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 =
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) =
The critical code is:
$PRED INTERCEPT = THETA(1) + ETA(1)
SLOPE = THETA(2) + ETA(2)
Y = INTERCEPT + SLOPE * TIME + =
R: LMER package:
lmer(DV ~ TIME + (1|PERIOD), data=DATA, REML=FALSE)
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 Fisher MD
P < (The "P Less Than" Company)
Phone / Fax: 1-866-PLessThan (1-866-753-7784)
Received on Wed Feb 28 2018 - 11:16:45 EST