mudiff.mblmodwoc(SampleSizeMeans)
mudiff.mblmodwoc()所属R语言包：SampleSizeMeans
Bayesian sample size determination for differences in normal means using the Mixed Bayesian/Likelihood Modified Worst Outcome Criterion
贝叶斯确定样本量的差异在正常的手段，使用混合贝叶斯/似然准则修改最坏的结果
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function mudiff.mblmodwoc uses a mixed Bayesian/likelihood approach to determine conservative sample sizes, in the sense that the desired posterior credible interval coverage and length for the difference between two normal means are guaranteed
函数mudiff.mblmodwoc使用的混合贝叶斯/可能性的方法来确定保守的样本量，在这个意义上，所需的后验可信区间覆盖和长度为两个正常手段之间的区别，保证
用法----------Usage----------
mudiff.mblmodwoc(len, alpha1, beta1, alpha2, beta2, level = 0.95, worst.level = 0.95, m = 50000, mcs = 3)
参数----------Arguments----------
参数：len
The desired total length of the posterior credible interval for the difference between the two unknown means
所需的总长度之间的差后的置信区间的两个未知的手段
参数：alpha1
First prior parameter of the Gamma density for the precision (reciprocal of the variance) for the first population
第一现有的Gamma密度参数的精度（方差的倒数）为第一人口
参数：beta1
Second prior parameter of the Gamma density for the precision (reciprocal of the variance) for the first population
第二现有的Gamma密度参数的精度（方差的倒数）为第一人口
参数：alpha2
First prior parameter of the Gamma density for the precision (reciprocal of the variance) for the second population
第一现有的Gamma密度参数的精度（方差的倒数）为所述第二人口
参数：beta2
Second prior parameter of the Gamma density for the precision (reciprocal of the variance) for the second population
第二现有的Gamma密度参数的精度（方差的倒数）为所述第二人口
参数：level
The desired fixed coverage probability of the posterior credible interval (e.g., 0.95)
所需的固定后的可信区间（例如，0.95）的覆盖概率
参数：worst.level
The probability that the length of the posterior credible interval of fixed coverage probability level will be at most len
固定覆盖概率水平后置信区间的长度的概率将至多len
参数：m
The number of points simulated from the preposterior distribution of the data. For each point, the length of the highest posterior density interval of fixed coverage probability level is estimated, in order to approximate the (100*worst.level)%-percentile of the posterior credible interval length. Usually 50000 is sufficient, but one can increase this number at the expense of program running time.
点模拟从的preposterior的分布的数据的数量。对于每一个点，固定覆盖概率水平最高的后验概率密度间隔的长度的估计，以近似的（100 * worst.level）后的可信区间长度的％百分位数。通常50000是足够的，但可以增加这个数字在程序运行时间为代价的。
参数：mcs
The Maximum number of Consecutive Steps allowed in the same direction in the march towards the optimal sample size, before the result for the next upper/lower bound is cross-checked. In our experience, mcs = 3 is a good choice.
允许在同一方向的连续步骤的最佳样本量，在迈向下一个上/下限的结果是交叉检查的最大数量。根据我们的经验，MCS = 3是一个不错的选择。
Details
详细信息----------Details----------
Assume that a sample from each of two populations will be collected in order to estimate the difference between two independent normal means. Assume that the precision within each of the two the populations are unknown, but have prior information in the form of Gamma(alpha1, beta1) and Gamma(alpha2, beta2) densities, respectively. The function mudiff.mblmodwoc returns the required sample sizes to attain the desired length len for the posterior credible interval of fixed coverage probability level for the difference between the two unknown means. The Modified Worst Outcome Criterion used is conservative, in the sense that the posterior credible interval length len is guaranteed over the worst.level proportion of all possible data sets that can arise according to the prior information, for a fixed coverage probability level. <br><br> This function uses a Mixed Bayesian/Likelihood (MBL) approach. MBL approaches use the prior information to derive the predictive distribution of the data, but uses only the likelihood function for final inferences. This approach is intended to satisfy investigators who recognize that prior information is important for planning purposes but prefer to base final
假设以估计之间的差，两个独立的正常手段，将被收集在一个样品从每两个群体。假设的精度内的两个人口是未知的，但有先验信息的形式伽玛（α1，β1）和γ（ALPHA2，β2）的密度，分别。函数mudiff.mblmodwoc返回所需的样本量才能达到所需的长度len后的置信区间的固定覆盖概率水平之间的差异两个未知的手段。修改最坏的结果是保守的标准，在这个意义上，后的置信区间的长度len保证在所有可能的数据集，可能会出现根据先验信息的worst.level比例，一个固定的覆盖概率水平。参考该函数使用一个混合的贝叶斯/似然方法（MBL）。 MBL方法使用的先验信息，得出的预测分布的数据，但只使用了似然函数为最终推断。这种方法是为了满足研究人员承认，之前的信息，规划的目的是重要的，但更喜欢基础最终
值----------Value----------
The required sample sizes (n1, n2) for each group given the inputs to the function.
各组所需的样本量（N1，N2）输入的功能。
注意----------Note----------
The sample sizes are calculated via Monte Carlo simulations, and therefore may vary from one call to the next.
通过Monte Carlo模拟计算样本量，因此可能会有所不同从一个调用到下一个。
（作者）----------Author(s)----------
Lawrence Joseph <a href="mailto:lawrence.joseph@mcgill.ca">lawrence.joseph@mcgill.ca</a> and Patrick Belisle
参考文献----------References----------
Bayesian sample size determination for Normal means and differences between Normal means<br>
参见----------See Also----------
mudiff.mblacc, mudiff.mblalc, mudiff.mblacc.equalvar, mudiff.mblalc.equalvar, mudiff.mblmodwoc.equalvar, mudiff.mbl.varknown, mudiff.acc, mudiff.alc, mudiff.modwoc, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.varknown, mudiff.freq, mu.mblacc, mu.mblalc, mu.mblmodwoc, mu.mbl.varknown, mu.acc, mu.alc, mu.modwoc, mu.varknown, mu.freq
mudiff.mblacc，mudiff.mblalc，mudiff.mblacc.equalvar，mudiff.mblalc.equalvar，mudiff.mblmodwoc.equalvar，mudiff.mbl.varknown，mudiff.acc，mudiff.alc，mudiff.modwoc，mudiff.acc.equalvar，mudiff.alc.equalvar，mudiff.modwoc.equalvar，mudiff.varknown，mudiff.freq，mu.mblacc，mu.mblalc，mu.mblmodwoc ，mu.mbl.varknown，mu.acc，mu.alc，mu.modwoc，mu.varknown，mu.freq
实例----------Examples----------
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 2012-9-29 21:40:20
