zageometric(VGAM)
zageometric()所属R语言包：VGAM
Zero-Altered Geometric Distribution
零几何分布的改变
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a zero-altered geometric distribution based on a conditional model involving a Bernoulli distribution and a positive-geometric distribution.
适合零改变几何分布的基础上有条件的模型伯努利分配和积极的几何分布。
用法----------Usage----------
zageometric(lpobs0 = "logit", lprob = "logit",
epobs0 = list(), eprob = list(),
imethod = 1, ipobs0 = NULL, iprob = NULL, zero = NULL)
参数----------Arguments----------
参数：lpobs0
Link function for the parameter pobs0 or phi, called pobs0 or phi here. See Links for more choices.
链接函数的参数pobs0或phi，pobs0或phi这里。见Links更多的选择。
参数：lprob
Parameter link function applied to the probability of success, called prob or p. See Links for more choices.
参数链接功能适用于成功的概率，被称为prob或p。见Links更多的选择。
参数：epobs0, eprob
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, iprob
Optional initial values for the parameters. If given, they must be in range. For multi-column responses, these are recycled sideways.
可选的初始的参数的值。如果给定的，他们必须在范围内。对于多列的反应，这些被回收横盘整理。
参数：zero, imethod
See CommonVGAMffArguments.
见CommonVGAMffArguments。
Details
详细信息----------Details----------
The response Y is zero with probability pobs0, or Y has a positive-geometric distribution with probability 1-pobs0. Thus 0 < pobs0 < 1, which is modelled as a function of the covariates. The zero-altered geometric distribution differs from the zero-inflated geometric distribution in that the former has zeros coming from one source, whereas the latter has zeros coming from the geometric distribution too. The zero-inflated geometric distribution is implemented in the VGAM package. Some people call the zero-altered geometric a hurdle model.
的响应Y的概率是零pobs0或Y有积极的几何分布的概率1-pobs0。因此0 < pobs0 < 1，这是仿照作为协变量的函数。零改变几何分布不同于零膨胀的几何分布，前者具有来自一个信号源的零，而后者则具有零也来自几何分布。 VGAM包中实现的零膨胀的几何分布。有些人称之为零改变模型几何的一道坎。
The input can be a matrix. By default, the two linear/additive predictors are (log(phi), logit(prob))^T.
该输入可以是一个矩阵。默认情况下，两个线性/添加剂的预测是(log(phi), logit(prob))^T。
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. It is a conditional model, not a mixture model.
请注意这个家庭功能允许pobs0被定义成协变量的函数。这是一个有条件的模式，而不是混合模型。
This family function effectively combines binomialff and posgeometric() and geometric into one family function. However, posgeometric() is not written because it is trivially related to geometric.
这间家庭功能有效地结合binomialff和posgeometric()和geometric成一个大家庭功能。然而，posgeometric()不写，因为它是平凡的相关geometric。
（作者）----------Author(s)----------
T. W. Yee
参见----------See Also----------
dzageom, geometric, zigeometric, dgeom, CommonVGAMffArguments.
dzageom，geometric，zigeometric，dgeom，CommonVGAMffArguments。
实例----------Examples----------
zdata <- data.frame(x2 = runif(nn <- 1000))
zdata <- transform(zdata,
pobs0 = logit(-1 + 2*x2, inverse = TRUE),
prob = logit(-2 + 3*x2, inverse = TRUE))
zdata <- transform(zdata,
y1 = rzageom(nn, prob = prob, pobs0 = pobs0))
with(zdata, table(y1))
fit <- vglm(y1 ~ x2, zageometric, zdata, trace = TRUE)
coef(fit, matrix = TRUE)
head(fitted(fit))
head(predict(fit))
summary(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 15:59:58
