Model specification and estimation

You have seen the glm() command for running a logistic regression. glm() stands for generalized linear model and offers a whole family of regression models.

Take the exercise dataset for this coding task. The data defaultData you need for this exercise is available in your environment and ready for modeling.

Deze oefening maakt deel uit van de cursus

Machine Learning for Marketing Analytics in R

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Oefeninstructies

Use the glm() function in order to model the probability that a customer will default on his payment by using a logistic regression. Include every explanatory variable of the dataset and specify the data that shall be used.
Do not forget to specify the argument family.
Extract the coefficients from the model, then transform them to the odds ratios and round.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Build logistic regression model
logitModelFull <- ___(PaymentDefault ~ limitBal + sex + education + marriage +
                   age + pay1 + pay2 + pay3 + pay4 + pay5 + pay6 + billAmt1 + 
                   billAmt2 + billAmt3 + billAmt4 + billAmt5 + billAmt6 + payAmt1 + 
                   payAmt2 + payAmt3 + payAmt4 + payAmt5 + payAmt6, 
                family = ___, data = ___)

# Take a look at the model
___(logitModelFull)

# Take a look at the odds ratios
coefsexp <- ___(logitModelFull) %>% ___ %>% round(2)
coefsexp

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