- Start from a lower-order model (like just a constant) instead of starting from a more complex model.
- Change either your tolerance on the F-stat or change the criterion to either aic or bic and play with the tolerance.
I am getting p values more than 0.05 while doing step regression? How to handle the resulted equation
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Dear Sirs, I am using stepwise regression using 'stepwiselm' function. In my resulted equation, I am getting following p values in which some are greater than 0.05 and hence not required. How to avoid this type of results? p '(Intercept)' 0.032411401 'Mx' 0.001895131 'My' 0.001504061 'R' 0.029935921 'Mxsin' 0.966402394 'MxRsin' 0.50806764 'Rsin' 0.408869773
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Accepted Answer
jgg
on 3 Dec 2015
Edited: jgg
on 3 Dec 2015
This behaviour is actually expected in stepwiselm.
If you're running stepwiselm with the default options, the issue is because the selection criterion is not based on the p-value of the coefficient directly. Specifically, the default criterion uses the p-value of the F-stat for a comparison of the two models. Depending on your data, and the starting model, you can have individual coefficients which have non-significant coefficients (based on some level) but which pass the inclusion or exclusion criteria.
The second model they present there has a coefficient with a p-value of 0.08. In general, you wouldn't want to do data analysis using the p-value directly, because you're essentially just data mining for significant coefficients at that point, instead of considering the explanatory power of the model as a whole. A coefficient may fall below some (arbitrary) significance level but still be informative as a control in the model; this is especially true of categorical controls.
There are a couple of things you can try:
In general, though, you should try not to exclude this outcome. This model is the best fit for your data!
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