# pZOIP: ZOIP Distribution In jucdiaz/ZOIP: ZOIP Distribution, ZOIP Regression, ZOIP Mixed Regression

## Description

The pZOIP function defines the cumulative distribution function of the ZOIP distribution.

## Usage

 ```1 2``` ```pZOIP(q, mu = 0.5, sigma = 0.1, p0 = 0.08333333, p1 = 0.08333333, family = "R-S", lower.tail = TRUE, log.p = FALSE) ```

## Arguments

 `q` quantiles vector. `mu` vector of location parameters. `sigma` vector of scale parameters. `p0` parameter of proportion of zeros. `p1` Parameter of proportion of ones. `family` choice of the parameterization or distribution, family = 'R-S' parameterization beta distribution Rigby and Stasinopoulos, 'F-C' distribution Beta parametrization Ferrari and Cribari-Neto, 'Original' Beta distribution classic parameterization, 'Simplex' simplex distribution. `lower.tail` logical; if TRUE (default), probabilities will be P [X <= x], otherwise, P [X> x]. `log.p` logical; if TRUE, the probabilities of p will be given as log (p).

## Details

x has ZOIP distribution with shape parameters "μ", scale "σ", proportion of zeros "p0" and proportion of ones " p1 ", has density: p0 if x = 0, p1 if x = 1, (1-p0-p1) f (x; μ, σ) yes 0 <x <1.

where p0 ≥ 0 represents the probability that x = 0, p1 ≥ 0 represents the probability that x = 1, 0 ≤ p0 + p1 ≤ 1 and f (x; μ, σ) represents some of the functions of probability density for proportional data, such as the beta distribution with its different parameterizations and the simplex distribution.

When family =' R-S 'uses the beta distribution with beta parameterization Rigby and Stasinopoulos (2005) which has a beta distribution function. μ is the parameter of mean and shape, plus σ is the dispersion parameter of the distribution. family =' F-C 'distribution Beta parametrization Ferrari and Cribari-Neto (2004), where σ = φ, φ is a precision parameter. family =' Original 'beta distribution original parametrization where μ = a, a parameter of form 1; σ = b, b parameter of form 2. family =' Simplex 'simplex distribution. proposed by Barndorff-Nielsen and Jørgensen (1991)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```library(ZOIP) pZOIP(q=0.5, mu = 0.2, sigma = 0.5, p0 = 0.2, p1 = 0.2,family='R-S',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0.2, p1 = 0.2,family='F-C',log = FALSE) pZOIP(q=0.5, mu = 0.6, sigma = 2.4, p0 = 0.2, p1 = 0.2,family='Original',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0.2, p1 = 0.2,family='Simplex',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 0.5, p0 = 0, p1 = 0.2,family='R-S',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0, p1 = 0.2,family='F-C',log = FALSE) pZOIP(q=0.5, mu = 0.6, sigma = 2.4, p0 = 0, p1 = 0.2,family='Original',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0, p1 = 0.2,family='Simplex',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 0.5, p0 = 0.2, p1 = 0,family='R-S',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0.2, p1 = 0,family='F-C',log = FALSE) pZOIP(q=0.5, mu = 0.6, sigma = 2.4, p0 = 0.2, p1 = 0,family='Original',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0.2, p1 = 0,family='Simplex',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 0.5, p0 = 0, p1 = 0,family='R-S',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0, p1 = 0,family='F-C',log = FALSE) pZOIP(q=0.5, mu = 0.6, sigma = 2.4, p0 = 0, p1 = 0,family='Original',log = FALSE) pZOIP(q=0.5, mu = 0.2, sigma = 3, p0 = 0, p1 = 0,family='Simplex',log = FALSE) ```

jucdiaz/ZOIP documentation built on Aug. 17, 2018, 2:24 a.m.