From: Douglas Bates <bates_at_stat.wisc.edu>

Date: Sat 29 Jul 2006 - 02:48:30 EST

R-help@stat.math.ethz.ch 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. Received on Sat Jul 29 04:05:00 2006

Date: Sat 29 Jul 2006 - 02:48:30 EST

On 7/26/06, Bill Shipley <bill.shipley@usherbrooke.ca> wrote:

> Hello. Douglas Bates has explained in a previous posting to R why he does

*> not output residual degrees of freedom, F values and probabilities in the
**> mixed model (lmer) function: because the usual degrees of freedom (obs -
**> fixed df -1) are not exact and are really only upper bounds. I am
**> interpreting what he said but I am not a professional statistician, so I
**> might be getting this wrong...
**> Does anyone know of any more recent results, perhaps from simulations, that
**> quantify the degree of bias that using such upper bounds for the demoninator
**> degrees of freedom produces? Is it possible to calculate a lower bounds for
**> such degrees of freedom?
*

I can give another perspective on the issue of degrees of freedom for a linear mixed model although it probably doesn't address the question that you want to address.

The linear predictor in a mixed model has the form X\beta + Zb where \beta is the fixed-effects vector and b is the random-effects vector. The fitted values, y-hat, are the fitted values from a penalized least squares fit of the response vector, y, to this linear predictor subject to a penalty on b defined by the variance components. When the penalty is large, the fitted values approach those from the ordinary least squares fit of y on X\beta only. When the penalty is small, the fitted values approach those from an unpenalized least squares fit of y on the linear predictor. (In this case estimates of the coefficients are not well defined because the combined matrix [X:Z] is generally rank deficient but the fitted values are well defined.)

If the rank of X is p and the rank of [X:Z] is r then the effective number of parameters in the linear predictor for the penalized least squares fit is somewhere between p and r. One way of defining the effective number of parameters is as the trace of the hat matrix for the penalized least squares problem. This number will change as the variance components change and is usually evaluated at the estimates of the variance components. This is exactly what Spiegelhalter, Best, Carlin and van der Linde (JRSSB, 64(4), 583-639, 2002) define to be their p_D for this model. The next release of the lme4/Matrix packages will include an extractor function to evaluate the trace of the hat matrix for a fitted lmer model, using an algorithm due to Jialiang Li.

This effective number of parameters in the linear predictor is like the degrees of freedom for regression. In the limiting cases it is exactly the degrees of freedom for regression. One might then argue that the degrees of freedom for residuals would be n - hat trace and use this for the denominator degrees of freedom in the F ratios. However, this number does not vary with the numerator term and many people will claim that it must. (I admit to being a bit perplexed as to why the denominator degrees of freedom should change according to the numerator term when the denominator of the F ratio itself doesn't change, but many people insist that this is the way things must be.)

So it is possible to calculate a number that can reasonably be
considered to be the

degrees of freedom for the denominator that is actually used in the F
ratios but this will not correspond to what many people will insist is
the "obviously correct" number of degrees of freedom.

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