[R-sig-ME] What is the lmer/nlme equivalent of the REPEATED subcommand in SPSS's MIXED procedure?
Maarten Jung
Maarten.Jung at mailbox.tu-dresden.de
Thu Mar 22 10:05:34 CET 2018
I think the problem is that there is only one observation per
subject-occasion-combination in this example.
In this case the random slopes are confounded with the residual variation
(see [1]).
One *can* fit this model using lmer(test ~ 1 + occ2 + occ3 + (1 + occ2 +
occ3|person), data = mydata, control = lmerControl(check.nobs.vs.nRE =
"ignore")) or
lme(test ~ 1 + occ2 + occ3, mydata, random = ~ occ2 + occ3|person).
However, I don't know if the gls() fit ist more trustworthy than the
lmer/lme fit here.
I would be grateful if somebody more experienced in mixed models could
comment on this.
Best regards,
Maarten
[1]
https://stackoverflow.com/questions/26465215/random-slope-for-time-in-subject-not-working-in-lme4?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
On Wed, Mar 21, 2018 at 9:27 PM, Ben Pelzer <b.pelzer at maw.ru.nl> wrote:
> Hi Maarten,
>
> Here is an example which shows the unstructured model with gls and the
> not converging model with lmer. In this example, we have three occasions
> on which the dependent variable "test" was observed, for each of 20
> persons. In total then we have 60 observations, with the "occasion"
> variable taking values 1, 2, 3. The data also contain the person id
> variable "person" and dummy variables "occ1", "occ2", "occ3" as (0 or 1)
> indicators of the occasion. In the syntax below, a factor variable
> "factor1" is created also, to be in line with your question.
>
> I used two different specifications for the unstructured model with gls,
> depending on whether dummies or factor1 was used. For lmer, I used these
> three different specifications, none of which converges.
>
> The lmer syntax was added only to show the problem which lmer has with
> estimating an unstructured correlation pattern.
>
>
> #-----------------------------------------------------------
> ------------------------------------------------------------
> -------------------------------------
> mydata <-
> read.table(url("https://surfdrive.surf.nl/files/index.
> php/s/XfE3mtbFCTUejIz/download"),
> header=TRUE)
>
>
> #------------------- unstructured correlation matrix
> -----------------------
>
>
> # Before applying a model, let's first examine the variances and
> correlations
> # for the three occasions. We have a strong violation of the assumptions
> # of homoscedasticity and compound symmetry.
> test1 <- mydata[mydata$occasion==1,"test"]
> test2 <- mydata[mydata$occasion==2,"test"]
> test3 <- mydata[mydata$occasion==3,"test"]
> cor(cbind(test1, test2, test3))
> var(cbind(test1, test2, test3))
>
> # Unstructured model using gls from package nlme and dummies for occasion.
> # This model exactly reproduces the observed correlations between
> occasions.
> unstruc.gls1 <- gls(test ~ 1+ occ2 + occ3,
> method="REML", data=mydata,
> correlation=corSymm(form = ~ 1 |person),
> weights = varIdent(form = ~1|occasion))
> summary(unstruc.gls1)
>
>
> # Unstructured model using factor1 for occasion instead of dummies.
> # The results are exactly the same as those above, as should be.
> mydata$factor1 <- as.factor(mydata$occasion)
> unstruc.gls2 <- gls(test ~ factor1,
> method="REML", data=mydata,
> correlation=corSymm(form = ~ 1|person),
> weights = varIdent(form = ~1|factor1))
> summary(unstruc.gls2)
>
>
> # Unstructured model using lmer and dummies for occasion: does not
> converge.
> unstruc.lmer <- lmer(test ~ 1+ occ2 + occ3 + (1+occ2+occ3|person),
> data=mydata, REML=TRUE)
> summary(unstruc.lmer)
>
>
> # Unstructured model using lmer and factor1 for occasion: does not
> converge.
> unstruc.lmer <- lmer(test ~ 1+ factor1 + (1+factor1|person),
> data=mydata, REML=TRUE)
> summary(unstruc.lmer)
>
>
> # Unstructured model using lmer and factor1 for occasion, no intercept
> specified: does not converge.
