I think - though I'm not sure about this - you may have difficulty reconciling the requirement to predict response over time with the need to set up time as a factor for inclusion in a repeated measures design, because the latter would require it to have its modeling type set to nominal or ordinal, whereas calculating a prediction (at least for any time) would need it to be continuous. 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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