What is the lmer/nlme equivalent of the REPEATED subcommand in SPSS's MIXED procedure?
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.
>
vignettes/covstruct.html
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]
> >>
>
longitudinal-data-analysis-ch-7/
longitudinal-data-analysis-ch-7/>
> >> [2]
> >>
>
applied-longitudinal-data-analysis-modeling-change-and- event-occurrenceby-judith-d-singer-and-john-b-willett- chapter-7-examining-the-multilevel-model-s-erro/
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]
> >>
>
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
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
> >>
> >>
>
longitudinal-data-analysis-ch-7/
longitudinal-data-analysis-ch-7/>
>
longitudinal-data-analysis-ch-7/
longitudinal-data-analysis-ch-7/>>
> >>
>
longitudinal-data-analysis-ch-7/
longitudinal-data-analysis-ch-7/>
>
longitudinal-data-analysis-ch-7/
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:
> >>
>
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>
>
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>>
> >>
>
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc>
>
the-lmer-nlme-equivalent-of-the-repeated-subcommand-in-spsss-mixed-proc
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
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