# zinegbinomial

##### Zero-Inflated Negative Binomial Distribution Family Function

Fits a zero-inflated negative binomial distribution by full maximum likelihood estimation.

- Keywords
- models, regression

##### Usage

```
zinegbinomial(zero = "size",
type.fitted = c("mean", "munb", "pobs0", "pstr0",
"onempstr0"),
mds.min = 1e-3, nsimEIM = 500, cutoff.prob = 0.999,
eps.trig = 1e-7, max.support = 4000, max.chunk.MB = 30,
lpstr0 = "logitlink", lmunb = "loglink", lsize = "loglink",
imethod = 1, ipstr0 = NULL, imunb = NULL,
iprobs.y = NULL, isize = NULL,
gprobs.y = (0:9)/10,
gsize.mux = exp(c(-30, -20, -15, -10, -6:3)))
zinegbinomialff(lmunb = "loglink", lsize = "loglink", lonempstr0 = "logitlink",
type.fitted = c("mean", "munb", "pobs0", "pstr0",
"onempstr0"), imunb = NULL, isize = NULL, ionempstr0 =
NULL, zero = c("size", "onempstr0"), imethod = 1,
iprobs.y = NULL, cutoff.prob = 0.999,
eps.trig = 1e-7, max.support = 4000, max.chunk.MB = 30,
gprobs.y = (0:9)/10, gsize.mux = exp((-12:6)/2),
mds.min = 1e-3, nsimEIM = 500)
```

##### Arguments

- lpstr0, lmunb, lsize
Link functions for the parameters \(\phi\), the mean and \(k\); see

`negbinomial`

for details, and`Links`

for more choices. For the zero-*deflated*model see below.- type.fitted
See

`CommonVGAMffArguments`

and`fittedvlm`

for more information.- ipstr0, isize, imunb
Optional initial values for \(\phi\) and \(k\) and \(\mu\). The default is to compute an initial value internally for both. If a vector then recycling is used.

- lonempstr0, ionempstr0
Corresponding arguments for the other parameterization. See details below.

- imethod
An integer with value

`1`

or`2`

or`3`

which specifies the initialization method for the mean parameter. If failure to converge occurs try another value. See`CommonVGAMffArguments`

for more information.- zero
Specifies which linear/additive predictors are to be modelled as intercept-only. They can be such that their absolute values are either 1 or 2 or 3. The default is the \(\phi\) and \(k\) parameters (both for each response). See

`CommonVGAMffArguments`

for more information.- nsimEIM
See

`CommonVGAMffArguments`

for information.- iprobs.y, cutoff.prob, max.support, max.chunk.MB
See

`negbinomial`

and/or`posnegbinomial`

for details.- mds.min, eps.trig
See

`negbinomial`

for details.- gprobs.y, gsize.mux
These arguments relate to grid searching in the initialization process. See

`negbinomial`

and/or`posnegbinomial`

for details.

##### Details

These functions are based on
$$P(Y=0) = \phi + (1-\phi) (k/(k+\mu))^k,$$
and for \(y=1,2,\ldots\),
$$P(Y=y) = (1-\phi) \, dnbinom(y, \mu, k).$$
The parameter \(\phi\) satisfies \(0 < \phi < 1\).
The mean of \(Y\) is \((1-\phi) \mu\)
(returned as the fitted values).
By default, the three linear/additive predictors
for `zinegbinomial()`

are \((logit(\phi), \log(\mu), \log(k))^T\).
See `negbinomial`

, another VGAM family function,
for the formula of the probability density function and other details
of the negative binomial distribution.

Independent multiple responses are handled.
If so then arguments `ipstr0`

and `isize`

may be vectors
with length equal to the number of responses.

