Imp.Estimates(boot)
Imp.Estimates()所属R语言包：boot
Importance Sampling Estimates
重要性抽样估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Central moment, tail probability, and quantile estimates for a statistic under importance resampling.
中央的时刻，尾概率，并根据重要性重采样的统计位数的估计。
用法----------Usage----------
imp.moments(boot.out = NULL, index = 1, t = boot.out$t[, index],
w = NULL, def = TRUE, q = NULL)
imp.prob(boot.out = NULL, index = 1, t0 = boot.out$t0[index],
t = boot.out$t[, index], w = NULL, def = TRUE, q = NULL)
imp.quantile(boot.out = NULL, alpha = NULL, index = 1,
t = boot.out$t[, index], w = NULL, def = TRUE, q = NULL)
参数----------Arguments----------
参数：boot.out
A object of class "boot" generated by a call to boot or tilt.boot. Use of these functions makes sense only when the bootstrap resampling used unequal weights for the observations. If the importance weights w are not supplied then boot.out is a required argument. It is also required if t is not supplied.
一个类的对象"boot"调用boot或tilt.boot产生。这些功能的使用，使得只有当引导重采样用于观测不平等的重量感。如果的重要性权重w然后不提供boot.out是一个必需的参数。它也需要t如果不提供。
参数：alpha
The alpha levels for the required quantiles. The default is to calculate the 1%, 2.5%, 5%, 10%, 90%, 95%, 97.5% and 99% quantiles.
α水平所需的位数。默认是1％，2.5％，5％，10％，90％，95％，97.5％和99％分位数计算。
参数：index
The index of the variable of interest in the output of boot.out$statistic. This is not used if the argument t is supplied.
该指数的兴趣在boot.out$statistic输出变量。这是不使用参数t提供。
参数：t0
The values at which tail probability estimates are required. For each value t0[i] the function will estimate the bootstrap cdf evaluated at t0[i]. If imp.prob is called without the argument t0 then the bootstrap cdf evaluated at the observed value of the statistic is found.
尾概率估计值是必需的。对于每一个值t0[i]功能将估计引导CDF在t0[i]评估。如果imp.prob没有参数调用t0然后在观测值的统计评估的引导CDF被发现。
参数：t
The bootstrap replicates of a statistic. By default these are taken from the bootstrap output object boot.out but they can be supplied separately if required (e.g. when the statistic of interest is a function of the calculated values in boot.out). Either boot.out or t must be supplied.
引导复制的一个统计。默认情况下，这些取自引导输出对象boot.out，但他们可以单独提供，如果需要的话（例如，当利益的统计数据是一个计算值boot.out功能）。要么boot.out或t必须提供。
参数：w
The importance resampling weights for the bootstrap replicates. If they are not supplied then boot.out must be supplied, in which case the importance weights are calculated by a call to imp.weights.
复制重新取样重量为引导的重要性。如果他们不提供boot.out必须提供，在这种情况下计算的重要性权重调用imp.weights由。
参数：def
A logical value indicating whether a defensive mixture is to be used for weight calculation. This is used only if w is missing and it is passed unchanged to imp.weights to calculate w.
一个逻辑值，指示是否要使用重量计算混合防守。这是用来只有w失踪，它是通过不变imp.weights算w。
参数：q
A vector of probabilities specifying the resampling distribution from which any estimates should be found. In general this would correspond to the usual bootstrap resampling distribution which gives equal weight to each of the original observations. The estimates depend on this distribution only through the importance weights w so this argument is ignored if w is supplied. If w is missing then q is passed as an argument to imp.weights and used to find w.
指定的任何估计应重新取样分布的概率向量。一般情况下，这将对应于通常的引导重采样的分布提供了平等的重量每个原始观测。这种分布的估计仅取决于通过的重要性权重w所以这个参数将被忽略，如果w提供。然后w如果缺少q到imp.weights参数传递和使用找到w。
值----------Value----------
A list with the following components :
以下组件列表：
参数：alpha
The alpha levels used for the quantiles, if imp.quantile is used.
alpha水平位数，imp.quantile如果使用。
参数：t0
The values at which the tail probabilities are estimated, if imp.prob is used.
