zanegbinomial(VGAM)
zanegbinomial()所属R语言包：VGAM
Zero-Altered Negative Binomial Distribution
改变零负二项分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a zero-altered negative binomial distribution based on a conditional model involving a binomial distribution and a positive-negative binomial distribution.
适合一零改变负二项分布，二项式分布和正负二项分布有条件的模型的基础上。
用法----------Usage----------
zanegbinomial(lpobs0 = "logit", lmunb = "loge", lsize = "loge",
epobs0 = list(), emunb = list(), esize = list(),
ipobs0 = NULL, isize = NULL,
zero = c(-1, -3), imethod = 1,
nsimEIM = 250, shrinkage.init = 0.95)
参数----------Arguments----------
参数：lpobs0
Link function for the parameter pobs0, called pobs0 here. See Links for more choices.
链接函数的参数pobs0，pobs0这里。见Links更多的选择。
参数：lmunb
Link function applied to the munb parameter, which is the mean munb of an ordinary negative binomial distribution. See Links for more choices.
Link功能的munb参数，这是的平均munb一个普通的负二项分布。见Links更多的选择。
参数：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.
参数链接功能施加的分散参数的倒数，名为k。那就是，作为k增加，与响应方差减小。见Links更多的选择。
参数：epobs0, emunb, esize
List. Extra argument for the respective links. See earg in Links for general information.
列表。额外的参数，相应的链接。见earg中Links的一般信息。
参数：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.
可选的初始值pobs0和k。如果给出，它是可以得到的一个值通过输入向量，其长度是响应矩阵的列的数量，每一个响应/物种。
参数：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.
整型值向量，分配，例如，-3或3如果概率的观测值的协变量进行建模。指定的三个线性预测模型仅作为一个拦截。默认情况下，k和pobs0参数为每个响应被建模为一个未知的号码，估计。所有参数都可以通过设置zero = NULL，作为解释变量的函数模型。负值意味着，回收的价值，所以设置-3是指所有的k是仅截距。见CommonVGAMffArguments更多信息。
参数：nsimEIM, imethod
See CommonVGAMffArguments.
见CommonVGAMffArguments。
参数：shrinkage.init
See negbinomial and CommonVGAMffArguments.
见negbinomial和CommonVGAMffArguments。
Details
详细信息----------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.
的响应Y的概率是零pobs0或Y有积极的负二项分布的概率1-pobs0。因此0 < pobs0 < 1，这是仿照作为协变量的函数。从零膨胀负二项分布中，改变零负二项分布不同，前者有零点来自一个来源，而后者也来自负二项分布的零。 VGAM包中实现零膨胀负二项分布。有些人称之为零负二项分布改变的一道坎模型。
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.
一个反应/种，默认情况下，三个线性/添加剂的预测是(logit(pobs0), log(munb), log(k))^T。这个向量被回收多个品种。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，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
fitted.values插槽拟合的对象，应提取的通用函数fitted，返回的平均mu由下式给出
警告----------Warning ----------
Convergence for this VGAM family function seems to depend quite strongly on providing good initial values.
此VGAM家庭功能的融合似乎相当强烈依赖，提供了良好的初始值。
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.
推理summary.vglm和summary.vgam可能会或可能不会是正确的。特别是，p-值，标准误差及自由度可能需要调整。使用模拟人工数据检查，这些都是合理的。
注意----------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.
注意：这个家族功能允许pobs0建模为协变量的函数的zero的设置是否正确。这是一个有条件的模式，而不是混合模型。模拟费舍尔得分的算法。
This family function effectively combines posnegbinomial and binomialff into one family function.
这间家庭功能有效地结合posnegbinomial和binomialff成一个大家庭的功能。
This family function can handle a multivariate response, e.g., more than one species.
这间家庭功能，可以处理多变量响应，例如，一个以上的品种。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
D. B. (1996) Modelling the abundances of rare species: statistical models for counts with extra zeros. Ecological Modelling, 88, 297–308.
参见----------See Also----------
dzanegbin, posnegbinomial, negbinomial, binomialff, rposnegbin, zinegbinomial, zipoisson, dnbinom, CommonVGAMffArguments.
dzanegbin，posnegbinomial，negbinomial，binomialff，rposnegbin，zinegbinomial，zipoisson，dnbinom，CommonVGAMffArguments。
实例----------Examples----------
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))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 16:00:11
