What is the lmer/nlme equivalent of the REPEATED subcommand in SPSS's MIXED procedure?
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? And, if so, why should m2 cause an identification problem and m1 doesn't? Regards, Maarten
On Wed, Mar 21, 2018 at 12:03 PM, Ben Pelzer <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>> 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
cheers,
Mollie
> On 20Mar 2018, at 18:11, Ben Pelzer <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/
>> [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/
>> [3]
>>
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>>>> 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/>>
>>
>> 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>>
>>
>> Best regards,
>> Maarten
>>
>> [[alternative HTML version deleted]]
>>
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