That's a great help. Thanks so much. KW --
On Jun 19, 2012, at 2:31 PM, David L Carlson wrote: > Is this what you are looking for? > > newrec <- rep(recoveries[,1], recoveries[,2]) > plot(density(newrec), ylim=c(0, 5)) > lines(density(newrec*.67), col="red") > plot(ecdf(newrec), xlim=c(0,1), verticals=TRUE) > lines(ecdf(newrec*.67), verticals=TRUE, col="red") > > ---------------------------------------------- > David L Carlson > Associate Professor of Anthropology > Texas A&M University > College Station, TX 77843-4352 > >> -----Original Message----- >> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- >> project.org] On Behalf Of Keith Weintraub >> Sent: Tuesday, June 19, 2012 11:00 AM >> To: Bert Gunter >> Cc: r-help@r-project.org >> Subject: Re: [R] Scaling a "density". >> >> 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 >>> >>> >> >> >> [[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. > [[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.
