mudiff.alc(SampleSizeMeans)
mudiff.alc()所属R语言包：SampleSizeMeans
Bayesian sample size determination for differences in normal means using the Average Length Criterion
贝叶斯样本数的正常手段使用的平均长度标准的差异
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function mudiff.alc returns the required sample sizes
函数mudiff.alc返回所需的样本量
用法----------Usage----------
mudiff.alc(len, alpha1, beta1, alpha2, beta2, n01, n02, level = 0.95, equal = TRUE, m = 10000, mcs = 3)
参数----------Arguments----------
参数：len
The desired average length of the posterior credible interval for the difference between the two unknown means
后置信区间之间的差异的两个未知装置所需要的平均长度
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：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 average length. 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，固定覆盖概率水平最高的后验概率密度间隔的长度进行估计，以近似的平均长度。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 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.alc returns the required sample sizes to attain the desired average length len for the posterior credible interval of fixed coverage probability level for the difference between the two unknown means. <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.alc返回所需的样本量，以达到所需的平均长度为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。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.modwoc, 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.modwoc，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。
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发表于 2012-9-29 21:39:16
