.ldf_ and .cdf_ and their column labels

[kennethshsu]

genins = cl.load_sample("genins")
dev = cl.Development().fit(genins)
dev.ldf_
dev.cdf_

In the .ldf_ call, the last column is titled 108-120, whereas in the .cdf_ call, the last column is titled 108-ult instead. Is this intended?

[kennethshsu]

I think it makes sense, but wanted to make sure that the .cdf_ tails the tail into consideration, whereas .ldf_ does not?

[jbogaardt]

This is the intended behavior. On the cdf_, all labels are Age-Ult, e.g. 12-Ult, 24-Ult, etc. to denote that they are the cumulative of the available ldf_.

At this point in the example, no tail estimator has been fitted (and they are optional). Without a tail estimator, it is assumed that there is no tail (or more technically a tail = 1.0). If you do fit a tail, it will show up in the cdf_ and ldf_ vectors in the ways you'd expect.

Fun fact - you can to dev.ldf_.incr_to_cum() to calculate the cdf_ as well. The incr_to_cum method recognizes that the ldf_ triangle are a set of patterns.

[kennethshsu]

Ahh yes, I think I read it wrong the first time.

And what a neat feature with dev.ldf_.incr_to_cum(), I'll add that to the tutorial.