pCOMPBin | R Documentation |
These functions provide the ability for generating probability function values and cumulative probability function values for the COM Poisson Binomial Distribution.
pCOMPBin(x,n,p,v)
x |
vector of binomial random variables. |
n |
single value for no of binomial trials. |
p |
single value for probability of success. |
v |
single value for v. |
The probability function and cumulative function can be constructed and are denoted below
The cumulative probability function is the summation of probability function values.
P_{COMPBin}(x) = \frac{{n \choose x}^v p^x (1-p)^{n-x}}{\sum_{j=0}^{n} {n \choose j}^v p^j (1-p)^{(n-j)}}
x = 0,1,2,3,...n
n = 1,2,3,...
0 < p < 1
-\infty < v < +\infty
NOTE : If input parameters are not in given domain conditions necessary error messages will be provided to go further.
The output of pCOMPBin
gives cumulative probability values in vector form.
borges2014poissonfitODBOD
#plotting the random variables and probability values
col <- rainbow(5)
a <- c(0.58,0.59,0.6,0.61,0.62)
b <- c(0.022,0.023,0.024,0.025,0.026)
plot(0,0,main="COM Poisson 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,dCOMPBin(0:10,10,a[i],b[i])$pdf,col = col[i],lwd=2.85)
points(0:10,dCOMPBin(0:10,10,a[i],b[i])$pdf,col = col[i],pch=16)
}
dCOMPBin(0:10,10,0.58,0.022)$pdf #extracting the pdf values
dCOMPBin(0:10,10,0.58,0.022)$mean #extracting the mean
dCOMPBin(0:10,10,0.58,0.022)$var #extracting the variance
#plotting the random variables and cumulative probability values
col <- rainbow(5)
a <- c(0.58,0.59,0.6,0.61,0.62)
b <- c(0.022,0.023,0.024,0.025,0.026)
plot(0,0,main="COM Poisson 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,pCOMPBin(0:10,10,a[i],b[i]),col = col[i],lwd=2.85)
points(0:10,pCOMPBin(0:10,10,a[i],b[i]),col = col[i],pch=16)
}
pCOMPBin(0:10,10,0.58,0.022) #acquiring the cumulative probability values
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