YULE {gamlss.dist} | R Documentation |
Yule distribution for fitting a GAMLSS model
Description
The function YULE
defines the Yule distribution, a one parameter
distribution, for a gamlss.family
object to be used in GAMLSS fitting
using the function gamlss()
, with mean equal to the parameter mu
.
The functions dYULE
, pYULE
, qYULE
and rYULE
define
the density, distribution function, quantile function and random generation for
the YULE
parameterization of the Yule distribution.
Usage
YULE(mu.link = "log")
dYULE(x, mu = 2, log = FALSE)
pYULE(q, mu = 2, lower.tail = TRUE, log.p = FALSE)
qYULE(p, mu = 2, lower.tail = TRUE, log.p = FALSE,
max.value = 10000)
rYULE(n, mu = 2)
Arguments
mu.link |
Defines the |
x |
vector of (non-negative integer) quantiles. |
q |
vector of quantiles. |
p |
vector of probabilities. |
n |
number of random values to return. |
mu |
vector of positive |
lower.tail |
logical; if |
log , log.p |
logical; if |
max.value |
constant; generates a sequence of values for the cdf function. |
Details
The Yule distribution has density
P(Y=y|\mu) = (\mu^{-1}+1) B(y+1,\mu^{-1}+2)
for y=0,1,2,\ldots
and \mu>0
, see pp 477-478 of Rigby et al. (2019).
Value
Returns a gamlss.family
object which can be used to fit a Yule distribution in the gamlss()
function.
Author(s)
Fiona McElduff, Bob Rigby and Mikis Stasinopoulos.
References
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., 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
Wimmer, G. and Altmann, G. (1999) Thesaurus of univariate discrete probability distributions. Stamm.
(see also https://www.gamlss.com/).
See Also
Examples
par(mfrow=c(2,2))
y<-seq(0,20,1)
plot(y, dYULE(y), type="h")
q <- seq(0, 20, 1)
plot(q, pYULE(q), type="h")
p<-seq(0.0001,0.999,0.05)
plot(p , qYULE(p), type="s")
dat <- rYULE(100)
hist(dat)
#summary(gamlss(dat~1, family=YULE))