Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function PO
defines the Poisson distribution, an one parameter distribution, for a gamlss.family
object to be used in GAMLSS fitting
using the function gamlss()
. The functions dPO
, pPO
, qPO
and rPO
define the density, distribution function, quantile function and random
generation for the Poisson, PO()
, distribution.
1 2 3 4 5 |
mu.link |
Defines the |
x |
vector of (non-negative integer) quantiles |
mu |
vector of positive means |
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] |
Definition file for Poisson distribution.
f(y|μ)=e^(-μ)*μ^y/Γ(y+1)
for y=0,1,2,... and μ>0.
returns a gamlss.family
object which can be used to fit a Poisson distribution in the gamlss()
function.
mu is the mean of the Poisson distribution
Bob Rigby, Mikis Stasinopoulos mikis.stasinopoulos@gamlss.org, and Kalliope Akantziliotou
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.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).
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, http://www.jstatsoft.org/v23/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.
gamlss.family
, NBI
, NBII
,
SI
, SICHEL
1 2 3 4 5 6 7 8 9 10 11 12 13 | PO()# gives information about the default links for the Poisson distribution
# fitting data using PO()
# plotting the distribution
plot(function(y) dPO(y, mu=10 ), from=0, to=20, n=20+1, type="h")
# creating random variables and plot them
tN <- table(Ni <- rPO(1000, mu=5))
r <- barplot(tN, col='lightblue')
# library(gamlss)
# data(aids)
# h<-gamlss(y~cs(x,df=7)+qrt, family=PO, data=aids) # fits the constant+x+qrt model
# plot(h)
# pdf.plot(family=PO, mu=10, min=0, max=20, step=1)
|
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