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

[Simon Harmel]

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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