Thanks for the insight Bert. The data I quoted are an example from a
book(so I've got actual answers to compare to), but for my ultimate
purpose (analytical method development) there would typically be 3-6
replicate readings from each of 4-6 runs, which is on a similar scale.
I'll look into the lme functions.
Thanks
Paul.
Bert Gunter wrote:
Paul:
If these data are real -- or at least a reasonable facsimile -- then even
though the machines might be considered "random" -- i.e. a sample from a
potential population of machines -- there are too few of them to get a
reasonable estimate of their variance. Better to treat them as fixed and
just do a standard oneway anova with a single "within" = residual error
component.
Incidentally, this situation is quite common in gauge R&R = analytical
method development studies. While the problem is unavoidable -- you only
have so many machines or operators or whatever -- it is unfortunate that
standard statistical references do not point out that it's just basically
silly to try to estimate variance components with so few df, the resulting
confidence intervals, as here, being so wide as to be useless (reflecting
the inadequate information).
Incidentally, a better way to get at this when there are sufficient df is
via the lme() function in the nlme package -- it will work with unbalanced
data and not just in the balanced data situation. But there would be a
considerable learning curve required, I realize.
Cheers,
Bert Gunter
Genentech Nonclinical Biostatistics
-----Original Message-----
From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
Behalf Of Paul
Sent: Wednesday, September 09, 2009 1:38 PM
To: R-help@r-project.org
Subject: [R] Stats help with calculating between and within subject variance
and confidence intervals
Hello.
I'm trying to find a way in R to calculate between and within subject
variances and confidence intervals for some analytical method
development data.
I've found a reference to a method in Burdick, R. K. & Graybill, F. A.
1992, Confidence Intervals on variance components, CRC Press. This
example is for Balanced Data confidence interval calculation from Pg
62. The data are fill weights from bottles sampled randomly from a
sample of four filling machines. There are 12 values, and the
confidence intervals are for 1-2a = 95%. I have got the same results
as the book but using slightly different fomulae (see variables for H1,
G1 and H12 and G12).
I'd appreciate any help, and any comments on whether their is a better
way to do this.
Thanks
Paul.
<snip>
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