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In forward model selection, the selection process is started with an empty model and variables are added sequentially.

Backwards R Full Model And

In backward selection, the selection process is started with the full model and variables are excluded sequentially.

The stepAIC function automatically prints each step of the selection process in the console and it seems like the selection starts with the full model.

However, based on the answer of jjet I am not sure if I have done anything wrong.

I dont know if theres an authoritative answer as to whether forward-backward implies a particular starting model, or which; but even if there is, itd be sensible not to assume other people know about it when they use the expression.

Based on your comment and jjets answer I was able to solve my problem.

In this procedure, you start with an empty model and build up sequentially just like in forward selection.

In this approach, you can end up searching nonlinearly through all the different models.

First, I set a seed so that you can see the same data I used.

But once x1, x2 and x3 enter the model, x6 becomes independent of y and should be excluded.

It starts with x6, proceeds to include x1, x2 and x3, then it goes back and drops x6 and terminates.

If you just use forward, then x6 will stay in the model because the algorithm never goes back to this sort of multicollinearity check.

If you dont have the scope list defined, it will assume your lower model is whatever model you specified initially and your upper model is the full model.

Hence, you need to look for suboptimal, computationally efficient strategies.

See for example Floating search methods in feature selection by Pudil et.

For each feature you train a model, and select the feature which yields the best model according to your metric.

In a similar fashion you proceed by adding the next feature that yield the best improvement when combined with the already selected ones.

You select the set of features with which you can build the best model of a particular type (in your case a linear model).

Backwards R Pdf And Rstudio

This is better illustrated in these two links: stat.columbia.edumartinW2024R10.pdf and rstudio-pubs-static.s3.amazonaws.com.

Provide details and share your research But avoid Asking for help, clarification, or responding to other answers.

Making statements based on opinion; back them up with references or personal experience.

MathJax reference.

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