ld.design(ldDesign)
ld.design()所属R语言包：ldDesign
Functions for design of experiments to detect linkage disequilibrium
实验设计的功能检测连锁不平衡
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Find the sample size required to detect linkage disequilibrium with a given Bayes factor, with a given power, or find the power of experimental designs to detect linkage equilibrium with a given Bayes factor.
找到所需的样本量与给定的贝叶斯因子，与给定的功率检测连锁不平衡，或发现实验设计，检测与给定的贝叶斯因子连锁平衡的力量。
用法----------Usage----------
ld.design(p, q, D, h2, phi, Bf, power, nmin = 50, nmax = 1e+05, ninterp = 50,
missclass.rate = 0, print.it = FALSE)
ld.power(n, p, q, D, h2, phi, Bf, missclass.rate = 0)
参数----------Arguments----------
参数：n
ld.power: vector of sample sizes
ld.power:向量的样本大小
参数：p
Bi-allelic marker allele frequency
双等位基因标记基因的频率
参数：q
Bi-allelic QTL allele frequency
双等位基因的QTL等位基因频率
参数：D
Linkage disequilibrium coefficient
连锁不平衡系数
参数：h2
QTL "heritability", i.e. proportion of total or phenotypic variance explained by the QTL
QTL的“遗传”，即比例全部或表型变异的QTL解释
参数：phi
Dominance ratio: phi = 0 denotes purely additive, phi = 1 denotes purely dominant allele effects
优势比：phi= 0表示纯粹添加剂，phi = 1表示纯粹是占主导地位的等位基因的影响
参数：Bf
Bayes factor
贝叶斯因子
参数：power
ld.design: Power, or probability of detecting an effect with Bayes factor greater than Bf
ld.design:电源，或概率的检测效果与贝叶斯因子大于的Bf
参数：nmin
ld.design: Lower bound for sample size
ld.design:下界样本大小
参数：nmax
ld.design: Upper bound for sample size
ld.design:上限样本大小
参数：ninterp
ld.design: Number of sample sizes to try
ld.design:的样本大小数尝试
参数：missclass.rate
Proportion of marker values which are missclassified, i.e. incorrect (to allow for genotyping errors)
比例是missclassified，即不正确的标记值（允许基因分型错误）
参数：print.it
If TRUE print results for sample sizes tried
如果样本量的打印结果为TRUE试图
Details
详细信息----------Details----------
These functions implement the method described in Ball (2005) for obtaining the power of designs for detecting linkage disequilibrium with a given Bayes factor. The F values, (and hence significance levels) corresponding to the given Bayes factors, sample sizes, and marker genotype frequecies, are calculated using the method of Spiegelhalter and Smith (1982) (R functions oneway.bf.alpha.required, SS.oneway.bf). The power is obtained using a corrected version of the classical deterministic power calculation from Luo (1988) (R function luo.ld.power).
这些函数实现在球（2005）记载的方法，用于获得电源的设计，用于检测与一个给定的贝叶斯因子连锁不平衡。的F值（并因此显着性水平）对应于给定的贝叶斯因子，样本大小，和标记基因型frequecies，使用Spiegelhalter和Smith（1982）的方法，计算（R函数oneway.bf.alpha.required，SS.oneway.bf ）。电源是罗（1988年）（R函数luo.ld.power使用的修正版本的经典精确的功率计算）。
值----------Value----------
For ld.power, a matrix with columns:
对于ld.power，矩阵的列：
参数：n
Sample sizes
样本量
参数：power
Power of the design with the given sample sizes
电源的设计与给定的样本大小
Additionally the return value has attributes indicating the linkage disequilibrium parameters used. For ld.design the sample size is returned.
此外，返回值表示的连锁不平衡参数使用的属性。对于ld.design返回的样本量。
（作者）----------Author(s)----------
Rod Ball <a href="mailto:rod.ball@scionresearch.com">rod.ball@scionresearch.com</a>
<a href="www.scionresearch.com">www.scionresearch.com</a>
参考文献----------References----------
disequilibrium in unstructured random population association studies. Genetics 170: 859–873.
Chapter 8, pp133–196 In: Association Mapping in Plants, N.C. Oraguzie, E.H.A. Rikkerink, S.E. Gardiner, and H.N. DeSilva (Editors), Springer, New York.
Detecting linkage disequilibrium between a polymorphic marker locus and a trait locus in natural populations. Heredity 80, 198–208
Bayes factors for linear and log-linear models with vague prior information J. Royal Statist Soc. B 44: 377–387.
参见----------See Also----------
luo.ld.power, ld.sim, oneway.bf.alpha,
luo.ld.power，ld.sim，oneway.bf.alpha，
实例----------Examples----------
ld.power(n=seq(100,1000,by=100),p=0.5,q=0.5,D=0.1,h2=0.1,phi=0,Bf=20)
ld.design(p=0.5,q=0.5,D=0.1,h2=0.1,phi=0,Bf=20,power=0.9,print.it=TRUE,nmin=600,nmax=4000)
ld.design(p=0.5,q=0.5,D=0.1,h2=0.1,phi=0,Bf=20,power=0.9,print.it=FALSE,nmin=1700,nmax=1900)
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 2012-9-21 18:18:22
