RE: [R] density estimation: compute sum(value * probability) for given distribution

2004-11-13 Thread Liaw, Andy
First thing you probably should realize is that density is _not_
probability.  A probability density function _integrates_ to one, not _sum_
to one.  If X is an absolutely continuous RV with density f, then Pr(X=x)=0
for all x, and Pr(a  X  b) = \int_a^b f(x) dx.

sum x*Pr(X=x) (over all possible values of x) for a discrete distribution is
just the expectation, or mean, of the distribution.  The expectation for a
continuous distribution is \int x f(x) dx, where the integral is over the
support of f.  This is all elementary math stat that you can find in any
textbook.

Could you tell us exactly what you are trying to compute, or why you're
computing it?

HTH,
Andy

 From: bogdan romocea
 
 Dear R users,
 
 This is a KDE beginner's question. 
 I have this distribution:
  length(cap)
 [1] 200
  summary(cap)
Min. 1st Qu.  MedianMean 3rd Qu.Max. 
   459.9   802.3   991.6  1066.0  1242.0  2382.0 
 I need to compute the sum of the values times their probability of
 occurence.
 
 The graph is fine,
 den - density(cap, from=min(cap), 
to=max(cap), give.Rkern=F)
 plot(den)
 
 However, how do I compute sum(values*probabilities)? The
 probabilities produced by the density function sum to only 26%: 
  sum(den$y)
 [1] 0.2611142
 
 Would it perhaps be ok to simply do
  sum(den$x*den$y) * (1/sum(den$y))
 [1] 1073.22
 ?
 
 Thank you,
 b.
 
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RE: [R] density estimation: compute sum(value * probability) for given distribution

2004-11-13 Thread bogdan romocea
Andy,

Thanks a lot for the clarifications. I was running a simulation a
number of times and trying to come up with a number to summarize the
results. And, I failed to realize from the beginning that what I was
trying to compute was just the mean.

Regards,
b.


--- Liaw, Andy [EMAIL PROTECTED] wrote:

 First thing you probably should realize is that density is _not_
 probability.  A probability density function _integrates_ to one,
 not _sum_
 to one.  If X is an absolutely continuous RV with density f, then
 Pr(X=x)=0
 for all x, and Pr(a  X  b) = \int_a^b f(x) dx.
 
 sum x*Pr(X=x) (over all possible values of x) for a discrete
 distribution is
 just the expectation, or mean, of the distribution.  The
 expectation for a
 continuous distribution is \int x f(x) dx, where the integral is
 over the
 support of f.  This is all elementary math stat that you can find
 in any
 textbook.
 
 Could you tell us exactly what you are trying to compute, or why
 you're
 computing it?
 
 HTH,
 Andy
 
  From: bogdan romocea
  
  Dear R users,
  
  This is a KDE beginner's question. 
  I have this distribution:
   length(cap)
  [1] 200
   summary(cap)
 Min. 1st Qu.  MedianMean 3rd Qu.Max. 
459.9   802.3   991.6  1066.0  1242.0  2382.0 
  I need to compute the sum of the values times their probability
 of
  occurence.
  
  The graph is fine,
  den - density(cap, from=min(cap), 
 to=max(cap), give.Rkern=F)
  plot(den)
  
  However, how do I compute sum(values*probabilities)? The
  probabilities produced by the density function sum to only 26%: 
   sum(den$y)
  [1] 0.2611142
  
  Would it perhaps be ok to simply do
   sum(den$x*den$y) * (1/sum(den$y))
  [1] 1073.22
  ?
  
  Thank you,
  b.
  
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  [EMAIL PROTECTED] mailing list
  https://stat.ethz.ch/mailman/listinfo/r-help
  PLEASE do read the posting guide! 
  http://www.R-project.org/posting-guide.html
  
  
 
 

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Re: [R] density estimation: compute sum(value * probability) for given distribution

2004-11-13 Thread Uwe Ligges
bogdan romocea wrote:
Dear R users,
This is a KDE beginner's question. 
I have this distribution:

length(cap)
[1] 200
summary(cap)
   Min. 1st Qu.  MedianMean 3rd Qu.Max. 
  459.9   802.3   991.6  1066.0  1242.0  2382.0 
I need to compute the sum of the values times their probability of
occurence.

The graph is fine,
den - density(cap, from=min(cap), 
   to=max(cap), give.Rkern=F)
plot(den)

However, how do I compute sum(values*probabilities)? 
I don't get the point. You are estimating using a gaussian kernel.
Hint: What's the probability to get x=0 for a N(0,1) distribution?
So sum(values*probabilities) is zero!
 The
probabilities produced by the density function sum to only 26%: 
and could also sum to, e.g., 783453.9, depending on the number of 
observations and the estimated parameters of the desnity ...

sum(den$y)
[1] 0.2611142
Would it perhaps be ok to simply do
sum(den$x*den$y) * (1/sum(den$y))
[1] 1073.22
?
No. den$x is a point where the density function is equal to den$y, but 
den$y is not the probability to get den$x (you know, the stuff with 
intervals)! I fear you are mixing theory from discrete with continuous 
distributions.

Uwe Ligges

Thank you,
b.
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[R] density estimation: compute sum(value * probability) for given distribution

2004-11-12 Thread bogdan romocea
Dear R users,

This is a KDE beginner's question. 
I have this distribution:
 length(cap)
[1] 200
 summary(cap)
   Min. 1st Qu.  MedianMean 3rd Qu.Max. 
  459.9   802.3   991.6  1066.0  1242.0  2382.0 
I need to compute the sum of the values times their probability of
occurence.

The graph is fine,
den - density(cap, from=min(cap), 
   to=max(cap), give.Rkern=F)
plot(den)

However, how do I compute sum(values*probabilities)? The
probabilities produced by the density function sum to only 26%: 
 sum(den$y)
[1] 0.2611142

Would it perhaps be ok to simply do
 sum(den$x*den$y) * (1/sum(den$y))
[1] 1073.22
?

Thank you,
b.

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[EMAIL PROTECTED] mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
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