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JMP CUSTOM DESIGN

Gabriel

Good day, Mark Bailey @Mark_Bailey ,
Please I have an issue setting up a custom design for my experiment.
According to the design experiment, it is supposed to be a Split plot design (having two drought treatments, with each having 3 XYZ-crop genotype replicated 4 times giving it 24 runs in all I believe). However, I am finding it difficult to set it up in custom design or should I say I'm not confident enough with what I did because I am new to JMP.
This is what I did:
I set my response (which is Chlorophyll), Factors as Treatment (Hard): two levels(WL-water limited and WW-Well watered) and Genotype as Easy (3 levels: x, y, z), Set whole plot as 2 and runs as 24 but I'm in doubt of how to set in the replicates. Please I need your assistance on the right path. Thank you

Accepted answers

Mark_Bailey

Well, I wish you were using more than two whole plots, but I understand that you have a practical constraint.

Here is a custom design definition that I think captures the essential characteristics as you explained them.

Capture 1.JPG

This design is the result.

Capture 2.JPG

The warning in red is by the custom design. Here is a script that will re-produce the custom design platform for you to study and possible use. You can copy it here and paste it into a new script window.

DOE(
	Custom Design,
	{Add Response( Maximize, "Chlorophyll", ., ., . ),
	Add Factor( Categorical, {"Water Limited", "Well Watered"}, "Drought", 1 ),
	Add Factor( Categorical, {"x", "y", "z"}, "Genotype", 0 ),
	Set Random Seed( 809122516 ), Number of Starts( 13501 ), Add Term( {1, 0} ),
	Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {1, 1}, {2, 1} ),
	Set N Whole Plots( 2 ), Set Sample Size( 24 ), Simulate Responses( 0 ),
	Save X Matrix( 0 )}
);

Mark_Bailey

I would use the approach of adding nine data columns to record the data. You can stack these columns for the analysis and add the date as a continuous numeric factor. You can cross date with the other factors to determine if the rate depends on the conditions.

All comments

Mark_Bailey

First of all, you needn't address a discussion to me. I will answer if I can. But you might inhibit other community members from replying with potentially a better idea than mine!

Why is the treatment hard to change? How will you run the experiment?

Also, if you have two levels of the hard-to-change factor and you request only two whole plots, you will lose some estimation because JMP will have to steal degrees of freedom from the model itself. Can you use 4 whole plots or more?

Gabriel

Oh noted, I will endeavor to make it open to all next time.

I am really not sure of how to set the nature of my experiment up in JMP for proper analysis. Like earlier posts I have 3 genotype of a crop having 4 replicates each, subject to two different drought treatments. (Main factors: Drought treatment and Genotype). I have attached a view of my field map. Green represents the Well water and Red represents WL.

Thank you

Field Map.png

Mark_Bailey

Well, I wish you were using more than two whole plots, but I understand that you have a practical constraint.

Here is a custom design definition that I think captures the essential characteristics as you explained them.

Capture 1.JPG

This design is the result.

Capture 2.JPG

DOE(
	Custom Design,
	{Add Response( Maximize, "Chlorophyll", ., ., . ),
	Add Factor( Categorical, {"Water Limited", "Well Watered"}, "Drought", 1 ),
	Add Factor( Categorical, {"x", "y", "z"}, "Genotype", 0 ),
	Set Random Seed( 809122516 ), Number of Starts( 13501 ), Add Term( {1, 0} ),
	Add Term( {1, 1} ), Add Term( {2, 1} ), Add Term( {1, 1}, {2, 1} ),
	Set N Whole Plots( 2 ), Set Sample Size( 24 ), Simulate Responses( 0 ),
	Save X Matrix( 0 )}
);

Gabriel

Thank you so much.

Would the model still remain valid if I stack the results of the chlorophyll response gotten over a span of days?

Like following the model design; I have Chlorophyll for days 1, 2, 3 ..... n, is it possible to stack them and have another variable Date still building it from the model source?

Thank you...