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

[Simon Harmel]

Great, many thanks to all!

On Sun, Nov 1, 2020 at 11:14 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Ah, very cool. Did not know about the lmeInfo package.
>
> And yes, one can think of the parameter/coefficient for females as a
> binary predictor that allows the error variance to differ for females from
> that of the males.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Simon Harmel [mailto:sim.harmel using gmail.com]
> >Sent: Sunday, 01 November, 2020 18:06
> >To: James Pustejovsky
> >Cc: Viechtbauer, Wolfgang (SP); Harold Doran; r-sig-mixed-models
> >Subject: Re: [R-sig-ME] lme: count the number extra parameters estimated
> for
> >variance or covariances
> >
> >Thanks, James!
> >
> >On Sun, Nov 1, 2020 at 11:03 AM James Pustejovsky <jepusto using gmail.com>
> wrote:
> >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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