On Apr 16, 2010, at 3:39 PM, Atte Tenkanen wrote:
Hi,
In fact, my original intention is to show that the measurings of the
musical data are not random. Here I have a measurement from a
composition.
http://www.ag.fimug.fi/~Atte/Comp.pdf
and here one random composition which I have used, among many
others, in order to produce that 'Distribution'.
http://www.ag.fimug.fi/~Atte/RandomComp.pdf
All the values are averaged over the bars. That's why the curves are
so smooth.
Is there any way to find such boundaries?
You can use density estimation if you are willing to ignore the
sequence information in your data, as it appears you desire at this
point. (I suggested offlist that you use a time series framework.)
?density
?denityplot
Also look into the quantile function. After calculating 5% and 95%
quantiles, you could offer the results to the abline function.
--
Atte Tenkanen
----- Original Message -----
From: Christos Argyropoulos <argch...@hotmail.com>
So ..
are you trying to figure out whether your data hasa substantial
number
of outliers that call into question the adequacy of the normal distro
fro your data?
If this is the case, note that you cannot individually check the
values (as you are doing) without taking into account of the
"Bonferoni" fallacy i.e. small p-values will be found with a
respectable frequency as the size of the dataset grows (C Robert
discusses this in a preprint in arxiv see
http://arxiv.org/PS_cache/arxiv/pdf/1002/1002.2080v1.pdf ) So even
though you could check each individual point for normality, testing
the whole dataset requires that you apply a Bonferoni correction to
your z.tests or use outlier.test from package "car" to reduce the
amount of code you have to write.
Regards,
Christos
Date: Fri, 16 Apr 2010 19:11:19 +0300
From: atte...@utu.fi
To: r-help@r-project.org
Subject: [R] Is it ok to apply the z.test this way?
Dear R-users,
I want to check if certain values are from random distribution, that
includes values between 0-1. So, it is not really normal even though
shapiro.test says it is highly normal... Can I do something like this
and think that the values given are right. z.test is from package
TeachingDemos.
-------------------------------------------------------------------------------
SelectedVals=c()
for(i in seq(0,1,by=0.001))
{
if((z.test(i, mu=mean(Distribution),
stdev=sd(Distribution))$p.value)<=0.05)
SelectedVals=c(SelectedVals,i)
}
-------------------------------------------------------------------------------
I have marked the border values given by this script to the
histogram of the original random distribution:
http://www.ag.fimug.fi/~Atte/62Hist100410.pdf
David Winsemius, MD
West Hartford, CT
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