imp.weights(boot)
imp.weights()所属R语言包：boot
Importance Sampling Weights
重要性抽样权重
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculates the importance sampling weight required to correct for simulation from a distribution with probabilities p when estimates are required assuming that simulation was from an alternative distribution with probabilities q.
此函数计算的重要性采样的重量，需要从概率分布仿真纠正p假设模拟从概率q替代分配时，估计需要。
用法----------Usage----------
imp.weights(boot.out, def = TRUE, q = NULL)
参数----------Arguments----------
参数：boot.out
A object of class "boot" generated by boot or tilt.boot. Typically the bootstrap simulations would have been done using importance resampling and we wish to do our calculations under the assumption of sampling with equal probabilities.
一个类的对象"boot"产生boot或tilt.boot。通常情况下，引导模拟使用的重要性重采样，具有同等概率的抽样假设下，我们希望做我们的计算已经完成。
参数：def
A logical variable indicating whether the defensive mixture distribution weights should be calculated. This makes sense only in the case where the replicates in boot.out were simulated under a number of different distributions. If this is the case then the defensive mixture weights use a mixture of the distributions used in the bootstrap. The alternative is to calculate the weights for each replicate using knowledge of the distribution from which the bootstrap resample was generated.
一个逻辑变量，指示是否应计算防御的混合分布权重。这使得只有在地方boot.out的不同分布下的模拟复制的意义。如果是这样的话，那么防守的混合权使用用于引导分布的混合物。另一种方法是计算每个重复使用知识的分布，引导重采样产生的重量。
参数：q
A vector of probabilities specifying the resampling distribution from which we require inferences to be made. In general this would correspond to the usual bootstrap resampling distribution which gives equal weight to each of the original observations and this is the default. q must have length equal to the number of observations in the boot.out$data and all elements of q must be positive. </table>
一个指定的重采样分布，从中我们需要作出推论的概率向量。一般情况下，这将符合通常的引导重采样分布提供了平等的重量每个原始观测，这是默认的。 q长度必须相等数量观测boot.out$dataq所有元素必须是积极的。 </ TABLE>
Details
详情----------Details----------
The importance sampling weight for a bootstrap replicate with frequency vector f is given by prod((q/p)^f). This reweights the replicates so that estimates can be found as if the bootstrap resamples were generated according to the probabilities q even though, in fact, they came from the distribution p.
重要性采样重量为引导复制与频率向量f给prod((q/p)^f)。这reweights的复制，这样估计可以发现，如果引导重新采样产生根据概率q尽管，事实上，他们分布p。
值----------Value----------
A vector of importance weights of the same length as boot.out$t. These weights can then be used to reweight boot.out$t so that estimates can be found as if the simulations were from a distribution with probabilities q.
为boot.out$t相同长度的重要性权重向量。这些重量可以然后用来reweight的boot.out$t所以，估计可以发现，如果模拟从分布概率q。
注意----------Note----------
See the example in the help for imp.moments for an example of using imp.weights.
看到例子在imp.moments使用imp.weights的例子的帮助下。
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.moments, smooth.f, tilt.boot
boot，exp.tilt，imp.moments，smooth.f，tilt.boot
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。
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发表于 2012-2-16 17:30:13
