Folks,
I have a small dataset of counts of recoveries on defaulted loans:
recoveries<-structure(c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1,
0, 0, 0, 0, 0, 4, 0, 1, 2, 2, 12), .Dim = c(11L, 2L), .Dimnames = list(
NULL, c("pcts", "counts")))
Here is the data in columnar form:
pcts counts
[1,] 0.0 0
[2,] 0.1 0
[3,] 0.2 0
[4,] 0.3 0
[5,] 0.4 0
[6,] 0.5 4
[7,] 0.6 0
[8,] 0.7 1
[9,] 0.8 2
[10,] 0.9 2
[11,] 1.0 12
For example row [6,] means that in our historical sample we saw 50% recoveries
4 times.
Now I would like to "stress" the recovery distribution by say 67% so that the
counts would stay the same but the bins (pcts) would contract like so:
> recoveries*matrix(c(.67,1),nrow = 11, ncol = 2, byrow = TRUE)
pcts counts
[1,] 0.000 0
[2,] 0.067 0
[3,] 0.134 0
[4,] 0.201 0
[5,] 0.268 0
[6,] 0.335 4
[7,] 0.402 0
[8,] 0.469 1
[9,] 0.536 2
[10,] 0.603 2
[11,] 0.670 12
I would like to plot this using densityplot or an equivalent but on the
original scale from 0.0 to 1.0.
In addition I would like to either "integrate" the density plot or come up with
a smooth version of the CDF after the 67% contraction.
I hope this is clear,
Thanks for your time,
KW
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