rbinomial.mixture {QRMlib}R Documentation

Sample Mixed Binomial Distribution

Description

random generation from mixed binomial distribution

Usage

rbinomial.mixture(n=1000, m=100, model="probitnorm", ...)

Arguments

n sample size
m vector of numbers of coin flips
model name of mixing distribution: "probitnorm", "logitnorm","beta","
... further parameters of mixing distribution

Details

see pages 354-355 and pages 375-377 of QRM

Value

vector of numbers of successes

See Also

rbeta, rprobitnorm, rlogitnorm

Examples

pi <- 0.04896; #one obligor defaulting pi = .04896 
pi2 <- 0.00321; #two obligors defaulting jointly pi2 = .0031265
beta.pars <- cal.beta(pi,pi2); 
probitnorm.pars <- cal.probitnorm(pi,pi2); 
n <- 1000; 
m <- rep(500,n); 
M.beta <- rbinomial.mixture(n,m,"beta",shape1=beta.pars[1],
         shape2=beta.pars[2]); 
M.probitnorm <- rbinomial.mixture(n,m,"probitnorm",
    mu=probitnorm.pars[1],sigma=probitnorm.pars[2]); 

[Package QRMlib version 1.4.4 Index]