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
On Dec 7, 2011 4:44 PM, "Erin Ryan" <erin@the-ryans.com> wrote:> > I am trying to specify a mixed model for my research, but I can't quiteget> it to work. I've spent several weeks looking thru various online sourcesto> no avail. I can't find an example of someone trying to do precisely whatI'm> trying to do. I'm hoping some smart member of this mailing list may beable> to help. > > First off, full disclosure: (1) I'm an engineer by trade, so the problemmay> 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 somany> 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 8002.1> 1997 AF A 0.002 7602.1> 1998 AF A 0.003 7602.1> 1999 AF A 0.005 7602.1> > 2001 AF A NA 9002.1> 2002 AF A 0.004 8802.1> 2003 AF A 0.005 8702.1> 2004 AF A 0.006 8702.1> 2005 AF A 0.006 9002.1> > 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 clearlycorrelation> in the random effects within each subject. The DepVar column representsthe> dependent variable which is a constant for each subject. All the data is > empirical, but I wish to create a predictive model. Specifically, I wishto> 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 Iselect> 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-Models@R-project.org mailing list. [[alternative HTML version deleted]]