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Repeated Measures with Time-Course Data

I have a dataset where the output (yield) of three treatments (varieties) was measured over the course of two months. I would like to do a repeated measures analysis on these data using the measurement date (harvest date in my dataset) as the repeated measure. Looking through existing JMP documentation (like this: http://www.jmp.com/support/notes/30/584.html), I do not see how I can run the analysis based on one of my variables. Also, I only have JMP, not JMP Pro. I know that in SAS I could run something like  PROC Mixed repeating on date; is there something similar that I could do in JMP?

 

My sample dataset is attached.

 

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Re: Repeated Measures with Time-Course Data

For reference, I am using the second method that is described in the technical note that your cited because you do not have JMP Pro. I imported the data from the Excel workbook. The data did not require any further change. I set up the model with plot as a nested random effect. The rest of the model included the variety (nominal modeling type) and harvest data (ordinal modeling type) with a crossed term. The variety does not appear to be significant for either response. Many degrees of freedom go to estimating all the harvest data parameters, though. I tried using the continuous modeling type for harvest data but was not successful.

 

I saved the script for the Fit Model launch dialog and for the Fit Least Squares platform. I uploaded this data table for you here.

Learn it once, use it forever!

View solution in original post

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Highlighted

Re: Repeated Measures with Time-Course Data

For reference, I am using the second method that is described in the technical note that your cited because you do not have JMP Pro. I imported the data from the Excel workbook. The data did not require any further change. I set up the model with plot as a nested random effect. The rest of the model included the variety (nominal modeling type) and harvest data (ordinal modeling type) with a crossed term. The variety does not appear to be significant for either response. Many degrees of freedom go to estimating all the harvest data parameters, though. I tried using the continuous modeling type for harvest data but was not successful.

 

I saved the script for the Fit Model launch dialog and for the Fit Least Squares platform. I uploaded this data table for you here.

Learn it once, use it forever!

View solution in original post

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