Re: $ERROR and LOGIT

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