尾概率的估计，如果imp.prob用于值。
参数：raw
The raw importance resampling estimates. For imp.moments this has length 2, the first component being the estimate of the mean and the second being the variance estimate. For imp.prob, raw is of the same length as t0, and for imp.quantile it is of the same length as alpha.
原材料的重要性重采样估计。 imp.moments这个长度为2，平均估计和第二方差估计的第一个组件。 imp.prob，raw长度相同t0，imp.quantile它是相同的长度为alpha。
参数：rat
The ratio importance resampling estimates. In this method the weights w are rescaled to have average value one before they are used. The format of this vector is the same as raw.
该比率的重要性重采样估计。在此方法中的权重w是重新调整的平均价值之一，使用前。这个向量的格式是相同的raw。
参数：reg
The regression importance resampling estimates. In this method the weights which are used are derived from a regression of t*w on w. This choice of weights can be shown to minimize the variance of the weights and also the Euclidean distance of the weights from the uniform weights. The format of this vector is the same as raw. </table>
回归的重要性重采样估计。在这种方法的使用权得到回归从t*w的w。可以证明这选择的权重，以尽量减少的重量和欧几里德距离的统一度量衡的重量差异。这个向量的格式是相同的raw。 </ TABLE>
参考文献----------References----------
Bootstrap Methods and Their Application. Cambridge University Press.
mixture distributions. Technometrics, 37, 185–194.
Journal of the American Statistical Association, 83, 709–714.
参见----------See Also----------
boot, exp.tilt, imp.weights, smooth.f, tilt.boot
boot，exp.tilt，imp.weights，smooth.f，tilt.boot
举例----------Examples----------
# Example 9.8 of Davison and Hinkley (1997) requires tilting the [戴维森和欣克利（1997）9.8为例，需要倾斜]
# resampling distribution of the studentized statistic to be centred [重采样分布集中的学生化统计]
# at the observed value of the test statistic, 1.84. In this example[在观测值的测试统计，1.84。在这个例子]
# we show how certain estimates can be found using resamples taken from[我们如何若干估计可使用重新采样取自]
# the tilted distribution.[倾斜的分配。]
grav1 <- gravity[as.numeric(gravity[,2]) >= 7, ]
grav.fun <- function(dat, w, orig) {
strata <- tapply(dat[, 2], as.numeric(dat[, 2]))
d <- dat[, 1]
ns <- tabulate(strata)
w <- w/tapply(w, strata, sum)[strata]
mns <- as.vector(tapply(d * w, strata, sum)) # drop names[下降的名字]
mn2 <- tapply(d * d * w, strata, sum)
s2hat <- sum((mn2 - mns^2)/ns)
c(mns[2] - mns[1], s2hat, (mns[2] - mns[1] - orig)/sqrt(s2hat))
}
grav.z0 <- grav.fun(grav1, rep(1, 26), 0)
grav.L <- empinf(data = grav1, statistic = grav.fun, stype = "w",
strata = grav1[,2], index = 3, orig = grav.z0[1])
grav.tilt <- exp.tilt(grav.L, grav.z0[3], strata = grav1[, 2])
grav.tilt.boot <- boot(grav1, grav.fun, R = 199, stype = "w",
strata = grav1[, 2], weights = grav.tilt$p,
orig = grav.z0[1])
# Since the weights are needed for all calculations, we shall calculate[由于权重计算所有需要，我们应计算]
# them once only.[他们只有一次。]
grav.w <- imp.weights(grav.tilt.boot)
grav.mom <- imp.moments(grav.tilt.boot, w = grav.w, index = 3)
grav.p <- imp.prob(grav.tilt.boot, w = grav.w, index = 3, t0 = grav.z0[3])
unlist(grav.p)
grav.q <- imp.quantile(grav.tilt.boot, w = grav.w, index = 3,
alpha = c(0.9, 0.95, 0.975, 0.99))
as.data.frame(grav.q)
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。
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发表于 2012-2-17 10:07:17
