cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
  • Instantly extract effect sizes, F-ratios, and FDR-adjusted p-values from your models with the Calculate Effects Sizes extension, available now in the JMP Marketplace!
  • New to JMP? Join us Sept. 23-24 for the Early User Edition of Discovery Summit, tailor-made for new users. Register now for free!
  • See how to use the JMP Marketplace – Free tools to expand JMP capabilities. Register. July 10, 2 pm US Eastern Time.

Discussions

Solve problems, and share tips and tricks with other JMP users.
Choose Language Hide Translation Bar
anon234
Level I

Partial Least Squares Regression

Hi,

I have a few questions about the k-fold cross-validation method in JMP for partial least squares regression.  I would really appreciate any help you can provide.

  1. Are the results from the folds averaged to give the Press statistic, or is the model with the best validation statistic chosen as the final model?  If the latter, would the Press statistic from leave-one-out cross-validation be more representative of the data?
  2. Is the "root mean Press" the same as the RMSEP (sqrt(Press/n))?

Thank you!

1 ACCEPTED SOLUTION

Accepted Solutions
anon234
Level I

Re: Partial Least Squares Regression

I emailed JMP Technical Support these questions and they replied as follows:

  1. Yes, the RMPRESS statistic is calculated from the results from different folds.  And then the model with the smallest values of RMPRESS is chosen as the best model.
  2. RMPRESS is the square root of the average squared residual from this prediction.

Hopefully this will help anyone else with similar questions.

View solution in original post

1 REPLY 1
anon234
Level I

Re: Partial Least Squares Regression

I emailed JMP Technical Support these questions and they replied as follows:

  1. Yes, the RMPRESS statistic is calculated from the results from different folds.  And then the model with the smallest values of RMPRESS is chosen as the best model.
  2. RMPRESS is the square root of the average squared residual from this prediction.

Hopefully this will help anyone else with similar questions.

Recommended Articles