[R] lme: null deviance, deviance due to the random effects, residual deviance
Spencer Graves
spencer.graves at pdf.com
Tue May 2 02:42:19 CEST 2006
As far as I know, the term "deviance" has no standard definition. A
good, fairly common definition (I think) is that the deviance is "up to
[an additive] constant, minus twice the maximised log-likelihood. Where
sensible, the constant is chosen so that a saturated model has deviance
zero." ("http://finzi.psych.upenn.edu/R/library/gnm/html/gnm.html".)
Because of this "constant", the "the proportion of deviance 'explained'
by the model" in not a well defined concept. I found this definition
using RSiteSearch("deviance define"). However, even this definition is
not used consistently; it's not even used for 'deviance.lm', which I
discovered using methods("deviance") and methods("logLik") followed by
'getAnywhere("deviance.lm"), etc.
This is not a "trivial and stupid question". Instead, it's connected
to subtle issues in statistical methods, and this reply may contribute
more obfuscation than enlightenment. If you describe some more specific
application where you might want to use something like this and what you
are trying to achieve, you might get a more useful reply.
hope this helps,
spencer graves
Patrick Giraudoux wrote:
> A maybe trivial and stupid question:
>
> In the case of a lm or glm fit, it is quite informative (to me) to have
> a look to the null deviance and the residual deviance of a model. This
> is generally provided in the print method or the summary, eg:
>
> Null Deviance: 658.8
> Residual Deviance: 507.3
>
> and (a bit simpled minded) I like to think that the proportion of
> deviance 'explained' by the model is (658.8-507.3)/658.8 = 23%
>
> In the case of lme models, is it possible and reasonable to try and get the:
> - null deviance
> - the total deviance due to the the random effect(s)
> - the residual deviance?
>
> With the idea that Null deviance = Fixed effects + Random Effects +
> Residuals
>
> If yes how to do it ? A lme object provides the following:
>
> > names(glm6)
> [1] "modelStruct" "dims" "contrasts" "coefficients"
> [5] "varFix" "sigma" "apVar" "logLik"
> [9] "numIter" "groups" "call" "method"
> [13] "fitted" "residuals" "fixDF" "family"
>
> so no $null.deviance and $deviance elements as in glm objects...
>
> I tried to find out an answer on R-help & Pineihro & Bates (2000).
> Partial success only:
>
> - null deviance: Response: possibly yes: see
> http://tolstoy.newcastle.edu.au/R/help/05/12/17796.html (Spencer
> Graves). The (null?) deviance is -2*logLik(mylme), but a personnal trial
> with some glm objects did not led to the same numbers that the one given
> by the print.glm method...
>
> - the deviance due to the the random effect(s). I was supposing that the
> coefficients given by ranef(mylme) may be an entry... but beyond this, I
> guess those coefficients must be weighed in some way... which is a far
> beyond my capacities in this matter...
>
> - residual deviance. I was supposing that it may be
> sum(residuals(mylme)^2). With some doubts as far as I feel that I am
> thinking sum of squares estimation in the context of likelihood and
> deviance estimations... So most likely irrelevant. Moreover, in the
> case I was exploring, this quantity is much larger than the null
> deviance computed as above...
>
> Any hint appreciated,
>
> Patrick Giraudoux
>
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