Dear Friends, I am writing to seek any help on "lme" error message. I am using lme to do Mixed-model linear regression. I use my simulated data. However, sometimes, I get the following error message, which I do not understand. "Error in solve.default(pdMatrix(a, fact=TRUE)): system is computationally sigular" I would appreciate any help about it. Thanks a lot Jingyuan Fu Drs, Groningen Bioinformatics Center the Netherlands [[alternative HTML version deleted]]
"J Fu" <j.fu at cs.rug.nl> writes:> I am writing to seek any help on "lme" error message. I am using lme > to do Mixed-model linear regression. I use my simulated > data. However, sometimes, I get the following error message, which I > do not understand. > > "Error in solve.default(pdMatrix(a, fact=TRUE)): system is computationally sigular" > > I would appreciate any help about it.This is usually an indication that the model is overfitting the data, which is not an unusual situation for simulated data. A simple example would be a variance component model being simulated under the null hypothesis that the variance component for the random effects is zero. In a large proportion of the cases (roughly half) the simulated data will be such that the MLE and REML estimates of the variance component are exactly zero. The code that is failing is trying to invert the factor of the relative variance-covariance matrix, which is approaching singularity. You could wrap your call to lme in try() or tryCatch() so you can detect the situations where lme fails to converge and decide what to do with them. -- Douglas Bates bates at stat.wisc.edu Statistics Department 608/262-2598 University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/