function for all possible regression models in model selection

Question

I have a linear regression model:

$Y~X_1+X_2+X_3+X_4+X_5+X_6+X_7+X_8+X_9$ and I need to create a function that find all possible models (e.g.

$Y = X_1+X_2+X_3+X_4$
$Y = X_2+X_3+X_4$
$Y = X_1+X_3+X_4+X_5+X_6$ etc) and then calculates each model's DIC values.

Can someone help me since I'm not experienced at programming?

Just out of curiousity, why do you want this technique? Is it to find the most overfit model? — IWS, Apr 04 '17 at 07:29
It's in the context of my internship project where I have to use different model selection criteria for a given dataset — j.erm, Apr 04 '17 at 09:35

score -2 · Accepted Answer · answered Apr 04 '17 at 08:07

-2

Just use a backward or forward method to do so.

Forward: Just add and remove all your independent variables and see how each variable affect the model. Then keep adding and subtracting variables until you got a good model.

Backward: Add all variables and then remove them one by one and readd them to evaluate your model.

answered Apr 04 '17 at 08:07

arezaie

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On could argue that "just use" should rather be replaced with "don't use": http://stats.stackexchange.com/questions/20836/algorithms-for-automatic-model-selection/20856#20856 – Tim Apr 04 '17 at 08:17
Fitting every model and using a criterion (AIC, BIC, BICc) is a valid way to do model selection for a small number of predictors. Backwards and forward variable selection have fallen out of favor because they produce highly variable results. – Eli Sep 21 '18 at 15:41
This algorithm does not explore the "all possible subsets" of models requested in the question. – whuber Sep 21 '18 at 15:42

function for all possible regression models in model selection

1 Answers1