yip88(VGAM)
yip88()所属R语言包：VGAM
Zero-Inflated Poisson Distribution (Yip (1988) algorithm)
零膨胀泊松分布（业（1988）算法）
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits a zero-inflated Poisson distribution based on Yip (1988).
适合零膨胀泊松分布的基础上业（1988）。
用法----------Usage----------
yip88(link.lambda = "loge", n.arg = NULL)
参数----------Arguments----------
参数：link.lambda
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：n.arg
The total number of observations in the data set. Needed when the response variable has all the zeros deleted from it, so that the number of zeros can be determined.
数据集合中的观测的总数。时所需的响应变量有从它的所有删除的零点，所以可确定零的个数。
Details
详细信息----------Details----------
The method implemented here, Yip (1988), maximizes a conditional likelihood. Consequently, the methodology used here deletes the zeros from the data set, and is thus related to the positive Poisson distribution (where P(Y=0) = 0).
实现的方法在这里，叶（1988年），有条件的可能性最大化。因此，这里所使用的方法删除零点从数据集，因此，有关的正泊松分布（其中P(Y=0) = 0）。
The probability function of Y is 0 with probability phi, and Poisson(lambda) with probability 1-phi. Thus
Y的概率函数，是0的概率phi，和泊松（lambda）的概率1-phi。从而
where W is Poisson(lambda). The mean, (1-phi) * lambda, can be obtained by the extractor function fitted applied to the object.
其中W是泊松（lambda）。的平均值，(1-phi) * lambda，可以通过以下方式获得的提取功能fitted施加到该对象。
This family function treats phi as a scalar. If you want to model both phi and lambda as a function of covariates, try zipoisson.
这间家庭功能将phi作为一个标量。如果你要模拟phi和lambda协变量的函数，试着去zipoisson。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，rrvglm和vgam。
警告----------Warning ----------
Under- or over-flow may occur if the data is ill-conditioned. Yip (1988) only considered phi being a scalar and not modelled as a function of covariates. To get around this limitation, try zipoisson.
不足或过流时可能会出现的数据是病态的。业（1988）只考虑phi是一个标量，而不是作为协变量的函数模型。为了绕过这个限制，尝试zipoisson。
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----------
The data may be inputted in two ways. The first is when the response is a vector of positive values, with the argument n in yip88 specifying the total number of observations. The second is simply include all the data in the response. In this case, the zeros are trimmed off during the computation, and the x and y slots of the object, if assigned, will reflect this.
该数据可以以两种方式输入。首先是时的反应是正面的价值观，一个向量的说法n yip88指定总数的观测，中。第二个是简单地包括在响应中的所有的数据。在这种情况下，零被修剪掉在计算过程中，x和y插槽的对象，如果分配，将反映这一点。
The estimate of phi is placed in the misc slot as @misc$pstr0. However, this estimate is computed only for intercept models, i.e., the formula is of the form y ~ 1.
估计phi misc插槽被放置在@misc$pstr0。然而，这估计是截距模型，即只计算，公式的形式y ~ 1。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Inference about the mean of a Poisson distribution in the presence of a nuisance parameter. The Australian Journal of Statistics, 30, 299–306.
A Bayesian analysis of zero-inflated generalized Poisson model. Computational Statistics & Data Analysis, 42, 37–46.
参见----------See Also----------
zipoisson, Zipois, zapoisson, pospoisson, poissonff, dzipois.
zipoisson，Zipois，zapoisson，pospoisson，poissonff，dzipois。
实例----------Examples----------
phi = 0.35; lambda = 2 # Generate some artificial data[产生一些人为的数据]
y = rzipois(n <- 1000, lambda, phi)
table(y)
# Two equivalent ways of fitting the same model[两种等价的方式安装相同的模型]
fit1 = vglm(y ~ 1, yip88(n = length(y)), subset = y > 0, trace = TRUE)
fit2 = vglm(y ~ 1, yip88, trace = TRUE, crit = "c")
(true.mean = (1-phi) * lambda)
mean(y)
head(fitted(fit1))
fit1@misc$phi # The estimate of phi[估计披]
# Compare the ZIP with the positive Poisson distribution [比较与正泊松分布的ZIP]
pp = vglm(y ~ 1, pospoisson, subset = y > 0, trace = TRUE, crit = "c")
coef(pp)
Coef(pp)
coef(fit1) - coef(pp) # Same [同]
head(fitted(fit1) - fitted(pp)) # Different [不同]
# Another example (Angers and Biswas, 2003) ---------------------[另一个例子（昂热和比斯瓦斯，2003年）---------------------]
abdata = data.frame(y = 0:7, w = c(182, 41, 12, 2, 2, 0, 0, 1))
abdata = subset(abdata, w > 0)
yy = with(abdata, rep(y, w))
fit3 = vglm(yy ~ 1, yip88(n = length(yy)), subset = yy > 0, trace = TRUE)
fit3@misc$phi # Estimate of phi (they get 0.5154 with SE 0.0707)[估计PHI（他们得到0.5154与0.0707 SE）]
coef(fit3, matrix = TRUE)
Coef(fit3) # Estimate of 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))
mean(yy) # Compare this with fitted(fit3)[与此相比，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。
|
发表于 2012-10-1 15:59:18
