Description Usage Arguments Details Value Author(s) References Examples
The cdf, pmf, quantile function, and random number generation for the Poisson binomial distribution.
1 2 3 4 |
kk |
The values where the cdf or pmf to be evaluated. |
pp |
The vector for p_j's which are the sucess probabilities for indicators. |
method |
"DFT-CF" for the DFT-CF method, "RF" for the recursive formula, "RNA" for the refined normal approximation, "NA" for the normal approximation, and "PA" for the Poisson approximation. |
wts |
The weights for p_j's. |
qq |
The values where the quantile function to be evaluated. |
m |
The number of random numbers to be generated. |
See the reference for computational details.
Returns the entire cdf, pmf, quantiles, and random numbers.
Yili Hong [aut, cre], R Core Team [aut, cph]
Hong, Y. (2013). On computing the distribution function for the Poisson binomial distribution. Computational Statistics & Data Analysis, Vol. 59, pp. 41-51.
1 2 3 4 5 6 7 8 9 10 | kk=0:10
pp=c(.1,.2,.3,.4,.5)
ppoibin(kk=kk, pp=pp, method = "DFT-CF",wts=rep(2,5))
ppoibin(kk=kk, pp=pp, method = "RF",wts=rep(2,5))
ppoibin(kk=kk, pp=pp, method = "RNA",wts=rep(2,5))
ppoibin(kk=kk, pp=pp, method = "NA",wts=rep(2,5))
ppoibin(kk=kk, pp=pp, method = "PA",wts=rep(2,5))
dpoibin(kk=kk, pp=pp,wts=rep(2,5))
qpoibin(qq=0:10/10,pp=pp,wts=rep(2,5))
rpoibin(m=2,pp=pp,wts=rep(2,5))
|
[1] 0.02286144 0.13517280 0.37211652 0.65383500 0.86212984 0.96181016
[7] 0.99294500 0.99918348 0.99994720 0.99999856 1.00000000
[1] 0.02286144 0.13517280 0.37211652 0.65383500 0.86212984 0.96181016
[7] 0.99294500 0.99918348 0.99994720 0.99999856 1.00000000
[1] 0.02812958 0.13669805 0.37078646 0.65398594 0.86019757 0.95840421
[7] 0.99114093 0.99873860 0.99988766 0.99999400 0.99999981
[1] 0.03486268 0.13825024 0.35840026 0.64159974 0.86174976 0.96513732
[7] 0.99444416 0.99945199 0.99996698 0.99999879 0.99999997
[1] 0.04978707 0.19914827 0.42319008 0.64723189 0.81526324 0.91608206
[7] 0.96649146 0.98809550 0.99619701 0.99889751 0.99970766
[1] 0.02286144 0.11231136 0.23694372 0.28171848 0.20829484 0.09968032
[7] 0.03113484 0.00623848 0.00076372 0.00005136 0.00000144
[1] 0 1 2 2 3 3 3 4 4 5 10
[1] 5 5
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