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(lpobs0 = "logit", lmunb = "loge", lsize = "loge",
              ipobs0 = NULL,                    isize = NULL,
              zero = c(-1, -3), imethod = 1,
              nsimEIM = 250, shrinkage.init = 0.95)

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.

ipobs0, isize

Optional initial values for pobs0 and k. If given, 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

Integer valued vector, may be assigned, e.g., -3 or 3 if the probability of an observed value is to be modelled with the covariates. Specifies which of the three linear predictors are modelled as an intercept only. By default, the k and pobs0 parameters for each response are modelled as single unknown numbers that are estimated. All parameters can be modelled as a function of the explanatory variables by setting zero = NULL. A negative value means that the value is recycled, so setting -3 means all k are intercept-only. See CommonVGAMffArguments for more information.

nsimEIM, imethod

See CommonVGAMffArguments.

shrinkage.init

See negbinomial and CommonVGAMffArguments.

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 are (logit(pobs0), log(munb), log(k))^T. This vector is recycled for multiple species.

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 which is given by

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

Warning

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 binomialff into one family function.

This family function can handle a multivariate response, 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.

See Also

dzanegbin, posnegbinomial, negbinomial, binomialff, rposnegbin, zinegbinomial, zipoisson, dnbinom, CommonVGAMffArguments.

Examples

## Not run: 
zdata <- data.frame(x2 = runif(nn <- 2000))
zdata <- transform(zdata, pobs0 = logit(-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, zdata, trace = TRUE)
coef(fit, matrix = TRUE)
head(fitted(fit))
head(predict(fit))

## End(Not run)
[Package VGAM version 0.9-0 Index]