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.

Erin

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