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I have conducted a logistic regression on a binary dependent variable and 5 independent variables. The dataframe I drew these variables from is survey data asking whether a person has voted for or against a policy change (binary dependent variable), with the other variables being questions regarding their income, location and other such personal information that could inform whether they would vote for or against the vote.

Having conducted the regression, I'd now like to calculate the predicted probability that each person would have voted yes/no to see how informative those variables are. In total my dataframe has information on 3000 people and I'd like to calculate the predicted probability of voting for/against for every single row/person.

What methods are available for doing so?

Appreciate the help!

Trier Von
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    When asking for help, you should include a simple [reproducible example](https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) with sample input and desired output that can be used to test and verify possible solutions. What exactly do you mean by "methods" are you looking for different statistical methods? If you need help with analysis recommendations, you should be asking at [stats.se] since that's not really a specific programming question. – MrFlick Apr 20 '18 at 13:42
  • Possible duplicate: https://stackoverflow.com/a/48132409/2372064 – MrFlick Apr 20 '18 at 13:42

1 Answers1

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You can use the predict function in order to calculate the predicted probabilities. 

predict(model, newdata, type="response")

With model our logistic regression (the result of the glm() function), newdata a dataset which contains all the variables defined in our model and for all the individuals for which you want a probability.

Mel Sscn
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