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)
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