RE: [NMusers] Parameter uncertainty

Hi Fanny, Marc I was thinking in the same direction as Marc. If you use MCMC (BAYES method in NONMEM) the algorithm will provide you with samples from the posterior density (posterior = likelihood * prior). From these samples you can then investigate different statistics, for example variance

Re: [NMusers] Parameter uncertainty

Dear Fanny, One additional method to obtain the parameter uncertainty, which I don't believe was mentioned, is Bayesian estimation using Markov-Chain Monte Carlo (MCMC) simulation. This method provides a full joint posterior distribution (e.g. uncertainty distribution) of the parameters and any

[NMusers] Mango Solutions two day R for Pharmacometrics training course PAGE Budapest 2017

Dear All, Mango are running a two day R for Pharmacometrics training course to coincide with this year's PAGE conference in Budapest, Hungary. This will be satellite training course provided on 5-6 June 2017 at the Novotel Budapest City hotel. The R for PK course is designed to review the basic

[NMusers] IOV with Mu modeling, using EM algorithms

Dear NMusers, Based on the NONMEM Inter-Occasion Variability (IOV) example control stream, located at NONMEM_install_dir/examples/example7.ctl, it seems that it is currently not possible to Mu parameterize IOV in NONMEM 7.3. The relevant lines from example7.ctl control stream are copypasted

Re: [NMusers] Parameter uncertainty

Thank you all for your responses. It is going to be very useful for my work. Best regards, F.G. 2017-02-15 17:35 GMT+01:00 Williams, Jason : > Dear Fanny, > > > > Another useful tool you may want to try is using the mrgsolve package > available in R, developed by