Hi:
Thanks for your answer.
Do you know how to test whether the data would fit to a gamma-distribution?
How can I call fBasics?
Note: I installed R-language on my Macintosh today; I have used the binary -- pre compiled -- package.
Some of the R-help facilties do not function on my Mac.
Again to my data: How can I compute the skew? I think I lack some basic packages - right?
The curious things actually is that the median and the mean are quite similar, e.g. 0.19 and 0.2 respectively; the skew is about 1.0 (I calculated the skew by my own computer code in Bigloo).
The problem actually is: my boss expects from me that I make some tests; personally I am a bit generous and everything is a Gaussian or log-Gaussian distribution, because how can I be sure that the underlying data to not have any serious flaws? Statistics is black art - right?
Regards, S. Gonzi
Vito Ricci wrote:
Hi,
from what you're writing: "The logaritmic transformation "shapiro.test(log10(y))" says: W=0.9773, p-value= 2.512e-05." it seems the log-values are not distributed normally and so original data are not distributed like a log-normal: the p-value is extremally small!
Other tests for normality are available in package: nortest
compare the log-transformation of your ecdf with normal cdf: see ? ecdf
use qqnorm and qqplot
did you calculate skewness and kurtosis? see in package fBasics.
I remember to you that the log-normal distribution as three parameters: shape parameter, location parameter and scale parameter. Transfroming by the simple log, you are missing the location parameter, or implicitely you assuming is =0.
See: http://www.itl.nist.gov/div898/handbook/eda/section3/eda3669.htm for more news about log-normal distribution.
I hope I give you a little help. Best Vito
you wrote:
Hello:
Yes I know that sort of questions comes up quite
often. But with all due respect I din't find how to perform what I want. I am
searching archives and bowsing manuals but it isn't there, though, it is
a ridiculous simple task for the experienced R user.
I have data and can do the following with them:
== hist(y, prob=TRUE) lines(density(y,bw=0.03) ==
The result actually is a nice histogram superimposed by a line plot.
The histogram is a bit skewed to the left. My
assumption actually is that a log-normal transformation would cure the
problem. But how the hell can one plot such a density function or Gaussian
function which has logarithmic scales on x axis.
For example I tried:
== plot(hist(y),log="x")
or
plot(hist(log10(y)),log="x") ==
But with no avail. I want my axis like: 1,10,100
What would be other methods to test whether the data
are logaritmically distributed.
A last question to the Shapiro-Wilk test. Were can I
get critical parameters? I mean I get for my distribution:
W=0.9686, p-value=6.887e-07.
What does that mean? Yes I have got some books about
statics, but none of them says what one should do with the values then.
The logaritmic transformation "shapiro.test(log10(y))" says:
W=0.9773, p-value= 2.512e-05.
Sorry for disturbing you. Although, it is really no
homework. I need it for my Phd in physics; after a lengthy computation on
the computer I would like to go to see whether the outputs are
log-normal or normal distributed.
Regards, Siegfried Gonzi == University of Graz Institute for Physics Tel.: ++43-316-380-8620
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