[R] Trouble understanding the behaviour of stableFit(fBasics)
Ted Byers
r.ted.byers at gmail.com
Wed Sep 24 04:46:23 CEST 2008
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
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