Dear All please provide insights to the following, if possible: we have E <-c(8.2638 ,7.9634, 7.5636, 6.8669, 5.7599, 8.1890, 8.2960, 8.1481, 8.1371, 8.1322 ,7.9488, 7.8416, 8.0650, 8.1753, 8.0986 ,8.0224, 8.0942, 8.0357, 7.8794, 7.8691, 8.0660, 8.0753, 8.0447, 7.8647, 7.8837, 7.8416, 7.6967, 7.4922, 7.7161, 7.6378 ,7.5128 ,7.4886, 7.4667, 7.3940, 7.2450, 7.1756, 6.7253, 6.7213, 6.9897, 6.7053, 6.3637, 6.8318 ,5.5420, 6.8955, 6.6074, 7.0689, 0.0010 ,1.3010, 1.3010 ,0.0010, 0.0010) D1<- c(0.00, 0.00, 0.00 , 0.00, 0.00, 0.25, 0.50 , 1.00 , 2.00, 4.00, 8.00, 16.00, 32.00, 0.25, 0.50, 1.00, 2.00, 4.00, 8.00, 16.00, 32.00 , 0.25 ,0.50, 1.00 , 2.00, 4.00 , 8.00, 16.00 ,32.00 , 0.25 , 0.50 , 1.00 , 2.00, 4.00, 8.00, 16.00 , 0.25, 0.50 , 1.00 ,2.00, 4.00, 8.00 ,16.00, 0.25, 0.50, 1.00, 4.00, 8.00, 16.00, 32.00, 32.00) D2 <-c(4 , 8, 16, 32, 64, 0, 0, 0, 0, 0, 0, 0, 0, 4, 4, 4, 4, 4, 4, 4, 4, 8, 8, 8, 8, 8, 8, 8, 8, 16 ,16 ,16, 16, 16, 16, 16, 32 ,32 ,32, 32, 32, 32, 32, 64, 64, 64, 64, 64, 64, 64, 32) y <-rep(1,length(E)) raw <-data.frame(D1,D2,E,y) require(nlmrt) start <-list(p1=60,p2=9,p3=-8.01258,p4=-1.74327,p5=-5,p6=82.8655) print(nlxb <-nlxb(y ~D1/(p1*((E/(p2-E))^(1/p3)))+D2/(p6*((E/(p2-E))^(1/p4)))+(p5*D1*D2)/(p1*p6*((E/(p2-E))^(0.5/p3+0.5/p4))), start=start,data=raw, lower=-Inf, upper=Inf)) and once you run the code you will see the "best" I was able to get out of this data set using the model. "Best" here means the result that made most sense from the perspective of applying it to life science.... My question is related to the lack of calculated SEs (standard errors, correct me if I am wrong)... I would like to calculate CIs for the parameters, and as far as I understand SEs would be needed to be able to do that. Any suggestions for how we may establish 95% CIs for the estimated parameters? appreciate your input, thanks, Andras