[R-sig-ME] Proper analysis for the Machines dataset in lme4
Reinhold Kliegl
reinhold.kliegl at gmail.com
Mon Apr 28 23:39:46 CEST 2008
Dear Michael,
A few comments on your example from the Baayen book:
> # pr2 is analogous to m2 and mr2
> pr2 <- lmer(rt ~ soa + (1 | subj / soa) + (1 | item), sp)
It does not look like subjects are nested with soa. So why would you
want to specify this model?
> # pr3 is analogous to m3 and mr3 This is how Baayen analyzes it
> # (the results aren't identical to his; I don't know why):
> pr3 <- lmer(rt ~ soa + (1 + soa | subj) + (1 | item), sp)
This is an appropriate model for this experiment. It tests the fixed
effects of soa. It allows for mean differences between subjects and
for mean differences between items (i.e., the variances of the two
intercepts) as well as for variance between subjects in the soa
effects.
Here is the current lmer (lme4_0.999375-13) fit:
Linear mixed model fit by REML
Random effects:
Groups Name Variance Std.Dev. Corr
subj (Intercept) 855.94 29.256
soaShort 491.81 22.177 -0.806
item (Intercept) 449.39 21.199
Residual 100.21 10.011
Number of obs: 64, groups: subj, 8; item, 8
Fixed effects:
Estimate Std. Error t value
(Intercept) 540.91 14.92 36.26
soaShort 22.41 17.10 1.31
And here are the results as reported in Baayen (2008):
Random effects:
Groups Name Variance Std.Dev. Corr
subj (Intercept) 861.99 29.360
soaShort 502.65 22.420 -0.813
item (Intercept) 448.29 21.173
Residual 100.31 10.016
Number of obs: 64, groups: subj, 8; item, 8
Fixed effects:
Estimate Std. Error t value
(Intercept) 540.91 14.93 36.23
soaShort 22.41 17.13 1.31
So you are correct: There are minor differences in the variance
estimates. The simple reason is that Baayen worked with a much earlier
version of lmer 0.9975-7. I am much more impressed by the stability of
the estimates than the differences, given the many changes lmer
underwent internally in the mean time.
Best
Reinhold
More information about the R-sig-mixed-models
mailing list