cor.me.vector(MeasurementError.cor)
cor.me.vector()所属R语言包：MeasurementError.cor
A function to calculate measurement error model estimates for correlation coefficient betweem two variables
一个函数来计算测量误差模型估计相关系数betweem两个变量
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Given the observed value of two variables and their respective standard error, the measurement error estimate for their correlation coefficient is returned
鉴于两个变量与各自的标准误差的观测值，其相关系数的测量误差估计返回
用法----------Usage----------
cor.me.vector(exp1, se1, exp2, se2)
参数----------Arguments----------
参数：exp1
observed value for vector 1
矢量1的观测值
参数：se1
estimated standard error for vector 1
估计标准误差为向量1
参数：exp2
observed value for vector 2
观测值向量2
参数：se2
estimated standard error for vector 2
估计标准误差向量2
值----------Value----------
参数：estimate
Vecotr containing the estimates from the measurement error model, i.e. cor.me, cor.true, mu1, mu2, s1, s2 which are correlation for the measurement error distribution of the two variables, true correlation between the two variables, and true mean and standard deviation estimates for the two variables respectively
vecotr含有测量误差模型的估计，即cor.me，cor.true，mu1，mu2，s1，s2这是关联两个变量的测量误差分布，真正的两个变量，和真正的均值和标准差之间的相关性，估计分别为这两个变量
参数：count
numer of function and gradient evaluation
numer的功能和梯度评价
参数：convergence
0 if converged. See optim() for details
0如果融合。 OPTIM（详情参见）
注意----------Note----------
Most applicable for microarray expression data where standard errors are readily estimated by most low level analysis softwares. Hence variables can be thought of as genes. One also need to differentiate between cor.me and cor.true: the first one being the correlation between the measurement error distributions of the two genes whereas the second one is the quantity of interest, i.e true correlation between the two gene expression profiles.\
最适用的芯片表达数据标准的错误很容易被估计最低水平分析软件。因此，变量可以被认为是基因。一个也需要，区分cor.me和cor.true：第一个是两个基因，而第二个测量误差分布之间的相关性利益，即两者之间的基因表达图谱的真实关系的数量\
The function involves using quasi-newton for linear optimization, "BFGS" is the only implemented method now.
功能涉及使用线性优化的拟牛顿“的BFGS”是现在唯一的实施方法。
作者（S）----------Author(s)----------
Beiying Ding
参考文献----------References----------
<h3>See Also</h3>
举例----------Examples----------
exp <- matrix(abs(rnorm(200,1000,20)),ncol=10)
se <- matrix(abs(rnorm(200,50,5)),ncol=10)
cor.me.vector(exp[1,],se[1,],exp[2,],se[2,])
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。
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发表于 2012-2-26 00:37:00
