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

[ONKELINX, Thierry]

Dear Erin,

Your model seems reasonable. As soon as you add the random effect, then the repeated measures are taken into account. However, I would specify the correlation structure more explicit like  corAR1(form = ~Years) which is equal to corAR1(form = ~Years|Subject) in your case.

Furthermore I would suggest that you center the continuous variables so that zero is at least near to your data. You'll get intercepts that are much more easy to interpret and it might help the computation.

Best regards,

Thierry


ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
Thierry.Onkelinx at inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


-----Oorspronkelijk bericht-----
Van: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens Erin Ryan
Verzonden: donderdag 8 december 2011 4:17
Aan: R-SIG-Mixed-Models at r-project.org
Onderwerp: [R-sig-ME] MIXED MODEL WITH REPEATED MEASURES

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):

 

            FixedVar1    FixedVar2    RandomVar1    RandomVar2   ...
DepVar

Subject1    

  1996         AF           A           0.002          800               2.1

  1997         AF           A           0.002          760               2.1

  1998         AF           A           0.003          760               2.1

  1999         AF           A           0.005          760               2.1


  2001         AF           A              NA          900               2.1

  2002         AF           A           0.004          880               2.1

  2003         AF           A           0.005          870               2.1

  2004         AF           A           0.006          870               2.1

  2005         AF           A           0.006          900               2.1

 

Subject2

  2001         NA           S           0.000          350              18.0

  2002         NA           S           0.000          350              18.0

  2003         NA           S           0.136          380              18.0

  2005         NA           S           0.146          390              18.0

  2006         NA           S           0.146          510              18.0

  2007         NA           S           0.161          510              18.0

  2009         NA           S           0.161           NA              18.0

  2010         NA           S           0.161          350              18.0

 

...

 

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.

 

Sorry for the long post. Hope this makes sense.

 

Erin

 


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