Poisson Regression
Family function for a generalized linear model fitted to Poisson responses.
poissonff(link = "loglink", imu = NULL,
imethod = 1, parallel = FALSE, zero = NULL, bred = FALSE,
earg.link = FALSE, type.fitted = c("mean", "quantiles"),
percentiles = c(25, 50, 75))link |
Link function applied to the mean or means.
See |
parallel |
A logical or formula. Used only if the response is a matrix. |
imu, imethod |
See |
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 |
bred, earg.link |
Details at |
type.fitted, percentiles |
Details at |
M defined above is the number of linear/additive predictors.
With overdispersed data try negbinomial.
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.
This function will handle a matrix response automatically.
Regardless of whether the dispersion parameter is to be estimated or
not, its value can be seen from the output from the summary()
of the object.
Thomas W. Yee
McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models, 2nd ed. London: Chapman & Hall.
Links,
hdeff.vglm,
negbinomial,
genpoisson1,
genpoisson2,
genpoisson0,
gaitpoisson,
zipoisson,
pospoisson,
oipospoisson,
otpospoisson,
skellam,
mix2poisson,
cens.poisson,
ordpoisson,
amlpoisson,
inv.binomial,
simulate.vlm,
loglink,
polf,
rrvglm,
cqo,
cao,
binomialff,
poisson,
Poisson,
poisson.points,
ruge,
V1,
V2,
residualsvglm.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.