Pavel,
I am not sure what is the problem with the log-transformation of the
data. log(x) = infinity only if x = infinity, do you have infinite
observations in your data set? If not, then transformed data cannot be
equal to infinity.
log(x) = - infinity only if x=0
do you have BQL observations coded as zeros? If so, you cannot use
exponential error model. But you can either exclude BQLs (and use
log-transformation) or treat them as BQLs (and still use
log-transformation).
Looks like your prediction F is between 0 and 1. I do not think that
exponential error is appropriate for this type of data. Could you
elaborate what exactly you are modeling? If this is indeed interval
data, this poster can be relevant (Estimating Transformations for
Population Models of Continuous, Closed Interval Data, Matthew M.
Hutmacher and Jonathan L. French):
http://www.page-meeting.org/default.asp?abstract=1463
Thanks
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
[email protected] wrote:
Hello,
NONMEM has the following property related to intra-subject variability:
"During estimation by the first-order method, the exponential model and
proportional models give identical results, i.e., NONMEM cannot
distinguish between them." So, NONMEM transforms F*DEXP(ERR(1)) into F
+ F*ERR(1).
Is there an easy around it? / /I try to code the logit transformation.
I cannot log-transform the original data as it is suggested in some
publications including the presentation by Plan and Karlsson (Uppsala)
because many values will be equal to plus or minus infinity. Will
NONMEM "linearize" the following code:
Z = DLOG((F+THETA(10))/(1-F+THETA(10)))
Y = DEXP(Z + ERR(1))/(1 + DEXP(Z + ERR(1)))
Thanks!
Pavel
