The distribution at the subject level is nowhere near normal.
The fixed effects account for a rather severe departure from normality.
For this reason, the between subjects components of variance
estimate is radically different between the two models. Also the
subjects random effects are radically different. Try
znull <- ranef(null, drop=TRUE)
zfix <- ranef(fixed, drop=TRUE)
plot(zfix$subject ~ znull$subject)
The residuals for the two models are simiiar, but not at all the same.
The residuals from the null model are surprisingly close to normal.
[Actually, a criticism of the Pinheiro and Bates book is that it relies
far too heavily on plots of residuals for its diagnostic checking.]
Think about how the results might be used. The analyses that you
give seem to imply that the existence of the two groups is hidden
from you. You want to generalize to other subjects (the usual
situation), so that fitting subject as a fixed effect would defeat the
whole aim of the enterprise. So, unless you learn of the existence
of the two groups from initial exploratory analysis of the data (you
darn well should), you have to do the null analysis.
At this late stage you might examine the random subject effects
and belatedly conclude that you have been laboring under a gross
misapprehension. Or you might charge blindly ahead and use the
model for predictive purposes. In the latter case, the accuracy will
be terrible (high SE), but, hey, you can make predictions on what to
expect from another subject or group of subjects.
There are strong reasons for trying to ensure that models (1) are
somewhat near correct, and (2) that they model aspects of the data
that reflect the intended use of the results.
Now try
test2 <- data.frame(subject=rep(1:200,each=500),
response=500+rep(rnorm(200), each=500)+rnorm(100000,0,10),
fixed=(rep(c("A","B"),each=50000)))
lmer(response~ (1|subject), data=test2)
lmer(response~ fixed+(1|subject), data=test2)
Wrestling with such simulated data is a good way to gain
understanding and tune one's intuitive processes.
By the way, I believe you should be fitting by maximum likelihood
(ML), for comparing models with different fixed effects.
John Maindonald email: john.maindonald at anu.edu.au
phone : +61 2 (6125)3473 fax : +61 2(6125)5549
Centre for Mathematics & Its Applications, Room 1194,
John Dedman Mathematical Sciences Building (Building 27)
Australian National University, Canberra ACT 0200.
On 20 Aug 2008, at 6:47 AM, Daniel Ezra Johnson wrote:
Dear All,
I'm sure this is a simple question, but I haven't been able to find an
answer to it that I can understand. I'd like an answer that is pitched
somewhere between the full mathematics on the one hand, and an
oversimplified overview on the other.
One way of putting the question is, what is the difference, really,
between a fixed and a random effect (as they are fit by lmer)?
Another way of putting it involves the following example.
Suppose we have observations of a response from many subjects.
The overall average response is 500.
The subjects fall into two groups.
Half have an effect of +100 and half an effect of -100.
test1 <- data.frame(subject=rep(1:200,each=500),
response=500+c(rep(-100,50000),rep(100,50000))+rnorm(100000,0,10),
fixed=(rep(c("A","B"),each=50000)))
The following model treats subject as a random effect:
null <- lmer(response~(1|subject),test1)
The following model keeps the subject effect and adds the fixed effect.
fixed <- lmer(response~fixed+(1|subject),test1)
Linear mixed model fit by REML
Formula: response ~ (1 | subject)
Data: test1
AIC BIC logLik deviance REMLdev
746923 746951 -373458 746923 746917
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 10041.81 100.209
Residual 100.46 10.023
Number of obs: 100000, groups: subject, 200
Fixed effects:
Estimate Std. Error t value
(Intercept) 500.000 7.086 70.56
Linear mixed model fit by REML
Formula: response ~ fixed + (1 | subject)
Data: test1
AIC BIC logLik deviance REMLdev
743977 744015 -371984 743960 743969
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.016654 0.12905
Residual 99.642120 9.98209
Number of obs: 100000, groups: subject, 200
Fixed effects:
Estimate Std. Error t value
(Intercept) 400.11806 0.04647 8610
fixedB 199.87485 0.06572 3041
The result is what one would expect, intuitively.
In the model "null" there is a large subject variance.
In the model "fixed" there is virtually no subject variance.
In both models the residuals are the same.
The logLik of the model with the fixed effect is closer to zero (by about
1500).
Therefore, we say the model with the fixed effect fits better.
This makes sense. Instead of 100 subject effects near +100 and 100
near -100, we have virtually no subject effects and the fixed effect
accounts for all the between-subject variance.
The question: why? Why does the model with the fixed effect fit better?
Why does the smaller (zero) random effect plus the fixed effect
translate into an improvement in log-likelihood?
It's not anything to do with the residuals. The models make the same
predictions:
fitted(null)[c(1:5,50001:50005)]
[1] 400.2807 400.2807 400.2807 400.2807 400.2807 600.2013 600.2013
600.2013
[9] 600.2013 600.2013
fitted(fixed)[c(1:5,50001:50005)]
[1] 400.1282 400.1282 400.1282 400.1282 400.1282 599.9839 599.9839
599.9839
[9] 599.9839 599.9839
And I don't think it has anything to do with the extreme non-normality
of the random effects in "null" as opposed to "fixed".
So what's the difference?
What, in terms of model fitting, makes it preferable to account for
the between-subject variation with a fixed effect (as in "fixed")
rather than with a random effect (as in "null")?
Thanks for your help,
Daniel