abc.ci(boot)
abc.ci()所属R语言包：boot
Nonparametric ABC Confidence Intervals
非参数农行置信区间
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculate equi-tailed two-sided nonparametric approximate bootstrap confidence intervals for a parameter, given a set of data and an estimator of the parameter, using numerical differentiation.
给出了一组数据和参数估计的参数，利用数值微分计算等尾两面近似引导非参数的置信区间。
用法----------Usage----------
abc.ci(data, statistic, index=1, strata=rep(1, n), conf=0.95,
eps=0.001/n, ...)
参数----------Arguments----------
参数：data
A data set expressed as a vector, matrix or data frame.
一个数据集表示为一个向量，矩阵或数据框。
参数：statistic
A function which returns the statistic of interest. The function must take at least 2 arguments; the first argument should be the data and the second a vector of weights. The weights passed to statistic will be normalized to sum to 1 within each stratum. Any other arguments should be passed to abc.ci as part of the ...{} argument.
一个函数，返回的利益统计。函数必须至少有2个参数，第一个参数应该是数据和第二的权重向量。的重量传递到statistic将正常化内各阶层总结1。任何其他参数应通过abc.ci的...{}参数的一部分。
参数：index
If statistic returns a vector of length greater than 1, then this indicates the position of the variable of interest within that vector.
如果statistic返回一个长度大于1的向量，那么这表明向量内，利率的变量的位置。
参数：strata
A factor or numerical vector indicating to which sample each observation belongs in multiple sample problems. The default is the one-sample case.
其中一个因素或数值向量表示样品每个观测属于多个样本问题。默认情况下是一个样本。
参数：conf
A scalar or vector containing the confidence level(s) of the required interval(s).
包含的信心水平所需的时间间隔（S）（S）的一个标量或矢量。
参数：eps
The value of epsilon to be used for the numerical differentiation.
epsilon值，可用于数字分化。
参数：...
Any other arguments for statistic. These will be passed unchanged to statistic each time it is called within abc.ci. </table>
任何statistic的其他参数。这些将通过不变statistic每次被称为时间内abc.ci。 </ TABLE>
Details
详情----------Details----------
This function is based on the function abcnon written by R. Tibshirani. A listing of the original function is available in DiCiccio and Efron (1996). The function uses numerical differentiation for the first and second derivatives of the statistic and then uses these values to approximate the bootstrap BCa intervals. The total number of evaluations of the statistic is 2*n+2+2*length(conf) where n is the number of data points (plus calculation of the original value of the statistic). The function works for the multiple sample case without the need to rewrite the statistic in an artificial form since the stratified normalization is done internally by the function.
此功能是基于功能abcnon由R. Tibshirani书面。上市的原有功能是在DiCiccio和埃弗龙（1996）。功能使用为第一和第二衍生物的统计数值微分，然后使用这些值来近似引导BCA间隔。总数的统计评估是2*n+2+2*length(conf)n是数据点的数量（加上统计的原始价值计算）。的功能，而不需要重写的统计，在一个人造的形式，分层正常化以来的功能是通过内部的多个样品的情况下。
值----------Value----------
A length(conf) by 3 matrix where each row contains the confidence level followed by the lower and upper end-points of the ABC interval at that level.
3矩阵，其中每行包含农行间隔上下终点在这一水平的信心水平。一个“length(conf)。
参考文献----------References----------
Bootstrap Methods and Their Application, Chapter 5. Cambridge University Press.
exponential families. Biometrika, 79, 231–245.
Discussion). Statistical Science, 11, 189–228.
参见----------See Also----------
boot.ci
boot.ci
举例----------Examples----------
# 90% and 95% confidence intervals for the correlation [90％和95％置信区间为相关]
# coefficient between the columns of the bigcity data[之间的bigcity数据列系数]
abc.ci(bigcity, corr, conf=c(0.90,0.95))
# A 95% confidence interval for the difference between the means of[一个95％置信区间的手段之间的区别]
# the last two samples in gravity[在重力的最后两个样本]
mean.diff <- function(y, w)
{ gp1 <- 1:table(as.numeric(y$series))[1]
sum(y[gp1, 1] * w[gp1]) - sum(y[-gp1, 1] * w[-gp1])
}
grav1 <- gravity[as.numeric(gravity[, 2]) >= 7, ]
abc.ci(grav1, mean.diff, strata = grav1$series)
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 2012-2-17 10:00:26
