[R] Fitdistr() versus nls()
Luca Telloli
telloli at cs.unibo.it
Sat Sep 23 12:35:10 CEST 2006
Hello R-Users,
I'm new to R so I apologize in advance for any big mistake I might
be doing. I'm trying to fit a set of samples with some probabilistic
curve, and I have an important question to ask; in particular I have
some data, from which I calculate manually the CDF, and then I import
them into R and try to fit: I have the x values (my original samples)
and the y values (P(X<x)).
To attempt the fit I've both fitdistr() and nls(), in the way you
can read in the piece of code at the end of the email. Because the
fit with all data doesn't work very well, I decided to take a subset
of samples randomly chosen (for some random x, the correspondant y is
chosen).
The first big problem is that fitdistr and nls, in the way I use
them in the code, they don't get me similar results. I think I'm
making a mistake but I can't really understand which one.
From this first issue, a second one arises because the plot with nls
is similar (maybe not a great fit bust still...) to my original CDF
while the plot of fitdistr is basically a straight line of constant
value y=1. On the other side, the fitdistr outperforms in the
Kolmogorov-Smirnov test, which for nls has values of probability
around 0 (not a good score huh?).
Can u please tell me if there's a major mistake in the code?
Thanks in advance,
Luca
------ BEGINNING OF CODE
----------------------------------------------------------------
cdf.all=read.table("all_failures.cdf", header=FALSE, col.names=c
("ttr", "cdf"), sep=":" )
allvals.x=array(t(cdf.all[1]))
allvals.y=array(t(cdf.all[2]))
library(MASS)
bestval.exp.nls=bestval.exp.fit=-1
plot(allvals.x, allvals.y)
for(it in 1:100){
#extract random samples
random=sort(sample(1:length(allvals.x), 15))
somevals.x=allvals.x[c(random)]
somevals.y=allvals.y[c(random)]
#fit with nls and fitdistr
fit.exp = fitdistr(somevals.y, "exponential")
nls.exp <- nls(somevals.y ~ pexp(somevals.x, rate), start=list(rate=.
0001), model=TRUE)
#plot what you get out of the two fits
lines(allvals.x, pexp(allvals.x, coef(fit.exp)), col=it)
lines(allvals.x, pexp(allvals.x, coef(nls.exp)), col=it)
#perform kolmogorov-smirnov test on your fit
ks.exp.nls = ks.test(somevals.y, "pexp", coef(nls.exp))
ks.exp.fit = ks.test(somevals.y, "pexp", coef(fit.exp))
bestval.exp.fit = max(bestval.exp.fit, ks.exp.fit$p.value)
bestval.exp.nls = max(bestval.exp.nls, ks.exp.nls$p.value)
}
print(bestval.exp.fit)
print(bestval.exp.nls)
----------END OF
CODE--------------------------------------------------------------------
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