
zinegbinomial(lphi = "logit", lmunb = "loge", lk = "loge", ephi = list(),
emunb = list(), ek = list(), iphi = NULL, ik = NULL, zero = -3,
method.init = 1, shrinkage.init = 0.95, nsimEIM = 200)
negbinomial
for details,
and Links
for more choices.earg
in Links
for general information.1
or 2
or 3
which
specifies the initialization method for the mean parameter.
If failure to converge occurs try another value
and/or else specify a value for shrinkage.init
.
CommonVGAMffArguments
for more information."vglmff"
(see vglmff-class
).
The object is used by modelling functions such as vglm
,
and vgam
.method.init
,
shrinkage.init
,
iphi
,
ik
, and/or
zero
if there are explanatory variables.negbinomial
, another Independent multivariate responses are handled.
If so then arguments iphi
and ik
may be vectors with
length equal to the number of responses.
Zinegbin
,
negbinomial
,
rpois
,
CommonVGAMffArguments
.# Example 1
nbdat <- data.frame(x2 = runif(nn <- 1000))
nbdat <- transform(nbdat, phi = logit(-0.5 + 1 * x2, inverse = TRUE),
munb = exp(3 + x2),
k = exp(0 + 2*x2))
nbdat <- transform(nbdat, y1 = rzinegbin(nn, phi, mu = munb, size = k),
y2 = rzinegbin(nn, phi, mu = munb, size = k))
with(nbdat, table(y1)["0"] / sum(table(y1)))
fit <- vglm(cbind(y1, y2) ~ x2, zinegbinomial(zero = NULL), nbdat)
coef(fit, matrix = TRUE)
summary(fit)
head(cbind(fitted(fit), with(nbdat, (1-phi) * munb)))
round(vcov(fit), 3)
# Example 2: RR-ZINB could also be called a COZIVGLM-ZINB-2
nbdat <- data.frame(x2 = runif(nn <- 2000))
nbdat <- transform(nbdat, x3 = runif(nn))
nbdat <- transform(nbdat, eta1 = 3 + 1 * x2 + 2 * x3)
nbdat <- transform(nbdat, phi = logit(-1.5 + 0.5 * eta1, inverse = TRUE),
munb = exp(eta1),
k = exp(4))
nbdat <- transform(nbdat, y1 = rzinegbin(nn, phi, mu = munb, size = k))
with(nbdat, table(y1)["0"] / sum(table(y1)))
rrzinb <- rrvglm(y1 ~ x2 + x3, zinegbinomial(zero = NULL), nbdat,
Index.corner = 2, szero = 3, trace = TRUE)
coef(rrzinb, matrix = TRUE)
Coef(rrzinb)
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