rpa.fit(RPA)
rpa.fit()所属R语言包：RPA
rpa.fit
rpa.fit
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits the RPA model, including estimation of probe-specific
适合RPA的模型，包括探针特异性估计
用法----------Usage----------
rpa.fit(dat, cind = 1, epsilon = 1e-2, alpha = NULL, beta = NULL,
sigma2.method = "fast", d.method = "fast")
参数----------Arguments----------
参数：dat
Original data: probes x samples.
原始数据：探针x样品。
参数：cind
Index of reference array.
指数参考阵列。
参数：epsilon
Convergence tolerance. The iteration is deemed converged when the change in all parameters is < epsilon.
收敛公差。被视为融合时，所有参数的变化是<小量的迭代。
参数：alpha, beta
Priors for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with sigma2.method 'var'. Scalar alpha and beta are specify equal inverse Gamma prior for all probes to regularize the solution. The defaults depend on the method.
探针的具体差额逆Gamma分布的先验。无信息之前得到α，β - > 0。不使用sigma2.methodVAR。标α和β是逆伽马指定等于事先规范的解决方案的所有探针。默认值取决于方法。
参数：sigma2.method
Optimization method for sigma2 (probe-specific variances). "robust": (default) update sigma2 by posterior mean, regularized by informative priors that are identical for all probes (user-specified by setting scalar values for alpha, beta). This regularizes the solution, and avoids overfitting where a single probe obtains infinite reliability. This is a potential problem in the other sigma2 update methods with non-informative variance priors. The default values alpha = 2; beta = 1 are used if alpha and beta are not specified. "mode": update sigma2 with posterior mean "mean": update sigma2 with posterior mean "var": update sigma2 with variance around d. Applies the fact that sigma2 cost function converges to variance with large sample sizes.
优化方法sigma2（探针的具体差异）。 “稳健”：（默认）更新后平均sigma2，正规化的先验信息是相同的所有探针（α，β的标值设置为用户指定）。本规范的解决方案，避免过拟合一个探针获得无限的可靠性。这是一个潜在的问题在其他sigma2更新方法与非信息方差先验。默认值，α= 2，β= 1，如果未指定alpha和beta。 “模式”：sigma2更新后平均“的意思”：更新后平均“VAR”sigma2：更新sigma2与周围ð方差。适用的事实，sigma2成本函数收敛大样本的方差。
参数：d.method
Method to optimize d. "fast": (default) weighted mean over the probes, weighted by probe variances The solution converges to this with large sample size. "basic": optimization scheme to find a mode used in Lahti et al. TCBB/IEEE; relatively slow; preferred with small sample size.
方法优化ð。 “快”：（默认）以上的探针，探针解决方案收敛到这个大样本方差加权加权平均。 “基本”：优化方案，以找到在拉赫蒂等使用模式。 TCBB / IEEE相对缓慢;小样本大小的首选。
Details
详情----------Details----------
First learns a point estimate for the RPA model in terms of differential expression values w.r.t. reference sample. After this, probe affinities are estimated by comparing original data and differential expression shape, and setting prior assumptions
首先学习差异表达值WRT一个爱国模型点估计参考样本。在此之后，探针亲和力估计通过比较原始数据和差异表达的形状，并设置事先假设
值----------Value----------
参数：d
Differential signal between reference sample and other samples.
参考样本和其他样本之间的差分信号。
参数：mu
Fitted signal in original data: mu.real + d
装在原始数据信号：mu.real + D
参数：affinity
Probe-specific affinities
探针的具体亲和力
参数：sigma2
Probe-specific stochastic noise
探测特定的随机噪声
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----------
Gene Expression Studies with Short Oligonucleotide Arrays. Lahti et al., TCBB/IEEE 2011. See
参见----------See Also----------
rpa, RPA.pointestimate, estimate.affinities
RPA，RPA.pointestimate，estimate.affinities;
举例----------Examples----------
## res <- rpa.fit(dat, cind, epsilon, alpha, beta, sigma2.method, d.method, affinity.method)[＃RES < - rpa.fit（DAT，cind，ε，α，β，sigma2.method，d.method，affinity.method的）]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-2-26 13:21:07
