Dave, I am a little confused. You ask about a triangle distribution, but describe the situation as a relation between y and x. Do you really want a piecewise linear fit of y to x? That could be gotten using nonlinear least squares. Dave
From: Jinsong Zhao <[email protected]> To: [email protected] Date: 12/23/2010 07:53 AM Subject: Re: [R] Fitting a Triangular Distribution to Bivariate Data Sent by: [email protected] On 2010-12-23 2:19, David Bapst wrote: > Hello, > I have some xy data which clearly shows a non-monotonic, peaked > triangular trend. You can get an idea of what it looks like with: > > x<-1:20 > y<-c(2*x[1:10]+1,-2*x[11:20]+42) > > I've tried fitting a quadratic, but it just doesn't the data-structure > with the break point adequately. Is there anyway to fit a triangular > or 'tent' function to my data in R? > > Some sample code would be appreciated; I'm not new to R, but I > sometimes have difficulty understanding the model-fitting functions > (finally figured out how to extrapolate with predict() today!) > > Thanks! > -Dave Bapst, UChicago > Hi, you may try the following code: > library(triangle) > library(fitdistrplus) > summary(fitdist(y, "triangle", start = list(a = 1.9, b= 21.1, c = 11.5))) Fitting of the distribution ' triangle ' by maximum likelihood Parameters : estimate Std. Error a -1.400007 2.3523724 b 23.627448 1.9804026 c 13.000000 0.1107073 Loglikelihood: -62.41994 AIC: 130.8399 BIC: 133.8271 Correlation matrix: a b c a 1.00000000 -0.14537297 -0.01203898 b -0.14537297 1.00000000 -0.01439500 c -0.01203898 -0.01439500 1.00000000 HTH ... Jinsong Zhao ______________________________________________ [email protected] 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]] ______________________________________________ [email protected] 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.
