I'm trying to use the
step() function in R for variable selection of a linear model. My model looks like this:
#I have some code first that randomly samples part of iris. step_mod <- step(glm(Species~., iris_sample, family = binomial), direction = "both")
step() function minimizes AIC as the way of finding the best combination of variables. However, I care about the % correct for a certain species (also called the "sensitivity") of the final predictions a lot more than I do about the AIC.
Is there a way to change the method of selection to "sensitivity" in the
step() function? Or is there a different function/package that you can use where you can change the selection criterion?
Here's the code I'm using to make predictions after I get the model, since it might helpful:
probs <- predict(step_mod, newdata = iris_unused, type="response") class_predictions <- ifelse(probs < 0.50, "setosa", "versicolor") final_check <- cbind(iris_unused, class_predictions) tablecheck <- confusionMatrix(data = as.factor(final_check$class_predictions), reference = as.factor(final_check$Species), prevalence = 2)
In the resulting confusion matrix, the proportion of setosa correct is the number I'd like to maximize when doing model selection.