ZABI {gamlss.dist} | R Documentation |
Zero inflated and zero adjusted Binomial distribution for fitting in GAMLSS
Description
The ZABI()
function defines the zero adjusted binomial distribution, a two parameter distribution,
for a gamlss.family
object to be used
in GAMLSS fitting using the function gamlss()
.
The functions dZABI
, pZABI
, qZABI
and rZABI
define the density, distribution function, quantile function and random
generation for the zero adjusted binomial, ZABI()
, distribution.
The ZIBI()
function defines the zero inflated binomial distribution, a two parameter distribution,
for a gamlss.family
object to be used
in GAMLSS fitting using the function gamlss()
.
The functions dZIBI
, pZIBI
, qZIBI
and rZIBI
define the density, distribution function, quantile function and random
generation for the zero inflated binomial, ZIBI()
, distribution.
Usage
ZABI(mu.link = "logit", sigma.link = "logit")
dZABI(x, bd = 1, mu = 0.5, sigma = 0.1, log = FALSE)
pZABI(q, bd = 1, mu = 0.5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
qZABI(p, bd = 1, mu = 0.5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
rZABI(n, bd = 1, mu = 0.5, sigma = 0.1)
ZIBI(mu.link = "logit", sigma.link = "logit")
dZIBI(x, bd = 1, mu = 0.5, sigma = 0.1, log = FALSE)
pZIBI(q, bd = 1, mu = 0.5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
qZIBI(p, bd = 1, mu = 0.5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
rZIBI(n, bd = 1, mu = 0.5, sigma = 0.1)
Arguments
mu.link |
Defines the |
sigma.link |
Defines the |
x |
vector of (non-negative integer) quantiles |
mu |
vector of positive probabilities |
sigma |
vector of positive probabilities |
bd |
vector of binomial denominators |
p |
vector of probabilities |
q |
vector of quantiles |
n |
number of random values to return |
log , log.p |
logical; if TRUE, probabilities p are given as log(p) |
lower.tail |
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x] |
Details
The definition of the zero adjusted binomial distribution, ZABI
and the the zero inflated binomial distribution, ZIBI
, are given in p. 526 and p. 527 of of Rigby et al. (2019), respectively.
Value
The functions ZABI
and ZIBI
return a gamlss.family
object which
can be used to fit a binomial distribution in the gamlss()
function.
Note
The response variable should be a matrix containing two columns, the first with the count of successes and the second with the count of failures.
Author(s)
Mikis Stasinopoulos, Bob Rigby
References
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC, doi:10.1201/9780429298547. An older version can be found in https://www.gamlss.com/.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, doi:10.18637/jss.v023.i07.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC. doi:10.1201/b21973
(see also https://www.gamlss.com/).
See Also
Examples
ZABI()
curve(dZABI(x, mu = .5, bd=10), from=0, to=10, n=10+1, type="h")
tN <- table(Ni <- rZABI(1000, mu=.2, sigma=.3, bd=10))
r <- barplot(tN, col='lightblue')
ZIBI()
curve(dZIBI(x, mu = .5, bd=10), from=0, to=10, n=10+1, type="h")
tN <- table(Ni <- rZIBI(1000, mu=.2, sigma=.3, bd=10))
r <- barplot(tN, col='lightblue')