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

[Viechtbauer, Wolfgang (SP)]

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))