Nick,
Thanks for your comments. We (+ Andreas Steingötter + Rickmer Braren) are
more rooted in physiology, physics, and statistics, so critical response
from the PK marked is highly appreciated.
You wrote:
On the other hand if the goal is to estimate the size of one or more
critical paramet
Thanks for your comments, Leonid.
To paraphrase your main argument: 7 negative eigenvalues mean 7 values close
to zero, so we have a highly over-parameterized system. While I fear it's
correct (I could not get untrendy CWRES otherwise), let's take Robert's
argument to the extreme:
Simplified, fr
Dieter,
You ask:
My question: can we trust this fit?
The answer depends on why you are doing the modelling.
If your goal is to describe the time course of concentrations then the
overall ability of the model to describe what you saw depends on the
totality of the model and its parameters.
I think, if at least 7 eigenvalues are nearly zero (up to the
numerical precision) it means that the model is greatly over
parametrized. While one can trust the model predictions, one may need to
investigate whether to trust the model parameter estimates. Results
indicate that there is a 7 (o
Dieter:
You can trust the fit. The negative eignvalue diagnostic arises from
evaluating the information matrix of the estimates evaluated after the fit.
Because this was constructed with Monte Carlo components, on occasion the
slight imprecision from calculating obscure off-diagonal elements r
