pBetaBin function

Beta-Binomial Distribution

Beta-Binomial Distribution

These functions provide the ability for generating probability function values and cumulative probability function values for the Beta-Binomial Distribution.

pBetaBin(x,n,a,b)

Arguments

  • x: vector of binomial random variables.
  • n: single value for no of binomial trials.
  • a: single value for shape parameter alpha representing as a.
  • b: single value for shape parameter beta representing as b.

Returns

The output of pBetaBin gives cumulative probability values in vector form.

Details

Mixing Beta distribution with Binomial distribution will create the Beta-Binomial distribution. The probability function and cumulative probability function can be constructed and are denoted below.

The cumulative probability function is the summation of probability function values.

PBetaBin(x)=(nx)B(a+x,n+bx)B(a,b) P_{BetaBin}(x)= {n \choose x} \frac{B(a+x,n+b-x)}{B(a,b)} a,b>0 a,b > 0 x=0,1,2,3,...n x = 0,1,2,3,...n n=1,2,3,... n = 1,2,3,...

The mean, variance and over dispersion are denoted as

EBetaBin[x]=naa+b E_{BetaBin}[x]= \frac{na}{a+b} VarBetaBin[x]=(nab)(a+b)2(a+b+n)(a+b+1) Var_{BetaBin}[x]= \frac{(nab)}{(a+b)^2} \frac{(a+b+n)}{(a+b+1)} overdispersion=1a+b+1 over dispersion= \frac{1}{a+b+1}

Defined as B(a,b) is the beta function.

Examples

#plotting the random variables and probability values col <- rainbow(5) a <- c(1,2,5,10,0.2) plot(0,0,main="Beta-binomial probability function graph",xlab="Binomial random variable", ylab="Probability function values",xlim = c(0,10),ylim = c(0,0.5)) for (i in 1:5) { lines(0:10,dBetaBin(0:10,10,a[i],a[i])$pdf,col = col[i],lwd=2.85) points(0:10,dBetaBin(0:10,10,a[i],a[i])$pdf,col = col[i],pch=16) } dBetaBin(0:10,10,4,.2)$pdf #extracting the pdf values dBetaBin(0:10,10,4,.2)$mean #extracting the mean dBetaBin(0:10,10,4,.2)$var #extracting the variance dBetaBin(0:10,10,4,.2)$over.dis.para #extracting the over dispersion value #plotting the random variables and cumulative probability values col <- rainbow(4) a <- c(1,2,5,10) plot(0,0,main="Cumulative probability function graph",xlab="Binomial random variable", ylab="Cumulative probability function values",xlim = c(0,10),ylim = c(0,1)) for (i in 1:4) { lines(0:10,pBetaBin(0:10,10,a[i],a[i]),col = col[i]) points(0:10,pBetaBin(0:10,10,a[i],a[i]),col = col[i]) } pBetaBin(0:10,10,4,.2) #acquiring the cumulative probability values

References

\insertRef young2008poolingfitODBOD

\insertRef trenkler1996continuousfitODBOD

\insertRef hughes1993usingfitODBOD