How to screen easily the non significant of many factors(~200 columns) before to perform a mulitple regression.I have many factor (~200 columns) and one response,i have many measures.
Most of this factors show colinearity between them,is there a simple way to perform a first filter of the not relevant factors?
Then in the remaining i will perform a multiple regression.
Predictive Modeling and Machine LearningRe: How to easily screen the non significant of many factors (~200 columns) before to perform a multiple regression.Good morning,
To classify parameters for a response variable you can use several tools:
- Screening of regressors
- Screening of responses
The two tools may not give the same results. In this case, this may mean that there are “hidden” terms which are not explicit in the model: quadratic terms, cross terms.
On the other hand, you can use least squares regression (linear model), if you want to have a prediction formula. With this method, you can build your model step by step, removing parameters that have no impact on your model.
Re: How to screen easily the non significant of many factors(~200 columns) before to perform a mulitple regression.Hi @Perpignan_italy,
If you want to highlight the factors contributing most to your response, the Predictor Screening (jmp.com) platform seems a good first exploration step.
As it is based on Random Forest, it will enable each factor/feature to be tested "equally", even in the presence of multicollinearity.
If you want to create a model based on the most important predictors and use a regression model, you may have to choose an adequate model (penalized like Lasso, Ridge or Elastic Net regression, or other regression models like Partial Least Squares, ...) or do some pre-processing (with Principal Component analysis and doing the regression on principal components) in order to account for multicollinearity correctly in your model.
You may also use simple models that are able to deal with multicollinearity and with less asumptions (like Decision Tree from platform Partition Models (jmp.com)) once your predictors are selected.
I hope this answer will help you,
Re: How to screen easily the non significant of many factors(~200 columns) before to perform a mulitple regression.Thank you very much,i will try your suggestion.
