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

Sun Nov 1 18:05:43 CET 2020

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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>>
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