VGAM (version 0.9-1)

pospoisson: Positive Poisson Distribution Family Function

Description

Fits a positive Poisson distribution.

Usage

pospoisson(link = "loge", expected = TRUE,
           ilambda = NULL, imethod = 1, zero = NULL)

Arguments

link

Link function for the usual mean (lambda) parameter of an ordinary Poisson distribution. See Links for more choices.

expected

Logical. Fisher scoring is used if expected = TRUE, else Newton-Raphson.

ilambda, imethod, zero

See CommonVGAMffArguments for more information.

Value

• An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.

Warning

Under- or over-flow may occur if the data is ill-conditioned.

Details

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.

References

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.

See Also

Pospois, posnegbinomial, poissonff, zipoisson.

Examples

# Data from Coleman and James (1961)
cjdata <- data.frame(y = 1:6, freq = c(1486, 694, 195, 37, 10, 1))
fit <- vglm(y ~ 1, pospoisson, cjdata, weights = freq)
Coef(fit)
summary(fit)
fitted(fit)

pdata <- data.frame(x2 = runif(nn <- 1000)) # Artificial data
pdata <- transform(pdata, lambda = exp(1 - 2 * x2))
pdata <- transform(pdata, y1 = rpospois(nn, lambda))
with(pdata, table(y1))
fit <- vglm(y1 ~ x2, pospoisson, pdata, trace = TRUE, crit = "coef")
coef(fit, matrix = TRUE)