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R语言 SampleSizeMeans包 mudiff.freq()函数中文帮助文档(中英文对照)

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发表于 2012-9-29 21:39:23 | 显示全部楼层 |阅读模式
mudiff.freq(SampleSizeMeans)
mudiff.freq()所属R语言包:SampleSizeMeans

                                        Frequentist sample size determination for differences in normal means
                                         抽样分配样本量确定为正常手段的差异

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

The function mudiff.freq returns the required sample sizes
函数mudiff.freq返回所需的样本量


用法----------Usage----------


mudiff.freq(len, lambda1, lambda2, level = 0.95, equal=TRUE)



参数----------Arguments----------

参数:len
The desired total length of the confidence interval for the difference between the two unknown means
的置信区间所需的总长度之间的差异的两个未知装置


参数:lambda1
Known precision (reciprocal of the variance) for the first population
已知的精度(方差的倒数)第一人口


参数:lambda2
Known precision (reciprocal of the variance) for the second population
已知精度(方差的倒数)为所述第二人口


参数:level
The desired confidence level (e.g., 0.95)
所需的置信水平(例如,0.95)


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 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)最大限度地减少方差,共N1 + N2的意见</ TD> </ TR> <TR> <td ALIGN="LEFT"> </ TD> </ TR> </ TABLE>


Details

详细信息----------Details----------

Assume that a random sample from each of two populations will be collected in order to estimate the difference between two independent normal means. Assume further that the two precisions lambda1 and lambda2 are known (where precision is the reciprocal of the variance). The function mudiff.freq returns the required sample sizes to attain the desired length len and confidence level level for the confidence interval
假设每个两类人群的一个随机样本将被收集,以估计两个独立的正常手段之间的差异。进一步假设,两个精度lambda1和lambda2的是已知的(其中精度的方差的倒数)。函数mudiff.freq返回所需的样本量才能达到所需的长度len和置信水平的置信区间


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.acc, mudiff.alc, 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, mu.freq, mu.acc, mu.alc, mu.modwoc, mu.varknown, mu.mblacc, mu.mblalc, mu.mblmodwoc, mu.mbl.varknown
mudiff.acc,mudiff.alc,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,mu.freq,mu.acc,mu.alc ,mu.modwoc,mu.varknown,mu.mblacc,mu.mblalc,mu.mblmodwoc,mu.mbl.varknown


实例----------Examples----------


#  Suppose variance1 = 2, variance2 = 4[假设variance1 = 2,variance2的= 4]
mudiff.freq(len=0.2, lambda1=1/2, lambda2=1/4)

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。
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