[R-sig-ME] lme: count the number extra parameters estimated for variance or covariances

Sun Nov 1 18:02:56 CET 2020

Simon,

Here is a quick way to accomplish the same thing that Wolfgang
demonstrated, using the lmeInfo package:
VC <- lmeInfo::extract_varcomp(fit)   # get all the variance components
lengths(VC)                                        # count the number of
estimated parameters in each component
sum(lengths(VC))                               # total number of variance
component parameters

Kind Regards,
James

On Sun, Nov 1, 2020 at 10:45 AM Simon Harmel <sim.harmel using gmail.com> wrote:

> Thank you, Wolfgang (sorry for misspelling). So, by: " The coefficient for
> the females is of course [one additional parameter]" you mean for the
> variance coefficient of `female == 1` as a binary predictor, right?
>
> On Sun, Nov 1, 2020 at 10:31 AM Viechtbauer, Wolfgang (SP) <
> wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
>
> > I meant that the 1 for males is not an estimated parameter. The
> > coefficient for the females is of course (and hence one additional
> > parameter). Apologies for the confusion.
> >
> > For correlation structures, there will indeed be a 'corStruct' element
> > under 'modelStruct'.
> >
> > Best,
> > Wolfgang
> >
> > >-----Original Message-----
> > >From: Simon Harmel [mailto:sim.harmel using gmail.com]
> > >Sent: Sunday, 01 November, 2020 17:15
> > >To: Viechtbauer, Wolfgang (SP)
> > >Cc: Harold Doran; r-sig-mixed-models
> > >Subject: Re: [R-sig-ME] lme: count the number extra parameters estimated
> > for
> > >variance or covariances
> > >
> > >Thank you Wolfang. That was exactly what I was looking for. If an lme()
> > >model uses  `correlation = corAR1()`, then I'm assuming something new
> > >will appear for the question mark in the following:
> `m2$modelStruct$???`,
> > >right?
> > >
> > >Wolfang, on the one the hand you mentioned: "you will also get the
> > >coefficient (= 1) for the males. But that is not actually an estimated
> > >parameter",
> > >
> > >On the other hand you mentioned: "And these *are* parameters (besides
> the
> > >fixed effects and the vars/covs of the random effects)."
> > >
> > >Multiple software I used show that my model with `varIdent(form = ~1
> > >|female)` estimates one additional parameter compared to a corresponding
> > >model without `varIdent(form = ~1 |female)`.
> > >
> > >Books (e.g., Mixed Effects Models and Extensions in Ecology with R by
> Zuur
> > >et al, 2009; Pinheiro & Bates, 2000, p. 209) also clearly mention
> > >`varIdent(form = ~1 |female)` estimates one more parameter.
> > >
> > >Would you please clarify?
> > >
> > >On Sun, Nov 1, 2020 at 9:44 AM Viechtbauer, Wolfgang (SP)
> > ><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> > >Dear Simon,
> > >
> > >For variance structures, you can use:
> > >
> > >coef(fit$modelStruct$varStruct)
> > >
> > >That will give you the parameter estimates involved in the variance
> > >structure (in their constrained form as used during the optimization).
> > With:
> > >
> > >coef(fit$modelStruct$varStruct, unconstrained=FALSE)
> > >
> > >you can get the unconstrained estimates. Only coefficients that are
> > actually
> > >estimated are returned by default. With:
> > >
> > >coef(fit$modelStruct$varStruct, unconstrained=FALSE, allCoef=TRUE)
> > >
> > >you will also get the coefficient (= 1) for the males. But that is not
> > >actually an estimated parameter. For more details, see:
> > >
> > >help(coef.varFunc)
> > >
> > >And these *are* parameters (besides the fixed effects and the vars/covs
> of
> > >the random effects).
> > >
> > >Best,
> > >Wolfgang
> > >
> > >>-----Original Message-----
> > >>From: R-sig-mixed-models [mailto:
