zapoisson(VGAM)
zapoisson()所属R语言包：VGAM
Zero-Altered Poisson Distribution
零改变的泊松分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a zero-altered Poisson distribution based on a conditional model involving a Bernoulli distribution and a positive-Poisson distribution.
适用于一个零的基础上有条件的模型伯努利分配和积极的泊松分布的改变泊松分布。
用法----------Usage----------
zapoisson(lpobs0 = "logit", llambda = "loge", epobs0 = list(),
elambda = list(), zero = NULL)
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：llambda
Link function for the usual lambda parameter. See Links for more choices.
Link功能的的通常是lambda参数。见Links更多的选择。
参数：epobs0, elambda
Extra argument for the respective links. See earg in Links for general information.
额外的参数，相应的链接。见earg中Links的一般信息。
参数：zero
See CommonVGAMffArguments for more information.
见CommonVGAMffArguments更多信息。
Details
详细信息----------Details----------
The response Y is zero with probability pobs0, else Y has a positive-Poisson(lambda) distribution with probability 1-pobs0. Thus 0 < pobs0 < 1, which is modelled as a function of the covariates. The zero-altered Poisson distribution differs from the zero-inflated Poisson distribution in that the former has zeros coming from one source, whereas the latter has zeros coming from the Poisson distribution too. Some people call the zero-altered Poisson a hurdle model.
的响应Y的概率是零pobs0，其他Y具有积极的泊松（lambda)分布概率1-pobs0。因此0 < pobs0 < 1这是仿照作为协变量的函数。改变零的零膨胀泊松分布，泊松分布不同于前有零点来自一个来源，而后者则有来自泊松分布的零。有些人称之为零改变泊松一个障碍模型。
For one response/species, by default, the two linear/additive predictors are (logit(pobs0), log(lambda))^T. Fisher scoring is implemented.
对于一个响应/种，默认情况下，这两个线性/添加剂的预测是(logit(pobs0), log(lambda))^T。费舍尔得分的实施。
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
注意----------Note----------
There are subtle differences between this family function and zipoisson and yip88. In particular, zipoisson is a mixture model whereas zapoisson() and yip88 are conditional models.
这个家庭的功能和zipoisson和yip88之间存在细微的差别。特别是，zipoisson是一个混合模型，而zapoisson()和yip88是有条件的模型。
Note this family function allows pobs0 to be modelled as functions of the covariates.
请注意这个家庭功能允许pobs0被定义成协变量的函数。
This family function effectively combines pospoisson and binomialff into one family function. This family function can handle a multivariate response, e.g., more than one species.
这间家庭功能有效地结合pospoisson和binomialff成一个大家庭的功能。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----------
Lindenmayer, D. B. (1996) Modelling the abundances of rare species: statistical models for counts with extra zeros. Ecological Modelling, 88, 297–308.
A Bayesian analysis of zero-inflated generalized Poisson model. Computational Statistics & Data Analysis, 42, 37–46.
http://www.stat.auckland.ac.nz/~yee contains further information and examples.
参见----------See Also----------
rzapois, zipoisson, pospoisson, posnegbinomial, binomialff, rpospois, CommonVGAMffArguments.
rzapois，zipoisson，pospoisson，posnegbinomial，binomialff，rpospois，CommonVGAMffArguments。
实例----------Examples----------
zapdata <- data.frame(x2 = runif(nn <- 1000))
zapdata <- transform(zapdata, pobs0 = logit( -1 + 1*x2, inverse = TRUE),
lambda = loge(-0.5 + 2*x2, inverse = TRUE))
zapdata <- transform(zapdata, y = rzapois(nn, lambda, pobs0 = pobs0))
with(zapdata, table(y))
fit <- vglm(y ~ x2, zapoisson, zapdata, trace = TRUE)
fit <- vglm(y ~ x2, zapoisson, zapdata, trace = TRUE, crit = "coef")
head(fitted(fit))
head(predict(fit))
head(predict(fit, untransform = TRUE))
coef(fit, matrix = TRUE)
summary(fit)
# Another example ------------------------------[另一个例子------------------------------]
# Data from Angers and Biswas (2003)[数据昂热和比斯瓦斯（2003）]
abdata <- data.frame(y = 0:7, w = c(182, 41, 12, 2, 2, 0, 0, 1))
abdata <- subset(abdata, w > 0)
yy <- with(abdata, rep(y, w))
fit3 <- vglm(yy ~ 1, zapoisson, trace = TRUE, crit = "coef")
coef(fit3, matrix = TRUE)
Coef(fit3) # Estimate lambda (they get 0.6997 with SE 0.1520)[估计λ（他们得到0.6997与0.1501 SE）]
head(fitted(fit3), 1)
mean(yy) # compare this with fitted(fit3)[比较安装（FIT3）]
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 16:00:25
