From: Yaning WangSubject: [NMusers] Weighting in NONMEM Date:Mon, 02 Jun 2003 10:26:11 -0400 Hi all: This may be a little bit distracting from the main question for this thread (reference to: 99jun022003), but could anyone explain why a weighting like W=SQRT(THETA(5)**2+THETA(6)**2/F**2) used by Justin is equivelant to a combination error model as NONMEM guide suggested? What is the advantage of this form to the standard combination error model? Any suggestion is appreciated. -- Yaning Wang Department of Pharmaceutics College of Pharmacy University of Florida _______________________________________________________ From:Leonid Gibiansky Subject:Re: [NMusers] Weighting in NONMEM Date: Mon, 02 Jun 2003 14:17:00 -0400 Yaning, This error model was suggested by Mats Karlsson (posted 29 April 2002) as an error model that combines additive and proportional errors and can be used with the log-transformed data: "To get the same error structure for log-transformed data as the additive+proportional on the normal scale, I use Y=LOG(F)+SQRT(THETA(x)**2+THETA(y)**2/F**2)*EPS(1) with $SIGMA 1 FIX " In non-transformed terms Conc=F*EXP(SQRT(THETA(x)**2+THETA(y)**2/F**2)*EPS(1)) Assuming that EXP(x)=1+x (for small x), you get Conc=F*(1+SQRT(THETA(x)**2+THETA(y)**2/F**2)*EPS(1)) Variance of this expression Var(conc)=F**2*(THETA(x)**2+THETA(y)**2/F**2)= = F**2*THETA(x)**2+THETA(y)**2 On the other hand, for the error model Y=F exp(EPS1)+EPS2=F(1+EPS1)+EPS2 variance is equal to F**2*OMEGA1+OMEGA2 Thus, these models are similar if not identical with OMEGA1=THETA(x)**2, OMEGA2=THETA(x)**2, Leonid _______________________________________________________