Generalized Poisson Regression (GP-1 Parameterization)
Estimation of the two-parameter generalized Poisson distribution (GP-1 parameterization) which has the variance as a linear function of the mean.
genpoisson1(lmeanpar = "loglink", ldispind = "logloglink",
imeanpar = NULL, idispind = NULL, imethod = c(1, 1),
ishrinkage = 0.95, gdispind = exp(1:5),
parallel = FALSE, zero = "dispind")lmeanpar, ldispind |
Parameter link functions for μ and \varphi.
They are called the mean parameter
and dispersion index respectively.
See |
imeanpar, idispind |
Optional initial values for μ and \varphi. The default is to choose values internally. |
imethod |
See |
ishrinkage, zero |
See |
gdispind, parallel |
See |
This is a variant of the generalized Poisson distribution (GPD)
and is similar to the
GP-1 referred to by some writers such as Yang, et al. (2009).
Compared to the original GP-0 (see genpoisson0
the GP-1 has
θ = μ / √{\varphi} and
λ = 1 - 1 / √{\varphi} so that
the variance is μ \varphi.
The first linear predictor by default is
eta1 = log mu so that the GP-1
is more suitable for regression than the GP-1.
This family function can handle only overdispersion relative to the Poisson. An ordinary Poisson distribution corresponds to \varphi = 1. The mean (returned as the fitted values) is E(Y) = μ. For overdispersed data, this GP parameterization is a direct competitor of the NB-1 and quasi-Poisson.
An object of class "vglmff" (see vglmff-class).
The object is used by modelling functions such as vglm,
and vgam.
See genpoisson0 for warnings relevant here,
e.g., it is a good idea to monitor convergence because of
equidispersion and underdispersion.
T. W. Yee.
gdata <- data.frame(x2 = runif(nn <- 500))
gdata <- transform(gdata, y1 = rgenpois1(nn, mean = exp(2 + x2),
logloglink(-1, inverse = TRUE)))
gfit1 <- vglm(y1 ~ x2, genpoisson1, data = gdata, trace = TRUE)
coef(gfit1, matrix = TRUE)
summary(gfit1)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.