dTriBin {fitODBOD}

Triangular Binomial Distribution

Description

These functions provide the ability for generating probability function values and cumulative probability function values for the Triangular Binomial distribution.

Usage

dTriBin(x,n,mode)

Arguments

x

vector of binomial random variables.

n

single value for no of binomial trials.

mode

single value for mode.

Details

Mixing unit bounded Triangular distribution with Binomial distribution will create Triangular 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.

P_{TriBin}(x)= 2 {n \choose x}(c^{-1}B_c(x+2,n-x+1)+(1-c)^{-1}B(x+1,n-x+2)-(1-c)^{-1}B_c(x+1,n-x+2))

0 < mode=c < 1

x = 0,1,2,...n

n = 1,2,3...

The mean, variance and over dispersion are denoted as

E_{TriiBin}[x]= \frac{n(1+c)}{3}

Var_{TriBin}[x]= \frac{n(n+3)}{18}-\frac{n(n-3)c(1-c)}{18}

over dispersion= \frac{(1-c+c^2)}{2(2+c-c^2)}

Defined as B_c(a,b)=\int^c_0 t^{a-1} (1-t)^{b-1} \,dt is incomplete beta integrals and B(a,b) is the beta function.

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

Value

The output of dTriBin gives a list format consisting

pdf probability function values in vector form.

mean mean of the Triangular Binomial Distribution.

var variance of the Triangular Binomial Distribution.

over.dis.para over dispersion value of the Triangular Binomial Distribution.

References

Horsnell G (1957). “Economical acceptance sampling schemes.” Journal of the Royal Statistical Society. Series A (General), 120(2), 148–201. Karlis D, Xekalaki E (2008). The polygonal distribution. Springer. Okagbue HI, Edeki SO, Opanuga AA, Oguntunde PE, Adeosun ME (2014). “Using the Average of the Extreme Values of a Triangular Distribution for a Transformation, and Its Approximant via the Continuous Uniform Distribution.” British Journal of Mathematics and Computer Science, 4(24), 3497.

Examples

#plotting the random variables and probability values
col <- rainbow(7)
x <- seq(0.1,0.7,by=0.1)
plot(0,0,main="Triangular binomial probability function graph",xlab="Binomial random variable",
ylab="Probability function values",xlim = c(0,10),ylim = c(0,.3))
for (i in 1:7)
{
lines(0:10,dTriBin(0:10,10,x[i])$pdf,col = col[i],lwd=2.85)
points(0:10,dTriBin(0:10,10,x[i])$pdf,col = col[i],pch=16)
}

dTriBin(0:10,10,.4)$pdf        #extracting the pdf values
dTriBin(0:10,10,.4)$mean       #extracting the mean
dTriBin(0:10,10,.4)$var        #extracting the variance
dTriBin(0:10,10,.4)$over.dis.para  #extracting the over dispersion value

#plotting the random variables and cumulative probability values
col <- rainbow(7)
x <- seq(0.1,0.7,by=0.1)
plot(0,0,main="Triangular binomial probability function graph",xlab="Binomial random variable",
ylab="Probability function values",xlim = c(0,10),ylim = c(0,1))
for (i in 1:7)
{
lines(0:10,pTriBin(0:10,10,x[i]),col = col[i],lwd=2.85)
points(0:10,pTriBin(0:10,10,x[i]),col = col[i],pch=16)
}

pTriBin(0:10,10,.4)    #acquiring the cumulative probability values