[R-sig-ME] Fwd: MIXED MODEL WITH REPEATED MEASURES

[Joerg Luedicke]

---------- Forwarded message ----------
From: Joerg Luedicke <joerg.luedicke at gmail.com>
Date: Sat, Dec 10, 2011 at 10:35 AM
Subject: Re: [R-sig-ME] MIXED MODEL WITH REPEATED MEASURES
To: Erin Ryan <erin at the-ryans.com>


On Fri, Dec 9, 2011 at 9:33 PM, Erin Ryan <erin at the-ryans.com> wrote:
> Good suggestions; however, there is inherent value in the temporal
> progression of the repeated measures, so I need to capture that in some way.

If your dependent variable is a constant within units for which you
observe "temporal progression", then this "progression" does not
matter whatsoever. Imagine you would fit a conventional regression and
your dependent variable would be a constant. It would not matter at
all how different the subjects would be in whatever regard.

> For similar reasons, averaging the values of the independent variables is
> problematic, as they progress over time to a final, actual value, which
> presumably should be weighted more heavily. In other words, truth is known
> on the final repeated measure, but I wish to make accurate predictions much
> earlier than the final repeated measure.

I don't know what your field of research is, but if you believe that
later measures are better measures of your object of interest, you
could just take the last one instead of the average. Or, you could
take a weighted average of some sort.


HTH,

Joerg

>
> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org
> [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Ben Bolker
> Sent: Thursday, December 08, 2011 5:01 PM
> To: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] MIXED MODEL WITH REPEATED MEASURES
>
> Erin Ryan <erin at ...> writes:
>
>>
>> I am trying to specify a mixed model for my research, but I can't
>> quite get it to work. I've spent several weeks looking thru various
>> online sources to no avail. I can't find an example of someone trying
>> to do precisely what I'm trying to do. I'm hoping some smart member of
>> this mailing list may be able to help.
>>
>> First off, full disclosure: (1) I'm an engineer by trade, so the
>> problem may be related to my ignorance of statistics, and/or (2) I'm
>> fairly new to R, so the problem may be related to my ignorance of R
>> syntax. Here is the basic structure of my data (in longitudinal form):
>
>  [snip]
>
>> The rows below each subject are repeated measures (in years), with the
>> specific pattern of repeated measurements unique to each subject. The
>> data contains fixed effects and random effects, and there is clearly
>> correlation in the random effects within each subject. The DepVar
>> column represents the dependent variable which is a constant for each
>> subject. All the data is empirical, but I wish to create a predictive
>> model. Specifically, I wish to predict the value for DepVar for new
> subjects.
>>
>> So I understand enough about statistics to know that I must employ a
>> mixed model. I further understand that I must specify a covariance
>> matrix structure. Given the relatively high degree of correlation in
>> consecutive years, an AR(1) structure seems like a good starting
>> point. I have been trying to build the model in SPSS, but without
>> success, so I've recently turned to R. My first attempt was as
>> follows--
>>
>> ModelFit <- lme(fixed = DepVar ~FixedVar1+FixedVar2, random =
>> ~RandomVar1+RandomVar2 | Subject, na.action = na.omit, data = dataset,
>> corr = corAR1())
>>
>> I assume this can't be the right specification since it neglects the
>> repeated measure aspect of the data, so I instead decided to employ
>> the
>> corCAR1 structure, i.e.--
>>
>> ModelFit <- lme(fixed = DepVar ~FixedVar1+FixedVar2, random =
>> ~RandomVar1+RandomVar2 | Subject, na.action = na.omit, data = dataset,
>> corr = corCAR1(0.5, form = ~ Years | Subject))
>>
>> Now perhaps neither correlation structure is the right one (probably a
>> different discussion for another day), but the problem I'm
>> experiencing seems to occur regardless of the structure I specify. In
>> both cases, I get the following error--
>>
>> Error in solve.default(estimates[dimE[1] - (p:1), dimE[2] - (p:1),
>> drop =
>> FALSE]) :
>>
>>   system is computationally singular: reciprocal condition number =
>> 5.42597e-022
>>
>> Anybody know what is going wrong here? This error appears to be
>> related to the fact that the DepVar is constant for each subject,
>> because when I select a different dependent variable that is different
>> for each repeated measure w/in the subject, I do not get this error.
>>
>
>  I think you're right that DepVar is fixed per individual.
> Technical details aside, I'm having trouble seeing how you're going to
> estimate the effects of predictor variables that vary within subject when
> you've only got one response per subject.
> Furthermore, I think what you're terming "RandomVar1" and "RandomVar2"
> are probably *not* random variables, but rather are variables
> that vary within subject.   For this response variable, I would
> suggest averaging the values of RandomVar1 and RandomVar2 per subject and
> collapsing the data set to a simple linear model on subjects -- and get rid
> of the correlation model at the same time.  For response variables that do
> vary within subject, I would suggest
>
> ModelFit <- lme(fixed = DepVar ~FixedVar1+FixedVar2+
>   RandomVar1 + RandomVar2, random = 1 | Subject,
>  na.action = na.omit, data = dataset, corr = corAR())
>
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