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Practical Predictive Analytics by Ralph Winters

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Computing baseline estimates

We will first determine the baseline (or average) estimates for the regression model we have just run, using the basehaz() function. We will assign it to an object named base, and then we will print and plot it.

#Let's start by looking at the baseline estimates for each time period.#base <- basehaz(CoxModel.1)print(base)> print(base)        hazard time1  0.007174321    12  0.033151848    23  0.051088747    34  0.062057960    45  0.084305023    56  0.109773610    67  0.149493300    78  0.200891166    89  0.281904190    910 0.420681696   1011 0.623702585   1112 1.281690658   12

Recall that the hazard is the likelihood that an event (churn) will happen, given that it hasn't already happened. This terminology is slightly different from the term survival rate, ...

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