Difference between revisions of "Beta distributions"

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=Beta distributions=
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For details on the beta distribution see [[WikiPedia:Beta distribution]]. The parameters p, and q  
For details on the beta distribution see [[WikiPedia:Beta distribution]]. Notice that thee distribution is symmetric for the parameters p, and q  
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are α & β in the Wikipedia article formula. The x axis range is from 0 to 1, the range for probabilities
(α & β in the Wikipedia article formula), and that the x axis range is from 0 to 1, a probability.
 
  
==R programming language==
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Beta distributions have many [http://en.wikipedia.org/wiki/Beta_distribution#Shapes shapes] depending on the parameters. Their flexibility makes them useful for modelling
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uniform distributions β(1,1) right through to symmetric or skewed distributions, or even U shaped distributions.
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== R programming language ==
 
For help on functions which create/manipulate beta distributions see;
 
For help on functions which create/manipulate beta distributions see;
<table class=document-code><tr><td>
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<R>
?beta # or help(beta)
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?beta # or help(beta)
?rbeta # or help(rbeta)
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?rbeta # or help(rbeta)
</table>
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</R>
 
Usage examples are provided at;
 
Usage examples are provided at;
<table class=document-code><tr><td>
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<R>
 
  example(beta)
 
  example(beta)
 
  example(rbeta)
 
  example(rbeta)
Line 16: Line 18:
 
  example(pbeta)
 
  example(pbeta)
 
  example(qbeta)
 
  example(qbeta)
</table>
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</R>
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== R code example 1 ==
  
Lets generate a beta distribution using the ''rbeta'' built in R function.  
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Let's generate a beta distribution using the ''rbeta'' built in R function.  
<table class=document-code><tr><td>
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<R>
quartz()
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quartz()
 
  # Alter the parameters
 
  # Alter the parameters
 
  n <- 1000 # Number of observations
 
  n <- 1000 # Number of observations
Line 28: Line 32:
 
  x <- rbeta(n, shape1= p, shape2=q)
 
  x <- rbeta(n, shape1= p, shape2=q)
 
  breaks <- seq(0,1, length=21)
 
  breaks <- seq(0,1, length=21)
  hist(x, breaks=breaks, freq=FALSE)
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  hist(x, breaks=breaks, freq=FALSE, main=paste("p=",p, ", q=", q, sep="")) lines(density(x, adjust=0.4), col="red")
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</R>
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Notice &alpha; and &beta; are less than one, so the plot is U shaped, as described in [[Wikipedia:Beta distribution#Shapes|Wikipedia:Beta distribution#Shapes]].
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== Symmetry of &alpha; &Beta; ==
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==R code example 2==
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<R>
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quartz()
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# Alter the parameters
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n <- 10000 # Number of observations
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for(i in 1:50) {
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p <- q <- i
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x <- rbeta(n, shape1= p, shape2=q)
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breaks <- seq(0,1, length=21)
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hist(x, breaks=breaks, freq=FALSE, main=paste("p=",p, ", q=", q, sep=""))
 
  lines(density(x, adjust=0.4), col="red")
 
  lines(density(x, adjust=0.4), col="red")
</table>
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Sys.sleep(1)
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}
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</R>>
  
==See also==
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== See also ==
 
[[BetaDistributions.R]]
 
[[BetaDistributions.R]]
 
 
[[Category:R]]
 
[[Category:R]]

Latest revision as of 20:04, 11 December 2010

For details on the beta distribution see WikiPedia:Beta distribution. The parameters p, and q are α & β in the Wikipedia article formula. The x axis range is from 0 to 1, the range for probabilities

Beta distributions have many shapes depending on the parameters. Their flexibility makes them useful for modelling uniform distributions β(1,1) right through to symmetric or skewed distributions, or even U shaped distributions.

R programming language

For help on functions which create/manipulate beta distributions see; <R>

?beta # or help(beta)
?rbeta # or help(rbeta)

</R> Usage examples are provided at; <R>

example(beta)
example(rbeta)
example(dbeta)
example(pbeta)
example(qbeta)

</R>

R code example 1

Let's generate a beta distribution using the rbeta built in R function. <R>

quartz()
# Alter the parameters
n <- 1000 # Number of observations
p <- 0.1   
q <- 0.1
x <- rbeta(n, shape1= p, shape2=q)
breaks <- seq(0,1, length=21)
hist(x, breaks=breaks, freq=FALSE, main=paste("p=",p, ", q=", q, sep="")) lines(density(x, adjust=0.4), col="red")

</R> Notice α and β are less than one, so the plot is U shaped, as described in Wikipedia:Beta distribution#Shapes.

Symmetry of α Β

R code example 2

<R>

quartz()
# Alter the parameters
n <- 10000 # Number of observations
for(i in 1:50) {
p <- q <- i
x <- rbeta(n, shape1= p, shape2=q)
breaks <- seq(0,1, length=21)
hist(x, breaks=breaks, freq=FALSE, main=paste("p=",p, ", q=", q, sep=""))
lines(density(x, adjust=0.4), col="red")
Sys.sleep(1)
}

</R>>

See also

BetaDistributions.R