mudiff.modwoc(SampleSizeMeans)
mudiff.modwoc()所属R语言包：SampleSizeMeans
Bayesian sample size determination for differences in normal means using the Modified Worst Outcome Criterion
贝叶斯样本量确定为正常的手段，利用经过修改后最坏的结果标准的差异
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function mudiff.modwoc calculates conservative sample sizes, in the sense that the desired posterior credible interval coverage and length for the difference between two normal means
的功能mudiff.modwoc计算保守的大小不一样，在这个意义上所需的后路可信区间的覆盖范围和长度为两个正常手段之间的区别
用法----------Usage----------
mudiff.modwoc(len, alpha1, beta1, alpha2, beta2, n01, n02, level = 0.95, worst.level = 0.95, equal = TRUE, 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：n01
Prior sample size equivalent for the mean for the first population
以前样本量相当于第一人口的平均
参数：n02
Prior sample size equivalent for the mean for the second population
之前第二人口的平均样本量相当于
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：equal
logical. Whether or not the final group sizes (n1, n2) are forced to be equal:<br> <table summary="Rd table"> <tr> <td align="left"> </td><td align="left"></td><td align="left"> when equal = TRUE,</td><td align="left"> final sample sizes n1 = n2;</td> </tr> <tr> <td align="left"> </td><td align="left"></td><td align="left"> when equal = FALSE,</td><td align="left"> final sample sizes (n1, n2) minimize the expected posterior variance given a total of n1+n2 observations</td> </tr> <tr> <td align="left"> </td> </tr> </table>
逻辑。不管是不是最后一组大小（N1，N2）被迫等于：<BR>表summary="Rd table"> <TR> <td ALIGN="LEFT"> </ TD> <TD对齐=“离开“> </ TD> <TD ALIGN="LEFT">当等于= TRUE，</ TD> <TD ALIGN="LEFT">最后的样本量为n1 = n2的; </ TD> </ TR> <TR> <td ALIGN="LEFT"> </ TD> <TD ALIGN="LEFT"> </ TD> <TD ALIGN="LEFT">当等于= FALSE，</ TD> <TD ALIGN="LEFT">最后样本量（N1，N2）降低预期后方差共N1 + N2的意见</ TD> </ TR> <TR> <td ALIGN="LEFT"> </ TD> </ TR> </表>
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. Assume that the means are unknown, but have prior information equivalent to (n01, n02) previous observations, respectively. The function mudiff.modwoc 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 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 fully Bayesian approach to sample size determination. Therefore, the desired coverages and lengths are only realized if the prior distributions input to the function are used for final inferences. Researchers preferring to use the data only for final inferences are encouraged
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。假设的手段是未知的，但有先验信息，相当于以前的意见（N01，N02），分别。函数mudiff.modwoc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。参考参考这个函数使用了一个完全贝叶斯方法确定样本量。因此，只有实现所需的覆盖度和长度，如果先验分布输入到函数用于最终推论。鼓励研究人员喜欢使用的数据为最终推断
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.acc, mudiff.alc, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.varknown, mudiff.mblacc, mudiff.mblalc, mudiff.mblmodwoc, mudiff.mblacc.equalvar, mudiff.mblalc.equalvar, mudiff.mblmodwoc.equalvar, mudiff.mbl.varknown, mudiff.freq, mu.acc, mu.alc, mu.modwoc, mu.varknown, mu.mblacc, mu.mblalc, mu.mblmodwoc, mu.mbl.varknown, mu.freq
mudiff.acc，mudiff.alc，mudiff.acc.equalvar，mudiff.alc.equalvar，mudiff.modwoc.equalvar，mudiff.varknown，mudiff.mblacc，mudiff.mblalc，mudiff.mblmodwoc，mudiff.mblacc.equalvar，mudiff.mblalc.equalvar，mudiff.mblmodwoc.equalvar，mudiff.mbl.varknown，mudiff.freq，mu.acc，mu.alc，mu.modwoc ，mu.varknown，mu.mblacc，mu.mblalc，mu.mblmodwoc，mu.mbl.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 21:40:35
