pospoisson {VGAM} | R Documentation |
Fits a positive Poisson distribution.
pospoisson(link = "loge", earg=list(), expected=TRUE, ilambda=NULL, method.init=1)
link |
Link function for the usual mean (lambda) parameter of
an ordinary Poisson distribution.
See Links for more choices.
|
earg |
List. Extra argument for the link.
See earg in Links for general information.
|
expected |
Logical.
Fisher scoring is used if expected = TRUE , else Newton-Raphson.
|
ilambda |
Optional initial value for lambda.
A NULL means a value is computed internally.
|
method.init |
An integer with value 1 or 2 which
specifies the initialization method for lambda.
If failure to converge occurs try another value
and/or else specify a value for ilambda .
|
The positive Poisson distribution is the ordinary Poisson
distribution but with the probability of zero being zero. Thus the
other probabilities are scaled up (i.e., divided by 1-P[Y=0]).
The mean, lambda/(1-exp(-lambda)),
can be obtained by the extractor function fitted
applied to
the object.
A related distribution is the zero-inflated Poisson, in which the
probability P[Y=0] involves another parameter phi.
See zipoisson
.
An object of class "vglmff"
(see vglmff-class
).
The object is used by modelling functions such as vglm
,
rrvglm
and vgam
.
Under- or over-flow may occur if the data is ill-conditioned.
Yet to be done: a quasi.pospoisson
which estimates a dispersion
parameter.
This family function can handle a multivariate response.
Thomas W. Yee
Coleman, J. S. and James, J. (1961) The equilibrium size distribution of freely-forming groups. Sociometry, 24, 36–45.
Documentation accompanying the VGAM package at http://www.stat.auckland.ac.nz/~yee contains further information and examples.
Pospois
,
posnegbinomial
,
poissonff
,
zipoisson
.
y = 1:6 w = c(1486, 694, 195, 37, 10, 1) # Data from Coleman and James (1961) fit = vglm(y ~ 1, pospoisson, weights=w) Coef(fit) summary(fit) fitted(fit) # Artificial data x = runif(n <- 1000) lambda = exp(1 - 2*x) y = rpospois(n, lambda) table(y) fit = vglm(y ~ x, pospoisson, trace=TRUE, crit="c") coef(fit, matrix=TRUE)