Thank you, Ben. The beta distribution seems flexible enough. I knew this distribution but had never seen it in this kind of application, and somehow did not recall it. rbeta(n, shape1 = 5, shape2 = 1) looks reasonable to start with for my simple task. If I had a real dataset I could parameterize it with a standard method.
Regards, Diego Ben Bolker wrote: > > diegol <diegol81 <at> gmail.com> writes: > >> >> >> R version: 2.7.0 >> Running on: WinXP >> >> I am trying to model damage from fire losses (given that the loss >> occurred). >> Since I have the individual insured amounts, rather than sampling dollar >> damage from a continuous distribution ranging from 0 to infinity, I want >> to >> sample from a percent damage distribution from 0-100%. One obvious >> solution >> is to use runif(n, min=0, max=1), but this does not seem to be a good >> idea, >> since I would not expect damage to be uniform. >> > > > Beta distribution (rbeta(...)) or > logistic-binomial distribution > plogis(rnorm(...)) . > > See e.g. > > Smithson, Michael, and Jay Verkuilen. 2006. A better lemon squeezer? > Maximum-likelihood regression with beta-distributed dependent variables. > Psychological Methods 11, no. 1 (March): 54-71. doi:2006-03820-004. > > ______________________________________________ > 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. > > ----- ~~~~~~~~~~~~~~~~~~~~~~~~~~ Diego Mazzeo Actuarial Science Student Facultad de Ciencias Económicas Universidad de Buenos Aires Buenos Aires, Argentina -- View this message in context: http://www.nabble.com/Percent-damage-distribution-tp21170344p21170996.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
