You didn't give your results (but DID give a script -- hooray!). I made a small change -- got rid of the bounds and added trace=TRUE, and got the output
## after 5001 Jacobian and 6997 function evaluations ## name coeff SE tstat pval gradient JSingval ## p1 53.1753 NA NA NA 0.01591 1.158e+13 ## p2 8.296 NA NA NA 8.959e+11 4.549 ## p3 -7.47638 NA NA NA -0.002521 0.3049 ## p4 -1.64963 NA NA NA -0.003805 0.1073 ## p5 1.44299 NA NA NA 0.001269 0.02521 ## p6 91.1994 NA NA NA -0.01548 0.01474 ## > Sorry that this doesn't display correctly in plain text emailer (wrapped lines). However, it shows 1) This is a pretty nasty problem that has NOT got to the convergence point, as indicated by 5001 Jacobians. In that case I don't give the summary(). That is a hint to provide more diagnostics when I do some upgrade (in process -- new nls14() with Duncan Murdoch is on r-forge now, but much work to be done). 2) The Jacobian is effectively singular. 3) The parameter scaling is awful. Maybe time to reformulate. Best, JN On 14-08-28 06:00 AM, r-help-requ...@r-project.org wrote: > Message: 23 > Date: Wed, 27 Aug 2014 12:52:59 -0700 > From: Andras Farkas <motyoc...@yahoo.com> > To: r-help@r-project.org > Subject: [R] nlxb generating no SE > Message-ID: > <1409169179.90920.yahoomailba...@web161605.mail.bf1.yahoo.com> > Content-Type: text/plain; charset=us-ascii > > 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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
