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KellyG
Community Member

Predictor Screening

Hello,

 

Can you tell me if there is a level below which the contribution number in the predictor screening tool is indicating that the predictor is weak? The contribution numbers look really low on my analysis. The #1 ranked predictor only had a contribution of "1.50671".

 

Thank you

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Victor_G
Community Trekker

Re: Predictor Screening

Hello Kelly,

There are good ressources and information about this analysis if you go to the "Help" section of JMP (either click on the thin line grey/blue, choose the question mark and click on your Predictor screening report, or go to your JMP Home window, and click on F1 or Help menu and look the contents related to predictor screening).

Predictor screening is based on bootstrap forest in order to identify potential predictors of your response, so you'll have information about how much your predictors explain the variation of your response, but no information (to my knowledge) about significance level. Also something to consider, your "contribution number" may vary if you rerun your analysis (because of the bootstrap forest model which involves random component). So I would also look at the portion your #1 predictor explain variation in the response.
I would have a look on your column "Portion" in order to see if you can identify a small number of predictors with a high percentage for explaining the response variation, or if all predictors seem to have an equivalent portion contribution to the response.
In the second case, either a high number of predictors are required (and have an equivalent importance) in order to explain variation in the response, or you may miss in your analysis one or several important predictors not taken into account in your analysis (not measured for example).
I hope these few words will help you :)

1 REPLY 1
Highlighted
Victor_G
Community Trekker

Re: Predictor Screening

Hello Kelly,

There are good ressources and information about this analysis if you go to the "Help" section of JMP (either click on the thin line grey/blue, choose the question mark and click on your Predictor screening report, or go to your JMP Home window, and click on F1 or Help menu and look the contents related to predictor screening).

Predictor screening is based on bootstrap forest in order to identify potential predictors of your response, so you'll have information about how much your predictors explain the variation of your response, but no information (to my knowledge) about significance level. Also something to consider, your "contribution number" may vary if you rerun your analysis (because of the bootstrap forest model which involves random component). So I would also look at the portion your #1 predictor explain variation in the response.
I would have a look on your column "Portion" in order to see if you can identify a small number of predictors with a high percentage for explaining the response variation, or if all predictors seem to have an equivalent portion contribution to the response.
In the second case, either a high number of predictors are required (and have an equivalent importance) in order to explain variation in the response, or you may miss in your analysis one or several important predictors not taken into account in your analysis (not measured for example).
I hope these few words will help you :)