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Jul 28, 2010 12:01 AM
(960 views)

How is such a design correctly modelled in jmp?

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Jul 28, 2010 1:47 PM
(904 views)

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Jul 28, 2010 10:57 PM
(904 views)

I usually do that, but I think I lose some information and, in addition, depending on the number of samples I draw, I have quite a few responses, which makes it difficult to judge on the best settings.

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Aug 1, 2010 2:12 AM
(904 views)

Another way you might be able to tackle it might be to see if you can identify any features common to the time variable and re-express the analysis in those terms: for example, if you expected every sequence of time points for any one combination of settings to be a realisation of an exponential decay curve, you could log all your data and analyse the whole thing as a multiple regression problem in which you'd test for both lack of parallelism and lack of linearity. Having said that, if you did something like that you probably ought to be trying to incorporate the autocorrelations between successive time points into the analysis as well, though I'll guess that a lot of people probably wouldn't bother.

You last post suggests that you have a choice when it comes to deciding which samples you draw: could you elaborate on that? I'd originally assumed that the design was completely orthogonal, but if it isn't, then maybe some sort of EVOP approach might be more appropriate to the problem. Just a thought.

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Aug 9, 2010 9:23 AM
(904 views)

Thanks for the comprehensive answer. The MANOVA approach appears to me very reasonable, I, however, don't know, how to get predictions or find optimum solutions from that. That means I have about 3-4 sampling time points and the response is clearly depending on time. There is also an interaction between my factors and time (which is significant in the MANOVA). So I think I should not simply average the points over time.

Ideally I would just use time as a factor, identically to my other factors. From that I could also find the optimum "harvest" time point, also depending on the others factors settings.

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Aug 10, 2010 8:55 AM
(904 views)

If Time is treated as a continuous variable, you could generate a prediction for any combination of pH, Temperature and any specific time simply by adding extra rows onto the bottom of your data set for the factor levels for which you want predictions, but with the value of the response variable set to missing. Once you've fitted whatever model you want to the data, click on the "Response" tab's red triangle and select "Save Columns | Predicted Values". That will add a column to your original data set containing the predicted values of every factor combination in the study, plus any time levels (including ones you haven't actually tested if you wish) that you're interested in.

Setting those extra factor levels in a systematic way would provide you with the means to fit a response surface "slice" of any cut through your data... but in doing something like the above, obviously you'll have disregarded completely any autocorrelations that might exist between the successive time points. I'm afraid I don't know how to accommodate autocorrelations into such an analysis - which is why I guessed earlier that most people would probably ignore this little extra complication.

It's a long time ago now, but I seem to remember that Snedecor & Cochran's book on statistical methods once used to contain an example involving the baking times of 45 cakes made with different recipes to illustrate how to analyse a split-plot experiment, in which the error structure was assumed to be different inside the basic experimental unit, i.e. a cake (for within-cake comparisons involving time) compared with the error structure outside the "plot" (for between-recipe comparisons). I'm wondering therefore whether treating your experiment as a split-plot might go some way towards allowing for the effect of those autocorrelations (though obviously an analysis that incorporates them explicitly ought to be more appropriate). If it's of any help, this link http://support.sas.com/kb/24/512.html contains a link to a downloadable paper which describes how to both design and analyse a split-plot experiment in both SAS and JMP.

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Aug 1, 2010 8:14 AM
(904 views)

1. use control charts to analyze stability (IR)

2. Calculate an average and standard deviation over the time and do the analysis on both response variables (since these are not replicates, they do not give you more degrees of freedom, but can give you two response variables)

3. treat the time as if it were Blocks (how many will depend on the ability to identify subsets of time where the noise within the subset is somewhat stable). Write the model with the main effects, interactions, blocks and block by factor interactions.