> unstruc.lmer <- lmer(test ~ factor1 + (factor1|person),
> data=mydata, REML=TRUE)
> summary(unstruc.lmer)
>
>
>
> On 21/03/2018 13:07, Maarten Jung wrote:
> > Dear Ben,
> >
> > I am a bit puzzled.
> >
> > Do you mean that
> >
> > m1 <- gls(value ~ factor1, data, correlation = corSymm(form = ~
> > 1|participant), weights = varIdent(form = ~ 1|factor))
> >
> > would be equivalent to
> >
> > m2 <- lmer(value ~ factor1 + (factor1|participant), data)
> >
> > and one should use gls() because it allows for the same covariance
> > structures as /REPEATED does?
> >
>
>
> the two specifications are not equivalent in the sense that lmer also
> tries to estimate residual variance. However, with the given lmer model
> specification, the random factor1 effects capture all variance there is
> and no residual variance remains.
>
>
> > And, if so, why should m2 cause an identification problem and m1 doesn't?
> >
> > Regards,
> > Maarten
> >
> Regards, Ben.
>
>
>
> >
> > On Wed, Mar 21, 2018 at 12:03 PM, Ben Pelzer <b.pelzer at maw.ru.nl
> > <mailto:b.pelzer at maw.ru.nl>> wrote:
> >
> > Dear all,
> >
> > As far as I know, the specification for lmer using
> >
> > value ~ factor1 + (factor1 | participant)
> >
> > causes an identification problem, because the residual variance is
> not
> > excluded from the estimations. It would indeed work (e.g. in MlWin
> > this
> > can be done) if we could constrain that residual variance to zero.
> > There
> > have been some mails in this list about whether or not constraining
> > residual variance to zero is possible in lmer, but I believe this
> > is not
> > possible. Would be nice if we could do this in lmer!
> >
> > Best regards, Ben.
> >
> >
> > On 20-3-2018 18:34, Douglas Bates wrote:
> > > Kind of looks like SPSS went for bug-for-bug compatibility with
> > SAS on
> > > this one. In SAS PROC MIXED, "REPEATED" and "RANDOM" are two
> > ways of
> > > specifying the random effects variance structure but they often
> boil
> > > down to the same model.
> > >
> > > I believe the model can be specified in lme4 as
> > >
> > > value ~ factor1 + (factor1 | participant)
> > >
> > > This is what the mis-named* "UNSTRUCTURED" covariance type means
> > >
> > > * Old-guy, get off my lawn rant about terminology *
> > > As a recovering mathematician I find the name "unstructured" being
> > > used to denote a positive-definite symmetric matrix to be, well,
> > > inaccurate.
> > >
> > > On Tue, Mar 20, 2018 at 12:19 PM Mollie Brooks
> > > <mollieebrooks at gmail.com <mailto:mollieebrooks at gmail.com>
> > <mailto:mollieebrooks at gmail.com <mailto:mollieebrooks at gmail.com>>>
> > wrote:
> > >
> > > I don’t know anything about spss, but if you basically want
> lme4
> > > with more correlation structures, you could look at the
> > structures
> > > available with glmmTMB.
> > >
> > https://cran.r-project.org/web/packages/glmmTMB/
> vignettes/covstruct.html
> > <https://cran.r-project.org/web/packages/glmmTMB/
> vignettes/covstruct.html>
> > >
> > > cheers,
> > > Mollie
> > >
> > > > On 20Mar 2018, at 18:11, Ben Pelzer <b.pelzer at maw.ru.nl
> > <mailto:b.pelzer at maw.ru.nl>
> > > <mailto:b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>>>
> wrote:
> > > >
> > > > Hi Maarten,
> > > >
> > > > You are right: you need nlme and NOT lme4 to specify
> > particular
> > > > correlation structures. Also, in nlme you would need gls
> > to make it
> > > > similar to mixed in spss. The repeated command in spss
> > gives the
> > > same
> > > > results as gls does for any of the covariance structures.
