[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 11:03:37 CET 2018
Hi Ben,
I'm aware of this problem for lmer.
But how does gls() overcome the problem?
Regards,
Maarten
On Thu, Mar 22, 2018, 10:44 Ben Pelzer <b.pelzer at maw.ru.nl> wrote:
> Hi Maarten,
>
> Notice that with the syntax for lmer, 6 random-effect (co)variances must
> be estimated and 1 residual variance, so in total 7
> (co)variance-parameters. However, there are only 6 observed covariances,
> meaning that the model is over-specified. Many solutions are possible
> all having the same loglikelihood. Ignoring the nobs.vs.nRE rule leads
> to just one of the many solutions. I would not be surprised if you would
> find another solution after manipulating the starting values for the
> covariances or other criteria for convergence. Best regards,
>
> Ben.
>
>
> On 22/03/2018 10:05, Maarten Jung wrote:
> > 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
> > <mailto: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
> > <
> 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>
> > > <mailto: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>>
> > > <mailto: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>
> > >
> > <
> 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>>
> > > > <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:
> > > > >
> > > > > 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/
> >
> > >
> > <
> 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/
> >
> > >
> > <
> 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
> >
> > >
> > <
> 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>>
> > > <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 <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/
> >>>
> > > > >>
> > > >
> > >
> > <
> 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
> >>>
> > > > >>
> > > >
> > >
> > <
> 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]]
> > > > >>
> > > > >> _______________________________________________
> > > > >> R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>>
> > > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>>>
> > > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>>
> > > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>>>>
> > > > >> <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>>
> > > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>>>
> > > > <mailto:R-sig-mixed-models at r-project.org
> > <mailto:R-sig-mixed-models at r-project.org>
> > > <mailto:R-sig-mixed-models at r-project.org
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