.125314 -0.79
> item2:condB-A 0.116658 0.125314 0.93
> item3:condB-A -0.124696 0.126694 -0.98
> item4:condB-A 0.169267 0.126827 1.33
> item5:condB-A 0.010062 0.125314 0.08
> item6:condB-A 0.029288 0.126827 0.23
> item7:condB-A -0.159953 0.125314 -1.28
> item8:condB-A 0.082437 0.125314 0.66
> item9:condB-A -0.088934 0.125314 -0.71
> item10:condB-A -0.031815 0.125314 -0.25
> item11:condB-A -0.315189 0.125314 -2.52
> item12:condB-A 0.196759 0.126827 1.55
> item13:condB-A -0.138070 0.125314 -1.10
> item14:condB-A -0.193227 0.125314 -1.54
> item15:condB-A -0.188834 0.125314 -1.51
> item16:condB-A -0.023788 0.125314 -0.19
> item17:condB-A -0.052790 0.125314 -0.42
> item18:condB-A 0.037642 0.125314 0.30
> item19:condB-A -0.011895 0.126697 -0.09
> item20:condB-A -0.135061 0.125314 -1.08
> item21:condB-A 0.211311 0.128191 1.65
> item22:condB-A -0.184232 0.125314 -1.47
> item23:condB-A 0.319074 0.125314 2.55
> item24:condB-A -0.018217 0.126697 -0.14
> item25:condB-A 0.216689 0.126827 1.71
> item26:condB-A -0.192187 0.125314 -1.53
> item27:condB-A -0.073881 0.125314 -0.59
> item28:condB-A -0.147357 0.125314 -1.18
> item29:condB-A -0.055930 0.125314 -0.45
> item30:condB-A 0.091677 0.128193 0.72
> item31:condB-A 0.055254 0.126694 0.44
> item32:condB-A 0.170249 0.125314 1.36
> item33:condB-A -0.021665 0.125314 -0.17
> item34:condB-A 0.075640 0.125314 0.60
> item35:condB-A 0.261821 0.125314 2.09
> item36:condB-A -0.316347 0.125314 -2.52
> item37:condB-A 0.001981 0.125314 0.02
> item38:condB-A 0.157791 0.125314 1.26
> item39:condB-A 0.105193 0.126697 0.83
>
> Correlation matrix not shown by default, as p = 80 > 20.
> Use print(...., correlation=TRUE) or
> vcov(....) if you need it
>
>
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