On Dec 7, 2011 4:44 PM, "Erin Ryan" <e...@the-ryans.com> wrote:
>
> 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. I have tried so
many
> sources, my head is spinning,
>
> 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.
You may find it more effective to send such questions to the
r-sig-mixed-mod...@r-project.org mailing list.
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