> > r-sig-mixed-models-bounces using r-project.org]
> > >>On Behalf Of Harold Doran
> > >>Sent: Sunday, 01 November, 2020 16:26
> > >>To: Simon Harmel
> > >>Cc: r-sig-mixed-models
> > >>Subject: Re: [R-sig-ME] lme: count the number extra parameters
> estimated
> > >for
> > >>variance or covariances
> > >>
> > >>I think you need to understand what a reproducible example is intended
> to
> > >>do. Your data estimates a model and yields a fiitted model object. What
> > >>parameter from that object using an extractor are you intending to
> find?
> > >>
> > >>For example, a well posed question would be something like. I want to
> > >>extract the fixed effects from a fitted model object. How do I get
> them?
> > >>
> > >>To say I want the “parameters estimated for modeling residual
> variances”
> > >etc
> > >>makes no sense. The parameters of a mixed model are the fixed effects
> and
> > >>the marginal variances (and covariances) of the random effects.
> > >>
> > >>So, specifically what parameters do you think exist in a model that you
> > >>want?
> > >>
> > >>From: Simon Harmel <sim.harmel using gmail.com>
> > >>Sent: Sunday, November 1, 2020 9:58 AM
> > >>To: Harold Doran <harold.doran using cambiumassessment.com>
> > >>Cc: r-sig-mixed-models <r-sig-mixed-models using r-project.org>
> > >>Subject: Re: [R-sig-ME] lme: count the number extra parameters
> estimated
> > >for
> > >>variance or covariances
> > >>
> > >>Dear Harold,
> > >>
> > >>My question "specifically" is:  is there a way (e.g., via an extractor
> > >>function) to obtain parameters estimated for modeling residual
> variances
> > or
> > >>covariances from an "lme" model?
> > >>
> > >>For concreteness, please consider the reproducible model I provided in
> my
> > >>original post in which a variance function has been used.
> > >>
> > >>Thanks,
> > >>
> > >>On Sun, Nov 1, 2020, 4:43 AM Harold Doran
> > >><harold.doran using cambiumassessment.com<mailto:
> > harold.doran using cambiumassessment.c
> > >o
> > >>m>> wrote:
> > >>In order to answer that you need to specify what "thing" you want. The
> > >>object itself has many things and there are extractor functions to grab
> > >many
> > >>of them. I say "thing" because the *parameters* of a mixed model are
> the
> > >>fixed effects and the variance components. Random effects etc are not
> > >>parameters of a mixed model.
> > >>
> > >>You can always look at the structure of a fitted model object in R to
> see
> > >>what things are generally in it.
> > >>
> > >>-----Original Message-----
> > >>From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-
> > >project.org<mailto:r-
> > >>sig-mixed-models-bounces using r-project.org>> On Behalf Of Simon Harmel
> > >>Sent: Sunday, November 1, 2020 4:02 AM
> > >>To: r-sig-mixed-models <r-sig-mixed-models using r-project.org<mailto:r-sig-
> > >mixed-
> > >>models using r-project.org>>
> > >>Subject: [R-sig-ME] lme: count the number extra parameters estimated
> for
> > >>variance or covariances
> > >>
> > >>External email alert: Be wary of links & attachments.
> > >>
> > >>Hello All,
> > >>
> > >>In addition to fixed and random effects, is there a way to extract how
> > many
> > >>other parameters (for modeling residual variances or covariances) an
> > >"lme()"
> > >>object has estimated?
> > >>
> > >>Here is a reproducible example:
> > >>
> > >>library(nlme)
> > >>
> > >>hsb <- read.csv('
> > >>https://raw.githubusercontent.com/rnorouzian/e/master/hsb.csv')
> > >>hsb$female <- as.factor(hsb$female)
> > >>
> > >>fit <- lme(math ~ female, random = ~ 1|sch.id<http://sch.id>, data =
> > hsb,
> > >>weights = varIdent(form = ~1 |female))
> >
>
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