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Eliminating predictors

You technically still do not know which predictors are really required in the model. Again, you use the function stepAIC() from the add-on package MASS to exclude unnecessary predictors. The argument direction = "backward" starts the selection process with the extended.model and sequentially removes terms in an effort to lower the AIC. The argument trace = FALSE suppresses information to be printed during the running of the selection process. You summarize the final model, resulting in the minimum AIC value, by the function summary().

Diese Übung ist Teil des Kurses

Building Response Models in R

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Anleitung zur Übung

  • Perform backward selection of predictors on the extended.model object by using the function stepAIC(). Assign the result to an object named final.model.
  • Summarize the final.model object by using the function summary().

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Backward elemination
final.model <- ___(___, direction = ___, trace = ___)

# Summarize the final.model
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