NONMEM vs. R for linear mixed-effects

Colleagues I am implementing a linear mixed-effects model in R. Out of curiosity (and to confirm that I was doing the right thing), I wrote the code initially in NONMEM, then tried to replicate the results in R. The dataset is four (identical) treatments for one subject and the data are reasonably linear. For most subjects, the results from the NONMEM analysis are nearly identical to those from R. But, for one subject, the SLOPE/INTERCEPT are sufficiently different to concern me that I am implementing one of these (or possibly both) incorrectly. The critical code is: NONMEM: $PRED INTERCEPT = THETA(1) + ETA(1) SLOPE = THETA(2) + ETA(2) Y = INTERCEPT + SLOPE * TIME + EPS(1) R: LMER package: lmer(DV ~ TIME + (1|PERIOD), data=DATA, REML=FALSE) where: DV is the dependent variable PERIOD distinguishes the treatments (and is a factor) R: NLME package: lme(DV ~ TIME, random = ~ 1|PERIOD, data=DATA, method="ML") The two R packages yield identical results. Graphics from NONMEM and R differ slightly but there is no obvious preference between these approaches. Any thoughts as to a possible explanation? Dennis Dennis Fisher MD P < (The "P Less Than" Company) Phone / Fax: 1-866-PLessThan (1-866-753-7784) www.PLessThan.com http://www.plessthan.com/