These functions provide the ability for generating probability function values and cumulative probability function values for the Grassia-II-Binomial Distribution.
pGrassiaIIBin(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 a.
b: single value for shape parameter b.
Returns
The output of pGrassiaIIBin gives cumulative probability values in vector form.
Details
Mixing Gamma distribution with Binomial distribution will create the the Grassia-II-Binomial distribution, only when (1-p)=e^(-lambda) of the 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(0.3,0.4,0.5,0.6,0.8)plot(0,0,main="Grassia II 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,dGrassiaIIBin(0:10,10,2*a[i],a[i])$pdf,col = col[i],lwd=2.85)points(0:10,dGrassiaIIBin(0:10,10,2*a[i],a[i])$pdf,col = col[i],pch=16)}dGrassiaIIBin(0:10,10,4,.2)$pdf #extracting the pdf valuesdGrassiaIIBin(0:10,10,4,.2)$mean #extracting the meandGrassiaIIBin(0:10,10,4,.2)$var #extracting the variancedGrassiaIIBin(0:10,10,4,.2)$over.dis.para #extracting the over dispersion value#plotting the random variables and cumulative probability valuescol <- rainbow(4)a <- c(0.3,0.4,0.5,0.6)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,pGrassiaIIBin(0:10,10,2*a[i],a[i]),col = col[i])points(0:10,pGrassiaIIBin(0:10,10,2*a[i],a[i]),col = col[i])}pGrassiaIIBin(0:10,10,4,.2)#acquiring the cumulative probability values