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

[Viechtbauer, Wolfgang (SP)]

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))