> > > >
> > > > Regards, Ben.
> > > >
> > > >
> > > > On 20/03/2018 17:30, Maarten Jung wrote:
> > > >> Dear Ben, dear Phillip,
> > > >>
> > > >> comparing [1] with [2] I think the /REPEATED command
> > specifies
> > > >> the error (co)variance structure of the model. Would you
> > agree
> > > with that?
> > > >> If so, AFAIK this is not possible with lmer and thus the
> > answer on
> > > >> Stack Overflow [3] would be wrong.
> > > >>
> > > >> [1]
> > > >>
> > >
> > https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/>
> > > >> [2]
> > > >>
> > >
> > https://stats.idre.ucla.edu/spss/examples/alda/chapter7/
> applied-longitudinal-data-analysis-modeling-change-and-
> event-occurrenceby-judith-d-singer-and-john-b-willett-
> chapter-7-examining-the-multilevel-model-s-erro/
> > <https://stats.idre.ucla.edu/spss/examples/alda/chapter7/
> applied-longitudinal-data-analysis-modeling-change-and-
> event-occurrenceby-judith-d-singer-and-john-b-willett-
> chapter-7-examining-the-multilevel-model-s-erro/>
> > > >> [3]
> > > >>
> > >
> > https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>
> > > >>
> > > >> Regards,
> > > >> Maarten
> > > >>
> > > >> On Tue, Mar 20, 2018 at 2:10 PM, Ben Pelzer
> > <b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>
> > > <mailto:b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>>
> > > >> <mailto:b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>
> > <mailto:b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>>
> > > <mailto:b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>
> > <mailto:b.pelzer at maw.ru.nl <mailto:b.pelzer at maw.ru.nl>>>>> wrote:
> > > >>
> > > >> Dear Maarten,
> > > >>
> > > >> Take a look at
> > > >>
> > > >>
> > >
> > https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/>
> > >
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/>>
> > > >>
> > >
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/>
> > >
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/
> > <https://stats.idre.ucla.edu/r/examples/alda/r-applied-
> longitudinal-data-analysis-ch-7/>>>
> > > >>
> > > >> which shows you a number of covariance structures, among
> > > which is
> > > >> the unstructured matrix, for repeated measures in R
> > with lme. It
> > > >> refers to chapter 7 of Singer and Willett where they
> > discuss all
> > > >> these different structures and how to choose among them.
> > > Regards,
> > > >>
> > > >> Ben.
> > > >>
> > > >> On 20-3-2018 9:00, Maarten Jung wrote:
> > > >>
> > > >> Dear list,
> > > >> I came across a SPSS syntax like this
> > > >>
> > > >> MIXED value BY factor1
> > > >> /CRITERIA=CIN(95) MXITER(100) MXSTEP(10)
> > SCORING(1)
> > > >> SINGULAR(0.000000000001)
> > > >> HCONVERGE(0, ABSOLUTE) LCONVERGE(0, ABSOLUTE)
> > > >> PCONVERGE(0.000001,
> > > >> ABSOLUTE)
> > > >> /FIXED=factor1 | SSTYPE(3)
> > > >> /METHOD=REML
> > > >> /REPEATED=factor1 | SUBJECT(participant)
> > COVTYPE(UN).
> > > >>
> > > >> and struggle to find an equivalent lmer/nlme (or R in
> > > general)
> > > >> formulation
> > > >> for this kind of models.
> > > >> Does anybody know how to convert the REPEATED
> > subcommand
> > > into
> > > >> R code?
> > > >>
> > > >> Please note that I asked the question on Stack
> > Overflow
> > > about
> > > >> two month ago:
> > > >>
> > >
> > https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>
> > >
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>>
> > > >>
> > >
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>
> > >
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
> > <https://stackoverflow.com/questions/48518514/what-is-
> the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>>>
> > > >>
> > > >> Best regards,
> > > >> Maarten
> > > >>
> > > >> [[alternative HTML version deleted]]
> > > >>
> > > >> _______________________________________________
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> > > >>
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> > > >
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