R: log-normal distribution and shapiro test

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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