estimate.affinities(RPA)
estimate.affinities()所属R语言包：RPA
Probe affinity estimation
探针亲和力估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates probe-specific affinity parameters.
估计探针特异性亲和力参数。
用法----------Usage----------
estimate.affinities(dat, mu)
参数----------Arguments----------
参数：dat
Input data set: probes x samples.
输入一组数据：探针x样品。
参数：mu
Estimated expression signal from RPA model.
爱国模型估计的表达信号。
Details
详情----------Details----------
To estimate means in the original data domain let us assume that each probe-level observation x is of the following form: x = d + mu + noise, where x and d are vectors over samples, mu is a scalar (vector with identical elements) noise is Gaussian with zero mean and probe-specific variance parameters sigma2 Then the parameter mu will indicate how much probe-level observation deviates from the estimated signal shape d. This deviation is further decomposed as mu = mu.real + mu.probe, where mu.real describes the 'real' signal level, common for all probes mu.probe describes probe affinity effect Let us now assume that mu.probe ~ N(0, sigma.probe). This encodes the assumption that in general the affinity effect of each probe tends to be close to zero. Then we just calculate ML estimates of mu.real and mu.probe based on particular assumptions. Note that this part of the algorithm has not been defined in full probabilistic terms yet, just calculating the point estimates.
估计在原始数据域的方式，让我们假设每个探针的观测X是下列形式：X = D +亩+噪声，其中x和d以上样品的向量，亩，是一个标量（向量具有相同的元素）噪声是零均值和探测特定的方差参数sigma2高斯参数万亩然后将表明探针水位观测，从估计的信号形状D偏离多少。这种偏差是进一步分解亩= mu.real + mu.probe，其中mu.real描述为真正的信号水平，共同所有探针mu.probe的描述探针亲和力效应，现在让我们假设，mu.probe N（ 0，sigma.probe）。这编码，在一般情况下，每个探针的亲和力的效果往往是接近零的假设。然后，我们只是计算的mu.real mu.probe似然估计，基于特定的假设。请注意，这部分算法还没有被定义，但在全概率条款，只是计算的点估计。
Note that while sigma2 in RPA measures stochastic noise, and NOT the affinity effect, we use it here as a heuristic solution to weigh the probes according to how much they contribute to the overall signal shape. Intuitively, probes that have little effect on the signal shape (i.e. are very noisy and likely to be contaminated by many unrelated signals) should also contribute less to the absolute signal estimate. If no other prior information is available, using stochastic parameters sigma2 to determine probe weights is likely to work better than simple averaging of the probes without weights. Also in this case the probe affinities sum close to zero but there is some flexibility, and more noisy probes can be downweighted.
请注意，而在爱国sigma2测量随机噪声，不亲和的效果，我们使用它作为一个启发式的解决方案在这里权衡探针，根据多少，他们有助于整体信号的形状。直观，探针，对信号的形状影响不大（即是非常嘈杂和可能被污染的许多无关的信号）也应有助于减少绝对信号估计。如果没有其他先验信息是可用的，使用随机参数sigma2确定探针重量是可能优于简单的平均无重量的探针。也探针亲缘关系的总和，在这种情况下，接近于零，但有一定的灵活性，更嘈杂的探针可以downweighted。
值----------Value----------
A vector with probe-specific affinities.
与探针特异性亲和力的向量。
注意----------Note----------
Affinity estimation is not part of the original RPA procedure in TCBB/IEEE 2011 paper. It is added here since estimates of the absolute levels are often needed in microarray applications. Note that affinity parameters are unidentifiable in the model if no prior assumptions are given. We assume that affinity effects are zero on average, but allow
亲和力的估计是不是原来的爱国TCBB / IEEE 2011文件的程序的一部分。它被添加在这里以来的绝对水平的估计往往需要在芯片应用。注意亲和力参数模型辨认，如果没有事先的假设。我们认为亲和力的影响是平均为零，但允许
作者（S）----------Author(s)----------
Leo Lahti <a href="mailto:leo.lahti@iki.fi">leo.lahti@iki.fi</a>
参考文献----------References----------
<h3>See Also</h3>
举例----------Examples----------
## mu <- estimate.affinities(dat, mu)[＃亩< - estimate.affinities（DAT，亩）]
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 2012-2-26 13:20:07
