[R-sig-ME] Fwd: lmer stand dev of coefficients
A.Robinson at ms.unimelb.edu.au
Sun Dec 21 21:12:41 CET 2008
This article might help:
The BLUPs are not "best" when it comes to bootstrapping
Jeffrey S. Morris
Statistics & Probability Letters 56 (2002) 425-430
In the setting of mixed models, some researchers may construct a
semiparametric bootstrap by sampling from the best linear unbiased
predictor residuals. This paper demonstrates both mathematically and
by simulation that such a bootstrap will consistently underestimate
the variation in the data in finite samples.
On Sun, Dec 21, 2008 at 10:59:01AM -0600, Douglas Bates wrote:
> On Sun, Dec 21, 2008 at 9:40 AM, Daniel Ezra Johnson
> <danielezrajohnson at gmail.com> wrote:
> > ---------- Forwarded message ----------
> > From: Daniel Ezra Johnson <danielezrajohnson at gmail.com>
> > Date: Sun, Dec 21, 2008 at 3:39 PM
> > Subject: Re: [R-sig-ME] lmer stand dev of coefficients
> > To: Douglas Bates <bates at stat.wisc.edu>
> > Can you explain briefly what circumstances would lead these quantities
> > to be quite different?
> First, I misspoke. (Note to self: Don't try to answer questions on
> theory before the second cup of coffee.) The standard deviation of
> the BLUPs (or, as I prefer to call them, the conditional modes) of the
> random effects are not an estimate of the conditional standard
> deviation of the random effects given the data. I can only make sense
> of the conditional standard deviation of a particular random effect
> and that would be much smaller than the observed standard deviation of
> the conditional modes.
> What I should have said is somewhat more subtle. We know that the
> conditional modes of the random effects have less variability than the
> corresponding individual estimates of a parameter. I enclose a script
> and its output for a particularly simple example - a random-effects
> model fit to the Dyestuff data from the lme4 package. The design is a
> balanced, one-way classification so the estimate of the mean yield is
> simply the mean of the Yield variable.
> We see that the conditional modes are always smaller in magnitude than
> the deviations of the individual means from the overall mean. The
> fact that the ratio is constant is a consequence of the balanced
> design. We say that the conditional modes are shrunk towards zero
> because the random effects have a finite variance.
> The conditional modes are also shrunk relative to what would be
> expected from the unconditional variance of the random effects, but I
> find it more difficult to explain why. It makes sense to me that the
> mle of the unconditional standard deviation would be larger than the
> standard deviation of the conditional modes but of the way the way the
> likelihood criterion is formulated.
> Perhaps someone else can explain why.
> > Suppose the random effect grouping factor is Subject.
> > On what basis would the software estimate the unconditional SD of (the
> > population of) Subjects to be something quite different (and as you
> > say, usually larger) than that of the particular group of Subjects in
> > the data?
> > Dan
> > On Sun, Dec 21, 2008 at 3:32 PM, Douglas Bates <bates at stat.wisc.edu> wrote:
> >> On Sun, Dec 21, 2008 at 3:55 AM, Iasonas Lamprianou
> >> <lamprianou at yahoo.com> wrote:
> >>> Dear friends
> >>> when I use sd(coef(mymodel)$myvariable) I get 0.21
> >>> However, the summary of the model gives
> >>> Error terms:
> >>> Groups Name Std.Dev.
> >>> myvariable (Intercept) 0.33
> >>> Residual 0.76
> >>> Why dont I get the same value (0.21 instead of 0.33)?
> >> Because they are estimates of different quantities:
> >> sd(coef(mymodel)$myvariable) is an estimate (although it is not
> >> entirely clear what the properties of such an estimate would be) of
> >> the conditional standard deviation of the random effects given the
> >> data, whereas 0.33 is the maximum likelihood estimate or REML estimate
> >> of the unconditional standard deviation of the random effects. We
> >> would expect the conditional standard deviation to be smaller than the
> >> unconditional standard deviation.
> >> P.S. If you are starting a new topic on the mailing list you don't
> >> need to quote a previous message to the list and especially not an
> >> entire digest message.
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