mudiff.mblalc.equalvar(SampleSizeMeans)
mudiff.mblalc.equalvar()所属R语言包：SampleSizeMeans
Bayesian sample size determination for differences in normal means when variances are equal using the Mixed Bayesian/Likelihood Average Length Criterion
贝叶斯正常手段的差异样本数的当使用混合贝叶斯/可能性平均长度标准方差相等时，
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function mudiff.mblalc.equalvar returns the required sample sizes
函数mudiff.mblalc.equalvar返回所需的样本量
用法----------Usage----------
mudiff.mblalc.equalvar(len, alpha, beta, level = 0.95)
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：alpha
First prior parameter of the Gamma density for the common precision (reciprocal of the variance)
第一现有的Gamma密度参数（的方差的倒数）为共同的精度
参数：beta
Second prior parameter of the Gamma density for the common precision (reciprocal of the variance)
第二现有的Gamma密度参数（的方差的倒数）为共同的精度
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
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 precisions of the two normal sampling distributions are unknown but equal, with prior information in the form of a Gamma(alpha, beta) density. The function mudiff.mblalc.equalvar 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 Mixed Bayesian/Likelihood (MBL) approach. MBL approaches use the prior information to derive the predictive distribution of the data, but use only the likelihood function for final inferences.
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。假设的两个正常取样分布的精度是未知的，但相等的，与先验信息的形式的γ-（α，β）的密度。函数mudiff.mblalc.equalvar返回所需的样本量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名身份不明的手段。<BR> <BR>该函数使用一个混合的贝叶斯/似然法（MBL）的方法。 MBL方法使用的先验信息，得到的预测分布的数据，但只使用似然函数为最终推断。
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 returned by this function are exact.
这个函数返回的样本大小是准确的。
（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.equalvar, mudiff.mblmodwoc.equalvar, mudiff.mblacc, mudiff.mblalc, mudiff.mblmodwoc, mudiff.mbl.varknown, mudiff.acc.equalvar, mudiff.alc.equalvar, mudiff.modwoc.equalvar, mudiff.acc, mudiff.alc, mudiff.modwoc, 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.equalvar，mudiff.mblmodwoc.equalvar，mudiff.mblacc，mudiff.mblalc，mudiff.mblmodwoc，mudiff.mbl.varknown，mudiff.acc.equalvar，mudiff.alc.equalvar，mudiff.modwoc.equalvar，mudiff.acc，mudiff.alc，mudiff.modwoc，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。
|
发表于 2012-9-29 21:39:56
