jack.after.boot(boot)
jack.after.boot()所属R语言包：boot
Jackknife-after-Bootstrap Plots
刀切后，引导图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculates the jackknife influence values from a bootstrap output object and plots the corresponding jackknife-after-bootstrap plot.
此函数计算从一个引导输出对象，并绘制相应折刀后，引导图的的折刀影响值。
用法----------Usage----------
jack.after.boot(boot.out, index = 1, t = NULL, L = NULL,
useJ = TRUE, stinf = TRUE, alpha = NULL,
main = "", ylab = NULL, ...)
参数----------Arguments----------
参数：boot.out
An object of class "boot" which would normally be created by a call to boot. It should represent a nonparametric bootstrap. For reliable results boot.out$R should be reasonably large.
一个类的对象"boot"这通常会被创建由boot调用。它应该代表一种非参数的引导。对于可靠的结果boot.out$R应该是相当大。
参数：index
The index of the statistic of interest in the output of boot.out$statistic.
在boot.out$statistic输出的兴趣统计指数。
参数：t
A vector of length boot.out$R giving the bootstrap replicates of the statistic of interest. This is useful if the statistic of interest is a function of the calculated bootstrap output. If it is not supplied then the default is boot.out$t[,index].
长度boot.out$R引导向量重复统计的兴趣。这是非常有用的，如果感兴趣的统计是计算的引导输出功能。如果没有提供，则默认为boot.out$t[,index]。
参数：L
The empirical influence values for the statistic of interest. These are used only if useJ is FALSE. If they are not supplied and are needed, they are calculated by a call to empinf. If L is supplied then it is assumed that they are the infinitesimal jackknife values.
利益的统计经验的影响值。这些都是用来只有useJ是FALSE。如果他们不提供和需要，他们正在计算由empinf调用。如果L提供再假设他们是无穷的折刀值。
参数：useJ
A logical variable indicating if the jackknife influence values calculated from the bootstrap replicates should be used. If FALSE the empirical influence values are used. The default is TRUE.
应使用一个逻辑变量，指示引导折刀影响计算值复制。如果FALSE使用经验的影响值。默认TRUE。
参数：stinf
A logical variable indicating whether to standardize the jackknife values before plotting them. If TRUE then the jackknife values used are divided by their standard error.
指示是否规范之前，他们图折刀值逻辑变量。如果TRUE然后用折刀值划分其标准错误。
参数：alpha
The quantiles at which the plots are required. The default is c(0.05, 0.1, 0.16, 0.5, 0.84, 0.9, 0.95).
在该图的位数是必需的。默认c(0.05, 0.1, 0.16, 0.5, 0.84, 0.9, 0.95)。
参数：main
A character string giving the main title for the plot.
一个字符串，图的主标题。
参数：ylab
The label for the Y axis. If the default values of alpha are used and ylab is not supplied then a label indicating which percentiles are plotted is used. If alpha is supplied then the default label will not say which percentiles were used.
Y轴的标签。如果alpha使用ylab没有提供，则使用一个标签，说明绘制的百分。默认值如果alpha提供了默认的标签不会说使用的百分。
参数：...
Any extra arguments required by boot.out$statistic. These are required only if useJ is FALSE and L is not supplied, in which case they are passed to empinf for use in calculation of the empirical influence values. </table>
boot.out$statistic所需的任何额外的参数。这些都需要只有useJ是FALSE和L不提供的，在这种情况下，他们通过使用经验的影响值的计算empinf。 </ TABLE>
Details
详情----------Details----------
The centred jackknife quantiles for each observation are estimated from those bootstrap samples in which the particular observation did not appear. These are then plotted against the influence values. If useJ is TRUE then the influence values are found in the same way as the difference between the mean of the statistic in the samples excluding the observations and the mean in all samples. If useJ is FALSE then empirical influence values are calculated by calling empinf.
每个观测中心的折刀位数估计从样本中并未特别观察那些引导。然后这些暗算的影响值。如果useJ是TRUE然后在同样的方式之间的平均统计中不包括样品的意见，并在所有样本的平均值差异的影响值。如果useJ是FALSE然后计算经验的影响值是通过调用empinf。
The resulting plots are useful diagnostic tools for looking at the way individual observations affect the bootstrap output.
由此产生的图是在个人意见影响引导输出的方式寻找有用的诊断工具。
The plot will consist of a number of horizontal dotted lines which correspond to the quantiles of the centred bootstrap distribution. For each data point the quantiles of the bootstrap distribution calculated by omitting that point are plotted against the (possibly standardized) jackknife values. The observation number is printed below the plots. To make it easier to see the effect of omitting points on quantiles, the plotted quantiles are joined by line segments. These plots provide a useful diagnostic tool in establishing the effect of individual observations on the bootstrap distribution. See the references below for some guidelines on the interpretation of the plots.
该图将包括一些中心举分布的分位数对应的水平虚线。每个数据点的计算忽略这一点引导分布的分位数（可能标准化）折刀值暗算。观察号码被印在下面的图。使其更容易看到省略位数百分点的影响，绘制位数加入线段。这些图提供了一个有用的诊断工具，在建立个人意见的影响，引导分布。请参阅下面的参考资料解释的图上的一些准则。
值----------Value----------
There is no returned value but a plot is generated on the current graphics display.
有没有返回值，但生成当前图形上显示的一个图。
副作用----------Side Effects----------
A plot is created on the current graphics device.
当前图形设备上创建一个图。
参考文献----------References----------
functions (with Discussion). Journal of the Royal Statistical Society, B, 54, 83–127.
参见----------See Also----------
boot, empinf
boot，empinf
举例----------Examples----------
# To draw the jackknife-after-bootstrap plot for the head size data as in[提请折刀头大小的数据后，引导图]
# Example 3.24 of Davison and Hinkley (1997)[例如3.24戴维森和欣克利（1997）]
frets.fun <- function(data, i) {
pcorr <- function(x) {
# Function to find the correlations and partial correlations between[函数来查找相关和部分相关关系]
# the four measurements.[四个测量。]
v <- cor(x)
v.d <- diag(var(x))
iv <- solve(v)
iv.d <- sqrt(diag(iv))
iv <- - diag(1/iv.d) %*% iv %*% diag(1/iv.d)
q <- NULL
n <- nrow(v)
for (i in 1 n-1))
q <- rbind( q, c(v[i, 1:i], iv[i,(i+1):n]) )
q <- rbind( q, v[n, ] )
diag(q) <- round(diag(q))
q
}
d <- data[i, ]
v <- pcorr(d)
c(v[1,], v[2,], v[3,], v[4,])
}
frets.boot <- boot(log(as.matrix(frets)), frets.fun, R = 999)
# we will concentrate on the partial correlation between head breadth[我们将集中于头宽之间的偏相关]
# for the first son and head length for the second. This is the 7th[为第一个儿子和第二头长。这是7日]
# element in the output of frets.fun so we set index = 7[我们在输出frets.fun元素，以便设置指数= 7]
jack.after.boot(frets.boot, useJ = FALSE, stinf = FALSE, index = 7)
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 2012-2-16 20:01:32
