[R-sig-ME] FW: Variogram / confint prediction in LMM / time prediction some questions from Switerland area
Ben Bolker
bbo|ker @end|ng |rom gm@||@com
Sun Aug 8 21:11:30 CEST 2021
On 8/8/21 5:17 AM, mudryjm wrote:
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> Sent: Sunday, August 1, 2021 5:32 PM
> To: mudryjm <mudryjm using bluewin.ch>
> Subject: Re: Variogram / confint prediction in LMM / time prediction some questions from Switerland area
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> I regret that I am not able to answer your questions at this time. It has been a couple of decades since I worked on the nlme package and I have not kept up with the literature.
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> I suggest that you send your questions to the R-SIG-Mixed-Models using R-project.org <mailto:R-SIG-Mixed-Models using R-project.org> mailing list. Some information about the list is available at https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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> On Sun, Aug 1, 2021 at 9:25 AM mudryjm <mudryjm using bluewin.ch <mailto:mudryjm using bluewin.ch> > wrote:
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> Dear Mr Bates and Pinhero
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> Tks for your incredible packages in R I did my master in statistics (attached). I’m a big fan!
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> However I’m puzzled with several theoretical questions on some topic where I’m struggling; instead of luring for stat input I asks directly WIZARDS!:
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> Variograms (Cressie):P 52 PDF
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> I have done several variograms on my model residuals expecting flat line along (sill flat).However some patient are at sill some on the correlation path-slope.
I'm not really sure what this means. One very common issue is that
if you simply specify residuals(fitted_model), you get "raw"
(observed-fitted) residuals; if you want residuals that take the
autocorrelation structure of the model into account, you need to specify
type="normalized" (see ?nlme::residuals.lme)
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> Q:How to interpret? (Two class of patient with biophysical discerpancies?
Hard to say without more detail.
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> Q:The smoother fitted in graph is the theoretical one (Theoretical variogram based on exp?spherical?)?? Or is it a simple smoother.
Not sure what you're referring to. Perhaps some attached images got
lost (the mailing list strips some attachment types).
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> Q: Is it possible with variogram (within (patient and sampling error) cor + between from LMM variance) to forecast the best time for re-measure? > I.e in cholesterol study some authors demonstrated when intra-
variability exceed between variability? So measuring on too short
intervals has no value.But I don’t know how to proceed
This doesn't seem to have an obvious answer, but measurements taken
with a time interval large enough that the estimated autocorrelation
would be small would provide more information (e.g. if the
autocorrelation model gives you a scale parameter, autocorrelation
levels would typically drop to 'small' levels at a time difference of
2*scale or 3*scale - but exactly how far it would drop depends on the
particular autocorrelation model used, and the 'best' time for
remeasurement would depend on a whole lot of details of the particular
study system; how expensive or inconvenient is it to take additional
samples? An autocorrelated sample doesn't provide *no* information, it
just provides less information than one taken at a (typically) longer
time interval.
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> Or do you had a good reference.?
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> Fiiting confindance bands on XB+ZU:
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> Q.I try to get con band for predictings patients trajectories. As they is no close form (at least very complex) is the better way to bootstrap some model and fit a bootstrap confint on the 1000 Fitted values? Or do you have a smplier function?
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Bootstrapping seems like a good approach.
Since your data are autocorrelated, you might need block
bootstrapping (or, if you trust the model reasonably well, parametric
bootstrapping)
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> Q:If I should predict when time will be reached for a patient his Upper reference limit how do I have to proceed? (back reversing formula?)
> Page 54 :patients prediction
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> Tks for your help in an old retrained statistician
Yes, you probably need to invert the formula. You might end up
needing to solve numerically (?uniroot)
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> NB Your contribution to staworld is fabulous and inspired me in my daily work since a year!
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> :)
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> Veuillez recevoir mes sincères salutations ,
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> Sincerely,
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> Mudry Jean-Marie
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> 8 ch du Châno
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> 1802 CORSEAUX
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> Switzerland
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> +41.79.708.87.15
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> +41.21.921.10.18.
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--
Dr. Benjamin Bolker
Professor, Mathematics & Statistics and Biology, McMaster University
Director, School of Computational Science and Engineering
Graduate chair, Mathematics & Statistics
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