[R] lme4 package: Fitted values and residuals

[Renaud Lancelot]

2006/11/16, Douglas Bates <bates at stat.wisc.edu>:
> On 11/16/06, Frank Johannes <fjohannes at fastmail.fm> wrote:
> > Dear all,
> > I have three concerns:
> > 1)
> > I am running models with the lme4 package. I cannot find a way to pull
> > out a vector of the fitted values and the residuals. Does anybody know
> > how to do it?
>
> The fitted() and resid() extractor functions are the usual way of
> doing this for a fitted model in R and, astonishingly enough, they
> work!  Try
>
> library(lme4)
> example(lmer)
> fitted(fm1)
> resid(fm1)

But resid does not seem to work with GLMMs:

> library(lme4)   ##  version 0.9975-9
Le chargement a nécessité le package : Matrix
Le chargement a nécessité le package : lattice
> m1 <- lmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
+             family = binomial, data = cbpp)
relative tolerance set to 9.8721686386158e-05
> resid(m1)
Erreur : 'resid' n'est pas encore implémenté

[in English: Error: 'resid' is not implemented yet]

Best,

Renaud

>
> > 2)
> > How can I nest a random effect variable into a "two-level" fixed effect
> > variable?
>
> I'm not quite sure what you mean.  Are you talking about a situation
> like having a random effect for "Patient" and a fixed effect for
> "Treatment" with Patient nested within Treatment.  If the Patients are
> designated in a sensible way, so that each Patient has a distinct
> identifier (i.e. the number of levels of the Patient factor
> corresponds to the number of Patients) then you can ignore the nesting
> and specify the model as
>
> lmer(response ~ Treatment + (1|Patient), ...)
>
> Sometimes the Patients are designated with implicit nesting so that
> the treatment group has a patient labeled "1" and the control group
> has another patient also labeled as "1".  In that case you need to
> specify the grouping factor for the random effects as the
> Treatment:Patient interaction.  That is,
>
> lmer(response ~ Treatment + (1|Treatment:Patient), ...)
>
> The second version also works properly if the Patients had been
> designated in a sensible way so, if you want to be safe, use the
> second version.
>
> > 3)
> > Suppose I have the following model:
> > y = a + b|c + d + error,
> > where 'a' is a fixed effect, 'c' is a random effect nested with the
> > random effect 'b', and 'd' is  a non-nested random effect.
>
> That's not the greatest notation to use for the R-Help list because it
> doesn't correspond to the way that R formulas are parsed.
>
> > Suppose I obtain parameter estimates for all of the predictor variables.
> >  Now, I would like to calculate a fitted value, 'yhat', from only the
> > parameter estimates for 'a' and 'b|c'. Can this be done in lme4. That
> > is; can I pull out all of the requisite elements from the output to
> > calculate 'yhat'?
>
> The answer to the question of "Can this be done in lme4?" is
>
> install.packages("fortunes"); library(fortunes); fortune("Yoda")
>
> I could describe how to do it if I had a better idea of what the
> calculation you had in mind was.  I am having difficulty grasping how
> you obtain a prediction from a model involving a factor 'd' without
> assigning a value to that factor.  I suppose that someone who
> understands the deep magic of quantities like SAS's LS-means may be
> able to tell you what you want to know but I can't.
>
> I can say that you can obtain the coefficient estimates for the fixed
> effects with
>
> fixef(fm1)
>
> and a list of the conditional modes of the random effects (also called
> the BLUPs) as
>
> ranef(fm1)
>
> After that you are on your own.
>
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>


-- 
Renaud LANCELOT
Département Elevage et Médecine Vétérinaire (EMVT) du CIRAD
Directeur adjoint chargé des affaires scientifiques

CIRAD, Animal Production and Veterinary Medicine Department
Deputy director for scientific affairs

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