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

[Simon Harmel]

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.co
> >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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