pGrassiaIIBin function

Grassia-II-Binomial Distribution

Grassia-II-Binomial Distribution

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.

PGrassiaIIBin[x]=(nx)j=0x(xj)(1)xj(1+b(nj))a P_{GrassiaIIBin}[x]= {n \choose x} \sum_{j=0}^{x} {x \choose j} (-1)^{x-j} (1+b(n-j))^{-a} a,b>0 a,b > 0 x=0,1,2,...,n x = 0,1,2,...,n n=1,2,3,... n = 1,2,3,...

The mean, variance and over dispersion are denoted as

EGrassiaIIBin[x]=(bb+1)a E_{GrassiaIIBin}[x] = (\frac{b}{b+1})^a VarGrassiaIIBin[x]=n2[(bb+2)a(bb+1)2a]+n(bb+1)a1(b+1b+2)a Var_{GrassiaIIBin}[x] = n^2[(\frac{b}{b+2})^a - (\frac{b}{b+1})^{2a}] + n(\frac{b}{b+1})^a{1-(\frac{b+1}{b+2})^a} overdispersion=(bb+2)a(bb+1)2a(bb+1)a[1(bb+1)a] over dispersion= \frac{(\frac{b}{b+2})^a - (\frac{b}{b+1})^{2a}}{(\frac{b}{b+1})^a[1-(\frac{b}{b+1})^a]}

Examples

#plotting the random variables and probability values col <- 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 in 1:5) { 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)$pdf #extracting the pdf values dGrassiaIIBin(0:10,10,4,.2)$mean #extracting the mean dGrassiaIIBin(0:10,10,4,.2)$var #extracting the variance dGrassiaIIBin(0:10,10,4,.2)$over.dis.para #extracting the over dispersion value #plotting the random variables and cumulative probability values col <- rainbow(4) a <- c(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 in 1:4) { 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

References

\insertRef grassia1977familyfitODBOD