Hi Ken, Thank you again. But, I have seen models with 10^5 and above with no issues with covariance step and correlations not reaching 0.95 but some with moderate levels. It will be interesting to know other experiences.
The 10^n rule is from the PK-PD Data analysis, Gabrielsson and Weiner, Edition 3, page 313. I read this book most of my grad school days. Regards, Ayyappa On Tue, Nov 29, 2022 at 9:35 AM Ken Kowalski <kgkowalsk...@gmail.com> wrote: > Hi Ayyappa, > > I have not seen this rule but it strikes me as being too liberal to apply > in pharmacometrics where n can be very large for the models we fit. If we > have a structural model with say n=4 or 5 parameters and then also > investigate covariate effects on these parameters it would not be unusual > to have a covariate model with n=20+ fixed effects parameters. I doubt we > can get the COV step to run such that we can observe a CN >10^20. > > I have not seen CN criteria indexed by n. The classifications of > collinearity that I've seen based on CN are: > > Moderate: 100 <= CN < 1000 > High: 1000 <= CN < 10,000 > Extreme: CN >= 10,000 > > Ken > > -----Original Message----- > From: Ayyappa Chaturvedula [mailto:ayyapp...@gmail.com] > Sent: Tuesday, November 29, 2022 10:20 AM > To: Ken Kowalski <kgkowalsk...@gmail.com> > Cc: nmusers@globomaxnm.com > Subject: Re: [NMusers] Condition number > > Thank you, Ken. It is very reassuring. > > I have also seen a discussion on other forums that Condition number as a > function of dimension of problem (n). I am seeing contradiction between > 10^n and a static >1000 approach. I am curious if someone can also comment > on this and 10^n rule? > > Regards, > Ayyappa > > > On Nov 29, 2022, at 9:04 AM, Ken Kowalski <kgkowalsk...@gmail.com> > wrote: > > > > Hi Ayyappa, > > > > I think the condition number was first proposed as a statistic to > > diagnose multicollinearity in multiple linear regression analyses > > based on an eigenvalue analysis of the X'X matrix. You can probably > > search the statistical literature and multiple linear regression > > textbooks to find various rules for the condition number as well as > > other statistics related to the eigenvalue analysis. For the CN<1000 > > rule I typically reference the following textbook: > > > > Montgomery and Peck (1982). Introduction to Linear Regression Analysis. > > Wiley, NY (pp. 301-302). > > > > The condition number is good at detecting model instability but it is > > not very good for identifying the source. Inspecting the correlation > > matrix for extreme pairwise correlations is better suited for > identifying the source of > > the instability when it only involves a couple of parameters. It > becomes > > more challenging to identify the source of the instability > > (multicollinearity) when the CN>1000 but none of the pairwise > > correlations are extreme |corr|>0.95. Although when CN>1000 often we > > will find several pairwise correlations that are moderately high > > |corr|>0.7 but it may be hard to uncover a pattern or source of the > > instability without trying alternative models that may eliminate one > > or more of the parameters associated with these moderate to high > correlations. > > > > Best, > > > > Ken > > > > Kenneth G. Kowalski > > Kowalski PMetrics Consulting, LLC > > Email: kgkowalsk...@gmail.com > > Cell: 248-207-5082 > > > > > > > > -----Original Message----- > > From: owner-nmus...@globomaxnm.com > > [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Ayyappa > > Chaturvedula > > Sent: Tuesday, November 29, 2022 8:52 AM > > To: nmusers@globomaxnm.com > > Subject: [NMusers] Condition number > > > > Dear all, > > I am wondering if someone can provide references for the condition > > number thresholds we are seeing (<1000) etc. Also, the other way I > > have seen when I was in graduate school that condition number <10^n > > (n- number of parameters) is OK. Personally, I am depending on > > correlation matrix rather than condition number and have seen cases > > where condition number is large (according to 1000 rule but less than > > 10^n rule) but correlation matrix is fine. > > > > I want to provide these for my teaching purposes and any help is > > greatly appreciated. > > > > Regards, > > Ayyappa > > > > > > -- > > This email has been checked for viruses by Avast antivirus software. > > www.avast.com > > > -- > This email has been checked for viruses by Avast antivirus software. > www.avast.com >
