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. I have not seen such a distribution in actuarial applications, and rather than inventing one from scratch I thought I'd ask you if you know one, maybe from other disciplines, readily available in R. Thank you in advance. ----- ~~~~~~~~~~~~~~~~~~~~~~~~~~ Diego Mazzeo Actuarial Science Student Facultad de Ciencias Econ?micas Universidad de Buenos Aires Buenos Aires, Argentina -- View this message in context: http://www.nabble.com/Percent-damage-distribution-tp21170344p21170344.html Sent from the R help mailing list archive at Nabble.com.
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.
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 at 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 Econ?micas 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.
Not an R question as yet ..... In my limited experience (we have some insurance projets), 100% can occur, but otherwise a beta distbribution may suit, which suggests a mixture distribution. But start with an empirical examination (histogram, ecdf, density plot) of the distribution, since it may reveal other features. The next question is 'why model'? For such a simple problem (a univariate distribution) a plot may be a sufficent analysis, and for e.g. simulation you could just re-sample the data. On Thu, 25 Dec 2008, diegol wrote:> > 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. > > I have not seen such a distribution in actuarial applications, and rather > than inventing one from scratch I thought I'd ask you if you know one, maybe > from other disciplines, readily available in R. > > Thank you in advance. > > ----- > ~~~~~~~~~~~~~~~~~~~~~~~~~~ > Diego Mazzeo > Actuarial Science Student > Facultad de Ciencias Econ?micas > Universidad de Buenos Aires > Buenos Aires, Argentina > -- > View this message in context: http://www.nabble.com/Percent-damage-distribution-tp21170344p21170344.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help at 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. >-- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595