VGAM (version 1.0-6)

poissonff: Poisson Family Function

Description

Family function for a generalized linear model fitted to Poisson responses.

Usage

poissonff(link = "loge", imu = NULL,
          imethod = 1, parallel = FALSE, zero = NULL, bred = FALSE,
          earg.link = FALSE, type.fitted = c("mean", "quantiles"),
                       percentiles = c(25, 50, 75))

Arguments

link

Link function applied to the mean or means. See Links for more choices and information.

parallel

A logical or formula. Used only if the response is a matrix.

imu, imethod

See CommonVGAMffArguments for more information.

zero

Can be an integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set {1,2,…,$M$}, where $M$ is the number of columns of the matrix response. See CommonVGAMffArguments for more information.

bred, earg.link

Details at CommonVGAMffArguments. Setting bred = TRUE should work for multiple responses and all VGAM link functions; it has been tested for loge, identity but further testing is required.

type.fitted, percentiles

Details at CommonVGAMffArguments.

Value

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

Warning

With multiple responses, assigning a known dispersion parameter for each response is not handled well yet. Currently, only a single known dispersion parameter is handled well.

Details

$M$ defined above is the number of linear/additive predictors. With overdispersed data try negbinomial.

References

McCullagh, P. and Nelder, J. A. (1989) Generalized Linear Models, 2nd ed. London: Chapman & Hall.

See Also

Links, hdeff.vglm, negbinomial, genpoisson, zipoisson, pospoisson, oipospoisson, otpospoisson, skellam, mix2poisson, cens.poisson, ordpoisson, amlpoisson, inv.binomial, simulate.vlm, loge, polf, rrvglm, cqo, cao, binomialff, poisson, Poisson, poisson.points, ruge, V1, residualsvglm.

Examples

# NOT RUN {
poissonff()

set.seed(123)
pdata <- data.frame(x2 = rnorm(nn <- 100))
pdata <- transform(pdata, y1 = rpois(nn, exp(1 + x2)),
                          y2 = rpois(nn, exp(1 + x2)))
(fit1 <- vglm(cbind(y1, y2) ~ x2, poissonff, data = pdata))
(fit2 <- vglm(y1 ~ x2, poissonff(bred = TRUE), data = pdata))
coef(fit1, matrix = TRUE)
coef(fit2, matrix = TRUE)

nn <- 200
cdata <- data.frame(x2 = rnorm(nn), x3 = rnorm(nn), x4 = rnorm(nn))
cdata <- transform(cdata, lv1 = 0 + x3 - 2*x4)
cdata <- transform(cdata, lambda1 = exp(3 - 0.5 *  (lv1-0)^2),
                          lambda2 = exp(2 - 0.5 *  (lv1-1)^2),
                          lambda3 = exp(2 - 0.5 * ((lv1+4)/2)^2))
cdata <- transform(cdata, y1 = rpois(nn, lambda1),
                          y2 = rpois(nn, lambda2),
                          y3 = rpois(nn, lambda3))
# }
# NOT RUN {
 lvplot(p1, y = TRUE, lcol = 2:4, pch = 2:4, pcol = 2:4, rug = FALSE) 
# }