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 ==
log-normal distribution and shapiro test
1 message · Siegfried Gonzi