Kaplan-Meier Analysis

In this exercise you are going to practice Kaplan-Meier Analysis - without and with a categorical covariate.

The survival package is loaded to your workspace. Also, the survival object survObj and your data dataNextOrder are still in the environment. But now, the data contains an additional covariate called voucher, which you will need in this exercise. This categorical variable tells you if the customer used a voucher in her first order. It contains the value 0 or 1.

This exercise is part of the course

Machine Learning for Marketing Analytics in R

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Exercise instructions

Compute a Kaplan-Meier Analysis (without covariates) using survfit(). Store the result in an object called fitKMSimple. Remember, the dependent variable (variable to the left of the tilde ~) is again your survival object survObj. Then, print fitKMSimple.
Plot the result object fitKMSimple and add axis labels (xlab and ylab arguments).
Now go a step further: Compute a Kaplan-Meier Analysis with the survObj as dependent variable and the variable voucher as covariate. Don't forget to specify the data argument.
Again, plot the result of the new model and add axis labels.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Compute and print fit
fitKMSimple <- ___(___ ~ ___)
___(fitKMSimple)

# Plot fit
plot(___,
     conf.int = FALSE, ___ = "Time since first purchase", ___ = "Survival function", main = "Survival function")

# Compute fit with categorical covariate
fitKMCov <- survfit(___ ~ ___, data = ___)

# Plot fit with covariate and add labels
plot(___, lty = 2:3,
     ___ = "Time since first purchase", ___ = "Survival function", main = "Survival function")
legend(90, .9, c("No", "Yes"), lty = 2:3)

Edit and Run Code