Bert,
Thanks for your help and comments.
My inferior writing skills have failed to elucidate what I thought were
implicit questions in the following:
I would like to plot this:
> 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
using densityplot or an equivalent but on the original scale with pcts going
from 0.0 to 1.0. Can someone give me an example or pointer to complete this
task?
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. How would I go
about doing this?
In regards to your second point we are not looking to draw deep inferences,
just a reasonable looking graph for a presentation to go along with the data
table. Some folks just like to see pretty pictures.
Thanks so much for your time,
KW
--
On Jun 19, 2012, at 11:37 AM, Bert Gunter wrote:
> 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 <kw1...@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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