How to make dummy variables in effects type coding instead of 0/1 coding?

[#1]

I want to add a dummy variable for every crop I use. I this link I found out how to make them but the dummys are in 0/1 coding. (https://community.jmp.com/t5/JMP-Academic-Knowledge-Base/Make-Indicator-Dummy-Variables/ta-p/22636). I would like to change these 0/1 coding in effects type coding (-1,0,1) but I don't know how.

Is there somebody who knows a solution?

In the attachment you find a part of my dataset and the colom from which I want to derive dummys.

Thanks

[#2]

First of all, why do you need such a column? JMP analysis platforms offer choices about parametrization and will handle it internally for you. See Help > Books > Fitting Linear Models.

Second, see Help > Scripting Index > Functions > Matrix. See the functions named Design(). There are several that create various kinds of indicator columns for you. A script could use this function to create the new columns for you. You can find more information in the Using JMP and Scripting Guide books.

[#3]

I'm using a non-linear model so the information about the linear model is not very adequte.

I'm determining a common critical value with my model but I also want to determine crop specific critical values. Therefore I need a dummy for every crop. In 0/1 coding not everthing is estimated because there is collinearity so I want to try it with effects-type coding (-1,0,1) to avoid this collinearity. 

Also I'm not that familiar with making scripts so that makes it hard to use something in the scripting index

[#4 · Accepted solution]

I used your example. I deleted all but the first column with the crop levels. I wrote a script to make the predictor columns for each crop using the nominal modeling type (0,1,-1) and saved it with the data table. Run the script to see the result.

[#5]

Thank you very much!

[#6]

See Help > Books > Predictive and Specialized Modeling. I assume that you read the chapter about using the Nonlinear platform and you are specifying a custom model with a column formula. Did you notice the section about using the levels of a categorical predictor (such as crop) as a grouping variable in the model?