ZIBB
defines the zero inflated beta binomial distribution, a three parameter distribution,
for a gamlss.family
object to be used in GAMLSS fitting using the function gamlss()
.
The functions dZIBB
, pZIBB
, qZIBB
and rZINN
define the
density, distribution function, quantile function
and random generation for the zero inflated beta binomial, ZIBB
, distribution.
The function ZABB
defines the zero adjusted beta binomial distribution, a three parameter distribution, for a
gamlss.family
object to be used in GAMLSS fitting using the function gamlss()
. The functions dZABB
, pZABB
,
qZABB
and rZABB
define the
density, distribution function, quantile function
and random generation for the zero inflated beta binomial, ZABB()
, distribution.ZABB(mu.link = "logit", sigma.link = "log", nu.link = "logit")
ZIBB(mu.link = "logit", sigma.link = "log", nu.link = "logit")
dZIBB(x, mu = 0.5, sigma = 0.5, nu = 0.1, bd = 1, log = FALSE)
dZABB(x, mu = 0.5, sigma = 0.1, nu = 0.1, bd = 1, log = FALSE)
pZIBB(q, mu = 0.5, sigma = 0.5, nu = 0.1, bd = 1, lower.tail = TRUE, log.p = FALSE)
pZABB(q, mu = 0.5, sigma = 0.1, nu = 0.1, bd = 1, lower.tail = TRUE, log.p = FALSE)
qZIBB(p, mu = 0.5, sigma = 0.5, nu = 0.1, bd = 1, lower.tail = TRUE, log.p = FALSE)
qZABB(p, mu = 0.5, sigma = 0.1, nu = 0.1, bd = 1, lower.tail = TRUE, log.p = FALSE)
rZIBB(n, mu = 0.5, sigma = 0.5, nu = 0.1, bd = 1)
rZABB(n, mu = 0.5, sigma = 0.1, nu = 0.1, bd = 1)
mu.link
, with "logit" link as the default for the mu
parameter.
Other links are "probit" and "cloglog"'(complementary log-log)sigma.link
, with "log" link as the default for the sigma
parameter.sigma.link
, with "logit" link as the default for the mu
parameter.
Other links are "probit" and "cloglog"'(complementary log-log)ZIBB
and ZABB
return a gamlss.family
object which can be used to fit a
zero inflated or zero adjusted beta binomial distribution respectively in the gamlss()
function.gamlss.family
, NBI
, NBII
ZIBB()
ZABB()
# creating data and plotting them
dat <- rZIBB(1000, mu=.5, sigma=.5, nu=0.1, bd=10)
r <- barplot(table(dat), col='lightblue')
dat1 <- rZABB(1000, mu=.5, sigma=.2, nu=0.1, bd=10)
r1 <- barplot(table(dat1), col='lightblue')
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