pfmbeg: The finite mixture of bivariate distribution with mixture...

In camponsah/BivMixDist: Fit bivariate continous and discrete distributions as well as provide EM algorithm for discrete Pareto

Description

Distribution function for FMBEG.

Usage

pfmbeg(data, beta, p, q, pi, lower.tail = TRUE, log.p = FALSE)

Arguments

data	is bivariate vector (X,N) vector representing observations from BMEG model.
beta	vector of parameters which must be numeric greater than 0.
p	vector of numeric parameters between 0 and 1 of the finite mixture of geometric distribution.
q	vector of gamma membership probabilities between 0 and 1, and sum equal to 1.
pi	vector of geometric membership probabilities between 0 and 1, and sum equal to 1.
lower.tail	logical; if TRUE (default), probabilities are P[X ≤ x, N ≤q n], otherwise, P[X > x, N > n].
log.p	logical; if TRUE, probabilities p are given as log(p).

Details

pfmbeg is the distribution function.

Value

vector of distribution.

References

Amponsah, C. K. and Kozubowski, T.J., and Panorska, A.K. (2020). A Mixed bivariate distribution with mixture exponential and geometric marginals. Inprint.

Examples

data.df <- rfmbeg(10, beta = c(1,2,10), p=0.5, q=c(0.3,0.2,0.5), pi=1)
prob <- pfmbeg(data=data.df, beta = c(1,2,10), p=0.5, q=c(0.3,0.2,0.5), pi=1)
prob

camponsah/BivMixDist documentation built on Nov. 15, 2021, 3:11 a.m.