The VGAM family function `zinegbinomialff()`

has a few
changes compared to `zinegbinomial()`

.
These are:
(i) the order of the linear/additive predictors is switched so the
NB mean comes first;
(ii) `onempstr0`

is now 1 minus the probability of a structural 0,
i.e., the probability of the parent (NB) component,
i.e., `onempstr0`

is `1-pstr0`

;
(iii) argument `zero`

has a new default so that the `onempstr0`

is intercept-only by default.
Now `zinegbinomialff()`

is generally recommended over
`zinegbinomial()`

.
Both functions implement Fisher scoring and can handle
multiple responses.

##### Value

An object of class `"vglmff"`

(see `vglmff-class`

).
The object is used by modelling functions such as `vglm`

,
and `vgam`

.

##### Note

Estimated probabilities of a structural zero and an
observed zero can be returned, as in `zipoisson`

;
see `fittedvlm`

for more information.

If \(k\) is large then the use of VGAM family function
`zipoisson`

is probably preferable.
This follows because the Poisson is the limiting distribution of a
negative binomial as \(k\) tends to infinity.

The zero-*deflated* negative binomial distribution
might be fitted by setting `lpstr0 = identitylink`

,
albeit, not entirely reliably. See `zipoisson`

for information that can be applied here. Else try
the zero-altered negative binomial distribution (see
`zanegbinomial`

).

##### Warning

This model can be difficult to fit to data,
and this family function is fragile.
The model is especially difficult to fit reliably when
the estimated \(k\) parameter is very large (so the model
approaches a zero-inflated Poisson distribution) or
much less than 1
(and gets more difficult as it approaches 0).
Numerical problems can also occur, e.g., when the probability of
a zero is actually less than, and not more than, the nominal
probability of zero.
Similarly, numerical problems can occur if there is little
or no 0-inflation, or when the sample size is small.
Half-stepping is not uncommon.
Successful convergence is sensitive to the initial values, therefore
if failure to converge occurs, try using combinations of arguments
`stepsize`

(in `vglm.control`

),
`imethod`

,
`imunb`

,
`ipstr0`

,
`isize`

, and/or
`zero`

if there are explanatory variables.
Else try fitting an ordinary `negbinomial`

model
or a `zipoisson`

model.

This VGAM family function can be computationally expensive
and can run slowly;
setting `trace = TRUE`

is useful for monitoring convergence.

##### See Also

##### Examples

```
# NOT RUN {
# Example 1
ndata <- data.frame(x2 = runif(nn <- 1000))
ndata <- transform(ndata, pstr0 = logitlink(-0.5 + 1 * x2, inverse = TRUE),
munb = exp( 3 + 1 * x2),
size = exp( 0 + 2 * x2))
ndata <- transform(ndata,
y1 = rzinegbin(nn, mu = munb, size = size, pstr0 = pstr0))
with(ndata, table(y1)["0"] / sum(table(y1)))
nfit <- vglm(y1 ~ x2, zinegbinomial(zero = NULL), data = ndata)
coef(nfit, matrix = TRUE)
summary(nfit)
head(cbind(fitted(nfit), with(ndata, (1 - pstr0) * munb)))
round(vcov(nfit), 3)
# Example 2: RR-ZINB could also be called a COZIVGLM-ZINB-2
ndata <- data.frame(x2 = runif(nn <- 2000))
ndata <- transform(ndata, x3 = runif(nn))
ndata <- transform(ndata, eta1 = 3 + 1 * x2 + 2 * x3)
ndata <- transform(ndata, pstr0 = logitlink(-1.5 + 0.5 * eta1, inverse = TRUE),
munb = exp(eta1),
size = exp(4))
ndata <- transform(ndata,
y1 = rzinegbin(nn, pstr0 = pstr0, mu = munb, size = size))
with(ndata, table(y1)["0"] / sum(table(y1)))
rrzinb <- rrvglm(y1 ~ x2 + x3, zinegbinomial(zero = NULL), data = ndata,
Index.corner = 2, str0 = 3, trace = TRUE)
coef(rrzinb, matrix = TRUE)
Coef(rrzinb)
# }
```

*Documentation reproduced from package VGAM, version 1.1-1, License: GPL-3*