Mixture-Link-Binomial-Distribution: Distribution functions

In mixlink: Mixture Link Regression

Description

Functions for Mixture Link Binomial distribution

Usage

r.mixlink.binom(n, mean, Pi, kappa, m, save.latent = FALSE)

d.mixlink.binom(y, m, mean, Pi, kappa, log = FALSE)

p.mixlink.binom(y, m, mean, Pi, kappa)

Arguments

n	Number of observations to draw
mean	Parameter \vartheta of distribution
Pi	Parameter \bm{π} of distribution
kappa	Parameter κ of distribution
m	Number of success/failure trials
save.latent	Save intermediate latent variables used during draw.
y	Argument of pdf or cdf
log	Return log of the result (TRUE or FALSE)

Value

d.mixlink.binom gives the density, p.mixlink.binom gives the distribution function, and r.mixlink.binom generates random deviates.

References

Andrew M. Raim, Nagaraj K. Neerchal, and Jorge G. Morel. An Extension of Generalized Linear Models to Finite Mixture Outcomes. arXiv preprint: 1612.03302

Examples

  mean.true <- 1/3
  Pi.true <- c(1/5, 4/5)
  kappa.true <- 0.5
  m <- 10
  r.mixlink.binom(n = 30, mean.true, Pi.true, kappa.true, m)
  d.mixlink.binom(y = 5, m, mean.true, Pi.true, kappa.true)
  d.mixlink.binom(y = 5, m, mean.true, Pi.true, kappa.true, log = TRUE)
  p.mixlink.binom(y = 5, m, mean.true, Pi.true, kappa.true)

mixlink documentation built on May 2, 2019, 5:11 a.m.