[R] question on multilevel modeling

[Dave Atkins]

Christine--

You have two and only two individuals per dyad; when you try to fit random 
slopes at level-2 (within couples), you are attempting to estimate an intercept, 
slope, and covariance for each individual.  Basically, you don't have enough 
data to do it.  If you restrict yourself to a single random-effect at level-2, 
you shouldn't have any problems.  So, it's not a code problem; it's an issue of 
the amount of data (or nature of your data) relative to the model you're 
attempting to fit.

You might want to see a paper I wrote on HLM with dyadic data (which has an 
appendix with R code); I discuss this issue some and various strategies for 
dealing with it:

Atkins, D. C. (2005).  Using multilevel models to analyze marital and family 
treatment data: Basic and advanced issues.  Journal of Family Psychology, 19, 
98-110.

Hope that helps.

cheers, Dave


Hi,

I am trying to run a multilevel model with time nested in people and
people nested in dyads (3 levels of nesting) by initially running a
series of models to test whether the slope/intercept should be fixed or
random.  The problem that I am experiencing appears to arise between the
random intercept, fixed slope equation AND.

(syntax:
rint<-lme(BDIAFTER~BDI+WEEK+CORUMTO, random=~1|DYADID/PARTICIP,
data=new)
summary(rint))

the random slope, random intercept model

(syntax:
rslint<-lme(BDIAFTER~BDI+WEEK+CORUMTO, random=~CORUMTO|DYADID/PARTICIP,
data=new)
summary(rslint))

at which point I obtain the exact same results for each model suggesting
that one of the model is not properly specifying the slope or intercept.

Or, I receive the following error message when I try to run the random
slope/random intercept model.

Error in solve.default(pdMatrix(a, fact = TRUE)) :
         system is computationally singular: reciprocal condition number
= 6.77073e-017

(whether I receive an error message or the same results depends on the
specific variables in the model).

It has been suggested that I may need to change the default starting
values in the model because I may be approaching a boundary-is this a
plausible explanation for my difficulties?  If so, how do I do this in R
and can you refer me to a source that might highlight what would be
reasonable starting values?
If this does not seem like the problem, any idea what the problem may be
and how I might fix it?

Thank you so much for your assistance,
Christine Calmes


Christine A. Calmes, MA
Dept of Psychology
University at Buffalo: The State University of New York
Park Hall 216
Buffalo, NY 14260
ccalmes at buffalo.edu
(716) 645-3650 x578

-- 
Dave Atkins, PhD
Assistant Professor in Clinical Psychology
Fuller Graduate School of Psychology
Email: datkins at fuller.edu
Phone: 626.584.5554