Beta-Correlated Binomial Distribution

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

pBetaCorrBin(x,n,cov,a,b)

Arguments

x	vector of binomial random variables.
n	single value for no of binomial trials.
cov	single value for covariance.
a	single value for alpha parameter
b	single value for beta parameter.

Value

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

Details

The probability function and cumulative function can be constructed and are denoted below

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

$$x = 0,1,2,3,...n$$ $$n = 1,2,3,...$$ $$-\infty < cov < +\infty $$ $$0< a,b$$ $$0 < p < 1$$

$$p=\frac{a}{a+b}$$ $$\Theta=\frac{1}{a+b}$$

The Correlation is in between $$\frac{-2}{n(n-1)} min(\frac{p}{1-p},\frac{1-p}{p}) \le correlation \le \frac{2p(1-p)}{(n-1)p(1-p)+0.25-fo} $$ where \(fo=min (x-(n-1)p-0.5)^2 \)

The mean and the variance are denoted as $$E_{BetaCorrBin}[x]= np$$ $$Var_{BetaCorrBin}[x]= np(1-p)(n\Theta+1)(1+\Theta)^{-1}+n(n-1)cov$$ $$Corr_{BetaCorrBin}[x]=\frac{cov}{p(1-p)}$$

NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.

References

Paul, S.R., 1985. A three-parameter generalization of the binomial distribution. Communications in Statistics - Theory and Methods, 14(6), pp.1497-1506.

Available at: http://www.tandfonline.com/doi/abs/10.1080/03610928508828990.

Examples

#plotting the random variables and probability values
col <- rainbow(5)
a <- c(9.0,10,11,12,13)
b <- c(8.0,8.1,8.2,8.3,8.4)
plot(0,0,main="Beta-Correlated 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,dBetaCorrBin(0:10,10,0.001,a[i],b[i])$pdf,col = col[i],lwd=2.85)
points(0:10,dBetaCorrBin(0:10,10,0.001,a[i],b[i])$pdf,col = col[i],pch=16)
}

dBetaCorrBin(0:10,10,0.001,10,13)$pdf      #extracting the pdf values
#>  [1] 0.010992133 0.047498928 0.108466364 0.170474118 0.202669472 0.189491674
#>  [7] 0.140699312 0.081828892 0.035693121 0.010567136 0.001618852
dBetaCorrBin(0:10,10,0.001,10,13)$mean     #extracting the mean
#> [1] 4.347826
dBetaCorrBin(0:10,10,0.001,10,13)$var      #extracting the variance
#> [1] 3.469017
dBetaCorrBin(0:10,10,0.001,10,13)$corr     #extracting the correlation
#> [1] 0.004069231
dBetaCorrBin(0:10,10,0.001,10,13)$mincorr  #extracting the minimum correlation value
#> [1] -0.01709402
dBetaCorrBin(0:10,10,0.001,10,13)$maxcorr  #extracting the maximum correlation value
#> [1] 0.2145215

#plotting the random variables and cumulative probability values
col <- rainbow(5)
a <- c(9.0,10,11,12,13)
b <- c(8.0,8.1,8.2,8.3,8.4)
plot(0,0,main="Beta-Correlated binomial probability function graph",xlab="Binomial random variable",
ylab="Probability function values",xlim = c(0,10),ylim = c(0,1))
for (i in 1:5)
{
lines(0:10,pBetaCorrBin(0:10,10,0.001,a[i],b[i]),col = col[i],lwd=2.85)
points(0:10,pBetaCorrBin(0:10,10,0.001,a[i],b[i]),col = col[i],pch=16)
}

pBetaCorrBin(0:10,10,0.001,10,13)      #acquiring the cumulative probability values
#>  [1] 0.01099213 0.05849106 0.16695742 0.33743154 0.54010101 0.72959269
#>  [7] 0.87029200 0.95212089 0.98781401 0.99838115 1.00000000