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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?

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    $\begingroup$ Just out of curiousity, why do you want this technique? Is it to find the most overfit model? $\endgroup$
    – IWS
    Apr 4, 2017 at 7:29
  • $\begingroup$ It's in the context of my internship project where I have to use different model selection criteria for a given dataset $\endgroup$
    – j.erm
    Apr 4, 2017 at 9:35
  • $\begingroup$ See the dredge function in package MuMIn. $\endgroup$
    – Roland
    Apr 4, 2017 at 12:48
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    $\begingroup$ R has a package for all possible subsets: leaps (on CRAN) $\endgroup$
    – kjetil b halvorsen ♦
    Sep 21, 2018 at 15:36

1 Answer 1

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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.

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    $\begingroup$ On could argue that "just use" should rather be replaced with "don't use": stats.stackexchange.com/questions/20836/… $\endgroup$
    – Tim ♦
    Apr 4, 2017 at 8:17
  • $\begingroup$ 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. $\endgroup$
    – Eli
    Sep 21, 2018 at 15:41
  • $\begingroup$ This algorithm does not explore the "all possible subsets" of models requested in the question. $\endgroup$
    – whuber ♦
    Sep 21, 2018 at 15:42

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