Can anyone explain such different output: > stableFit(s,alpha = 1.75, beta = 0, gamma = 1, delta = 0, + type = c("q", "mle"), doplot = TRUE, trace = FALSE, title = NULL, + description = NULL)
Title: Stable Parameter Estimation Call: .qStableFit(x = x, doplot = doplot, title = title, description = description) Model: Student-t Distribution Estimated Parameter(s): alpha beta gamma delta 1.5340000 0.2750000 0.3211991 -0.9922306 Description: Tue Sep 23 22:18:44 2008 by user: Ted > refdata18 = read.csv("C:\\MerchantData\\RiskModel\\Capture.Week.18.csv", > na.strings="") > stableFit(refdata18[,1],alpha = 1.75, beta = 0, gamma = 1, delta = 0, + type = c("q", "mle"), doplot = TRUE, trace = FALSE, title = NULL, + description = NULL) Title: Stable Parameter Estimation Call: .qStableFit(x = x, doplot = doplot, title = title, description = description) Model: Student-t Distribution Estimated Parameter(s): alpha beta gamma delta NA NA NA NA Description: Tue Sep 23 22:20:23 2008 by user: Ted > I am just playing with it right now, trying to understand how to call it, so first I passed the s vector from the example. I don't care about the result except to know that stableFit accepted the input and obtained an estimate for the parameters. The I tried my data (a vector in integers, with a distribution that looks similar to poisson, but exponential and geometric give better fits). What I find puzzling is that I get no error messages complaining about one property or another of my data, to explain why there are no parameter estimates. The data I WILL be applying this to comes from the financial markets, and will be reals or floating point numbers that in some cases wil be best modelled by a normal distribution while in most cases, the distribution will be closer to cauchy. (but DistributionFits(fBasics) makes no explicit mention of cauchy, but IIRC cauchy is a special case of a stable distribution one of a family - are these the L-stable distributions Mandelbrot discussed, or something else - correct me if my memory has failed me sooner than anticipated ;-) An URL for a website discussing these in some detail would be handy as my stats texts, dated as they are and focussed more on applied biometrics, don't talk about these. What do I look at if this function just gives me a bunch of 'NA's instead of parameter estimates? And, givent he structure of the documentation, it is not clear if I can get an estimate of skewness for all the distributions or for all except t and normal distributions if I am using DistributionFits. Thanks Ted -- View this message in context: http://www.nabble.com/Trouble-understanding-the-behaviour-of-stableFit%28fBasics%29-tp19640972p19640972.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.