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.
#plotting the random variables and probability valuescol <- 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 in1:5){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)$pdf #extracting the pdf valuesdBetaBin(0:10,10,4,.2)$mean #extracting the meandBetaBin(0:10,10,4,.2)$var #extracting the variancedBetaBin(0:10,10,4,.2)$over.dis.para #extracting the over dispersion value#plotting the random variables and cumulative probability valuescol <- 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 in1:4){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