wle.negativebinomial(wle)
wle.negativebinomial()所属R语言包：wle
Robust Estimation in the Negative Binomial Model
负二项式模型的鲁棒估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
wle.negativebinomial is used to robust estimate the proportion parameters via Weighted Likelihood.
wle.negativebinomial稳健估计的比例参数，通过加权似然。
用法----------Usage----------
wle.negativebinomial(x, size, boot=30, group, num.sol=1,
raf="HD", tol=10^(-6), equal=10^(-3),
max.iter=500, verbose=FALSE)
参数----------Arguments----------
参数：x
a vector contain the number of failures which occur in a sequence of Bernoulli trials before a target number of successes size is reached.
一个向量包含数字的序列中的伯努利试验的成功size达到目标前发生的故障。
参数：size
target number of successes.
目标成功的次数。
参数：boot
the number of starting points based on boostrap subsamples to use in the search of the roots.
基于自举子样本的起点，使用在搜索的根的数目。
参数：group
the dimension of the bootstap subsamples. The default value is max(round(length(x)/4),2).
的维度的bootstap子样本。默认值是max(round(length(x)/4),2)。
参数：num.sol
maximum number of roots to be searched.
要搜索的最大根数。
参数：raf
type of Residual adjustment function to be use:
类型的残余调节功能，可以使用：
raf="HD": Hellinger Distance RAF,
raf="HD"：Hellinger距离RAF，
raf="NED": Negative Exponential Disparity RAF,
raf="NED"：负指数差异RAF，
raf="SCHI2": Symmetric Chi-Squared Disparity RAF.
raf="SCHI2"：对称卡方差异皇家空军。
参数：tol
the absolute accuracy to be used to achieve convergence of the algorithm.
要使用的绝对精度实现算法的收敛性。
参数：equal
the absolute value for which two roots are considered the same. (This parameter must be greater than tol).
绝对的值，两个根被认为是相同的。 （此参数必须大于tol）。
参数：max.iter
maximum number of iterations.
最大迭代次数。
参数：verbose
if TRUE warnings are printed.
如果TRUE警告被打印出来。
值----------Value----------
wle.negativebinomial returns an object of class "wle.negativebinomial".
wle.negativebinomial返回一个对象的class"wle.negativebinomial"的。
Only print method is implemented for this class.
只打印的方法来实现这个类。
The object returned by wle.negativebinomial are:
对象返回wle.negativebinomial是：
<table summary="R valueblock"> <tr valign="top"><td>p</td> <td> the estimator of the proportion parameter, one value for each root found.</td></tr> <tr valign="top"><td>tot.weights</td> <td> the sum of the weights divide by the number of observations, one value for each root found.</td></tr> <tr valign="top"><td>weights</td> <td> the weights associated to each observation, one column vector for each root found.</td></tr> <tr valign="top"><td>f.density</td> <td> the non-parametric density estimation.</td></tr> <tr valign="top"><td>m.density</td> <td> the smoothed model.</td></tr> <tr valign="top"><td>delta</td> <td> the Pearson residuals.</td></tr> <tr valign="top"><td>call</td> <td> the match.call().</td></tr> <tr valign="top"><td>tot.sol</td> <td> the number of solutions found.</td></tr> <tr valign="top"><td>not.conv</td> <td> the number of starting points that does not converge after the max.iter iteration are reached.</td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD> p</ TD> <TD>的比例参数的估计，每一根发现。</ TD> </ TR> <tr valign="top"> <TD> tot.weights</ TD> <TD>的权重总和除以观测值的数量，每根的一个值。</ TD> </ TR> <tr valign="top"> <TD> weights </ TD> <TD>相关的权重给每个观察，每一根发现一个列向量。</ TD> </ TR> <tr valign="top"> <TD> f.density </ TD> <TD>非参数密度估计。</ TD> </ TR> <tr valign="top"> <TD> m.density </ TD> <TD>平滑的模型。</ TD> </ TR> <tr valign="top"> <TD> delta </ TD> <TD>的Pearson残差。 </ TD> </ TR> <tr valign="top"> <TD>call </ TD> <TD> match.call（）。</ TD> </ TR> <TR VALIGN = “顶”> <TD> tot.sol </ TD> <TD>找到解决方案的数量。</ TD> </ TR> <tr valign="top"> <TD>not.conv /运输署“<td>达到起点的数目后max.iter迭代不收敛。</ TD> </ TR>
</table>
</ TABLE>
（作者）----------Author(s)----------
Claudio Agostinelli
参考文献----------References----------
Markatou, M., Basu, A., and Lindsay, B.G., (1997) Weighted likelihood estimating equations: The discrete case with applications to logistic regression, Journal of Statistical Planning and Inference, 57, 215-232.
Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.
实例----------Examples----------
library(wle)
set.seed(1234)
x <- rnbinom(20, size=10, prob=0.2)
wle.negativebinomial(x, size=10)
x <- c(rnbinom(20, size=10, prob=0.2),rnbinom(10, size=10, p=0.9))
result <- wle.negativebinomial(x, size=10)
print(result)
plot(result$weights)
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 22:40:24
