Building a stepwise regression model

In the absence of subject-matter expertise, stepwise regression can assist with the search for the most important predictors of the outcome of interest.

In this exercise, you will use a forward stepwise approach to add predictors to the model one-by-one until no additional benefit is seen. The donors dataset has been loaded for you.

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Supervised Learning in R: Classification

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Övningsinstruktioner

Use the R formula interface with glm() to specify the base model with no predictors. Set the explanatory variable equal to 1.
Use the R formula interface again with glm() to specify the model with all predictors.
Apply step() to these models to perform forward stepwise regression. Set the first argument to null_model and set direction = "forward". This might take a while (up to 10 or 15 seconds) as your computer has to fit quite a few different models to perform stepwise selection.
Create a vector of predicted probabilities using the predict() function.
Plot the ROC curve with roc() and plot() and compute the AUC of the stepwise model with auc().

Interaktiv övning med praktiskt arbete

Testa den här övningen genom att slutföra den här exempelkoden.

# Specify a null model with no predictors
null_model <- ___(___, data = ___, family = "___")

# Specify the full model using all of the potential predictors
full_model <- ___

# Use a forward stepwise algorithm to build a parsimonious model
step_model <- step(___, scope = list(lower = null_model, upper = full_model), direction = "___")

# Estimate the stepwise donation probability
step_prob <- ___

# Plot the ROC of the stepwise model
library(pROC)
ROC <- ___
plot(___, col = "red")
auc(___)

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