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.
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