zanegbinomial {VGAM} | R Documentation

## Zero-Altered Negative Binomial Distribution

### Description

Fits a zero-altered negative binomial distribution based on a conditional model involving a binomial distribution and a positive-negative binomial distribution.

### Usage

    zanegbinomial(zero = "size", type.fitted = c("mean", "munb", "pobs0"),
                  mds.min = 1e-3, nsimEIM = 500, cutoff.prob = 0.999,
                  eps.trig = 1e-7, max.support = 4000, max.chunk.MB = 30,
                  lpobs0 = "logitlink", lmunb = "loglink", lsize = "loglink",
                  imethod = 1, ipobs0 = NULL,
                  imunb = NULL, iprobs.y = NULL, gprobs.y = (0:9)/10,
                  isize = NULL, gsize.mux = exp(c(-30, -20, -15, -10, -6:3)))
    zanegbinomialff(lmunb = "loglink", lsize = "loglink", lonempobs0 = "logitlink",
                    type.fitted = c("mean", "munb", "pobs0", "onempobs0"),
                    isize = NULL, ionempobs0 = NULL, zero = c("size",
                    "onempobs0"), mds.min = 1e-3, iprobs.y = NULL, gprobs.y = (0:9)/10,
                    cutoff.prob = 0.999, eps.trig = 1e-7, max.support = 4000,
                    max.chunk.MB = 30, gsize.mux = exp(c(-30, -20, -15, -10, -6:3)),
                    imethod = 1, imunb = NULL,
                    nsimEIM = 500)

### Arguments

`lpobs0` | Link function for the parameter pobs0, called `pobs0` here. See `Links` for more choices.
`lmunb` | Link function applied to the `munb` parameter, which is the mean munb of an ordinary negative binomial distribution. See `Links` for more choices.
`lsize` | Parameter link function applied to the reciprocal of the dispersion parameter, called `k`. That is, as `k` increases, the variance of the response decreases. See `Links` for more choices.
`type.fitted` | See `CommonVGAMffArguments` and `fittedvlm` for information.
`lonempobs0, ionempobs0` | Corresponding argument for the other parameterization. See details below.

`ipobs0, imunb, isize` | Optional initial values for pobs0 and `munb` and `k`. If given then it is okay to give one value for each response/species by inputting a vector whose length is the number of columns of the response matrix.
`zero` | Specifies which of the three linear predictors are modelled as intercept-only. All parameters can be modelled as a function of the explanatory variables by setting `zero = NULL` (not recommended). A negative value means that the value is recycled, e.g., setting -3 means all `k` are intercept-only for `zanegbinomial`. See `CommonVGAMffArguments` for more information.
`nsimEIM, imethod` | See `CommonVGAMffArguments`.

`iprobs.y, gsize.mux, gprobs.y` | See `negbinomial`.
`cutoff.prob, eps.trig` | See `negbinomial`.
`mds.min, max.support, max.chunk.MB` | See `negbinomial`.

### Details

The response Y is zero with probability pobs0, or Y has a positive-negative binomial distribution with probability 1-pobs0. Thus 0 < pobs0 < 1, which is modelled as a function of the covariates. The zero-altered negative binomial distribution differs from the zero-inflated negative binomial distribution in that the former has zeros coming from one source, whereas the latter has zeros coming from the negative binomial distribution too. The zero-inflated negative binomial distribution is implemented in the VGAM package. Some people call the zero-altered negative binomial a hurdle model.

For one response/species, by default, the three linear/additive predictors for `zanegbinomial()` are (logit(pobs0), log(munb), log(k))^T. This vector is recycled for multiple species.

The VGAM family function `zanegbinomialff()` has a few changes compared to `zanegbinomial()`. These are: (i) the order of the linear/additive predictors is switched so the negative binomial mean comes first; (ii) argument `onempobs0` is now 1 minus the probability of an observed 0, i.e., the probability of the positive negative binomial distribution, i.e., `onempobs0` is `1-pobs0`; (iii) argument `zero` has a new default so that the `pobs0` is intercept-only by default. Now `zanegbinomialff()` is generally recommended over `zanegbinomial()`. 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`.

The `fitted.values` slot of the fitted object, which should be extracted by the generic function `fitted`, returns the mean mu (default) which is given by

mu = (1-pobs0) * munb / [1 - (k/(k+munb))^k].

If `type.fitted = "pobs0"` then pobs0 is returned.

### Warning

This family function is fragile; it inherits the same difficulties as `posnegbinomial`. Convergence for this VGAM family function seems to depend quite strongly on providing good initial values.

This VGAM family function is computationally expensive and usually runs slowly; setting `trace = TRUE` is useful for monitoring convergence.

Inference obtained from `summary.vglm` and `summary.vgam` may or may not be correct. In particular, the p-values, standard errors and degrees of freedom may need adjustment. Use simulation on artificial data to check that these are reasonable.

### Note

Note this family function allows pobs0 to be modelled as functions of the covariates provided `zero` is set correctly. It is a conditional model, not a mixture model. Simulated Fisher scoring is the algorithm.

This family function effectively combines `posnegbinomial` and `binomialff` into one family function.

This family function can handle multiple responses, e.g., more than one species.

### Author(s)

T. W. Yee

### References

Welsh, A. H., Cunningham, R. B., Donnelly, C. F. and Lindenmayer, D. B. (1996) Modelling the abundances of rare species: statistical models for counts with extra zeros. Ecological Modelling, 88, 297–308.

Yee, T. W. (2014) Reduced-rank vector generalized linear models with two linear predictors. Computational Statistics and Data Analysis, 71, 889–902.

### See Also

`dzanegbin`, `posnegbinomial`, `negbinomial`, `binomialff`, `rposnegbin`, `zinegbinomial`, `zipoisson`, `dnbinom`, `CommonVGAMffArguments`, `simulate.vlm`.

### Examples

    ## Not run:
    zdata <- data.frame(x2 = runif(nn <- 2000))
    zdata <- transform(zdata, pobs0 = logitlink(-1 + 2*x2, inverse = TRUE))
    zdata <- transform(zdata,
             y1 = rzanegbin(nn, munb = exp(0+2*x2), size = exp(1), pobs0 = pobs0),
             y2 = rzanegbin(nn, munb = exp(1+2*x2), size = exp(1), pobs0 = pobs0))
    with(zdata, table(y1))
    with(zdata, table(y2))

    fit <- vglm(cbind(y1, y2) ~ x2, zanegbinomial, data = zdata, trace = TRUE)
    coef(fit, matrix = TRUE)
    head(fitted(fit))
    head(predict(fit))

    ## End(Not run)

[Package VGAM version 1.1-1 Index]