R help - Jun 2012 - Scaling a "density".

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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1. You have not asked a question.

2. Your data set is too small to do anything more with it than show it in a
table as you have done. (IMHO) anything more than that would be wild,
foolish, unsupportable, and misleading "statisticizing" -- by which I
mean
creating the appearance of having more and more precise information than
you actually have by employing complex (if possible) statistical methods. *

-- Bert

* A common practice in many scientific fields these days, I admit. One
would hope that practical arenas like yours would avoid this, however.



On Tue, Jun 19, 2012 at 8:22 AM, Keith Weintraub <kw1958@gmail.com> wrote:
> 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
>
>
>
> --
>
>
>        [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>


-- 

Bert Gunter
Genentech Nonclinical Biostatistics

Internal Contact Info:
Phone: 467-7374
Website:
http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm

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