LG
defines the logarithmic distribution, a one parameter distribution, for a gamlss.family
object to be
used in GAMLSS fitting using the function gamlss()
. The functions dLG
, pLG
, qLG
and rLG
define the
density, distribution function, quantile function
and random generation for the logarithmic , LG()
, distribution.
The function ZALG
defines the zero adjusted logarithmic distribution, a two parameter distribution, for a gamlss.family
object to be
used in GAMLSS fitting using the function gamlss()
. The functions dZALG
, pZALG
, qZALG
and rZALG
define the
density, distribution function, quantile function
and random generation for the inflated logarithmic , ZALG()
, distribution.LG(mu.link = "logit")
dLG(x, mu = 0.5, log = FALSE)
pLG(q, mu = 0.5, lower.tail = TRUE, log.p = FALSE)
qLG(p, mu = 0.5, lower.tail = TRUE, log.p = FALSE, max.value = 10000)
rLG(n, mu = 0.5)
ZALG(mu.link = "logit", sigma.link = "logit")
dZALG(x, mu = 0.5, sigma = 0.1, log = FALSE)
pZALG(q, mu = 0.5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
qZALG(p, mu = 0.5, sigma = 0.1, lower.tail = TRUE, log.p = FALSE)
rZALG(n, mu = 0.5, sigma = 0.1)
mu.link
, with logit
link as the default for the mu
parametersigma.link
, with logit
link as the default for the sigma parameter which in this case
is the probability at zero.LG
and ZALG
return a gamlss.family
object which can be used to fit a
logarithmic and a zero inflated logarithmic distributions respectively in the gamlss()
function.gamlss.family
, PO
, ZAP
LG()
ZAP()
# creating data and plotting them
dat <- rLG(1000, mu=.3)
r <- barplot(table(dat), col='lightblue')
dat1 <- rZALG(1000, mu=.3, sigma=.1)
r1 <- barplot(table(dat1), col='lightblue')
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