propdiff.acc(SampleSizeProportions)
propdiff.acc()所属R语言包：SampleSizeProportions
Bayesian sample size determination for the difference between two binomial proportions using the Average Coverage Criterion
贝叶斯样品的平均覆盖准则之间的差异两个二项式比例的大小决定
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function propdiff.acc returns the required sample sizes to reach a given coverage probability on average for a posterior credible interval of fixed length for the difference between two binomial proportions.
函数propdiff.acc返回所需的样本量达到一个给定的平均覆盖概率的后固定长度的置信区间为两个二项式比例之间的差异。
用法----------Usage----------
propdiff.acc(len, c1, d1, c2, d2, level = 0.95, equal = TRUE, m = 10000, mcs = 3)
参数----------Arguments----------
参数：len
The fixed length of the posterior credible interval for the difference between the two unknown proportions
后的置信区间的固定长度的两个未知的比例之间的差异
参数：c1
First prior parameter of the Beta density for the binomial proportion for the first population
第一先验参数的Beta密度为二项式第一人口比例
参数：d1
Second prior parameter of the Beta density for the binomial proportion for the first population
二之前的Beta密度参数的二项式第一人口比例
参数：c2
First prior parameter of the Beta density for the binomial proportion for the second population
首先之前的Beta密度参数的二项式第二人口比例
参数：d2
Second prior parameter of the Beta density for the binomial proportion for the second population
二之前的Beta密度参数的二项式第二人口比例
参数：level
The desired average coverage probability of the posterior credible interval (e.g., 0.95)
所需的平均后的可信区间（例如，0.95）的覆盖概率
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 probability coverage of the highest posterior density interval of fixed length len is estimated, in order to approximate the average coverage probability. Usually 10000 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，估计最高后验概率密度间隔固定长度len的概率覆盖，以近似的平均覆盖概率。通常为10000足够了，但在程序运行时间为代价的，可以增加这个数字。
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 binomial proportions. Assume that the proportions have prior information in the form of Beta(c1, d1) and Beta(c2, d2) densities in each population, respectively. The function propdiff.acc returns the required sample sizes to attain the desired average coverage probability level for the posterior credible interval of fixed length len for the difference between the two unknown proportions. <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
假设为了估计之间的差，两个独立的二项式比例，将被收集在一个样品从每两个种群。假设的比例有先验信息的形式的测试（C1，D1）和β（C2，D2）在每个人口密度，分别。函数propdiff.acc返回所需的样本量，以达到所需的平均覆盖概率水平的固定长度len后的置信区间的两名身份不明的比例之间的差异。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>, Patrick Belisle and Roxane du Berger
参考文献----------References----------
Bayesian and mixed Bayesian/likelihood criteria for sample size determination<br>
参见----------See Also----------
propdiff.alc, propdiff.modwoc, propdiff.woc, propdiff.mblacc, propdiff.mblalc, propdiff.mblmodwoc, propdiff.mblwoc
propdiff.alc，propdiff.modwoc，propdiff.woc，propdiff.mblacc，propdiff.mblalc，propdiff.mblmodwoc，propdiff.mblwoc
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。
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发表于 2012-9-29 21:40:57
