RPA.pointestimate(RPA)
RPA.pointestimate()所属R语言包：RPA
Computing point estimate for the model parameters for all probe
所有探针的计算模型参数的点估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Computes point estimate
计算点估计
用法----------Usage----------
NULL, epsilon = 1e-2, cind = 1, sigma2.method = "robust", d.method =
"fast", verbose = TRUE, bg.method = "rma", normalization.method =
参数----------Arguments----------
参数：abatch
An AffyBatch object.
一个AffyBatch对象。
参数：sets
Specifies the probesets for which RPA estimates will be computed. Default: all probe sets.
指定为爱国估计将计算probesets的。默认：所有探针集。
参数：myseed
Specifies the random seed.
指定随机种子。
参数：priors
Optional list containing hyperparameters alpha and beta of the inverse Gamma prior of the probe-specific variances; alpha is a scalar, common for all probes and probeset; beta is a list where each element is a vector corresponding to one probeset, specifying beta for each probe. Can be used to set user-specified priors for the model parameters. Not applicable for sigma2.method = "var". Noninformative prior is obtained with alpha, beta -> 0. Not used with sigma2.method 'var'. Can be used to regularize the solution with small sample size, or in batch-wise online-updates with large sample size. If priors are not provided for certain probesets (NULL), default priors are used.
阿尔法和测试hyperparameters前逆伽玛探针具体的差异;阿尔法可选列表，其中包含一个标量，共同为所有探针和probeset;测试是一个列表，其中每个元素是一个矢量一个probeset，指定每个测试探针。可以用来为用户指定的先验模型参数。为sigma2.method =“VAR”不适用。无信息之前得到α，β - > 0。不使用sigma2.methodVAR。可以用来规范解决小样本，或在大样本的批次明智的在线更新。如果先验不提供一定probesets（空），默认先验使用。
参数：epsilon
Convergence tolerance. The iteration is deemed converged when the change in the d parameter is < epsilon.
收敛公差。被视为融合时，d参数的变化是<小量的迭代。
参数：cind
Specifies which array in abatch is used as a reference in computing probe-level differential expression.
指定在abatch阵列用于计算探针水平差异表达的参考。
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; this is the preferred method with small sample sizes.
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，这是小样本的首选方法。
参数：verbose
Print progress information during computation. Default: TRUE.
在计算打印进度信息。默认：true。
参数：bg.method
Specify background correction method. Default: "rma". See bgcorrect.methods() for other options.
指定背景校正方法。默认：“RMA”。请参阅其他选项bgcorrect.methods的（）。
参数：normalization.method
Specify quantile normalization method. Default: "pmonly". See normalize.methods(Dilution) for other options.
指定分量的归一化法。默认“pmonly”。看到其他选项normalize.methods（稀释）。
参数：cdf
Specify an alternative CDF environment. Default: none.
指定替代的民防部队环境。默认：无。
Details
详情----------Details----------
Calculates RPA estimates of probe reliability and differential expression between the user-specified reference array (cind) and the other arrays in the data set. The model assumes P observations for each transcript target (i.e. a probeset) with Gaussian noise which is specific for each probe (variance is specified by sigma2). The mean (affinity) parameters of the Gaussian noise model cancel out in calculating probe-level differential expression. RPA.pointestimate gives a point estimate for d and sigma2. The 'prior' parameter is not applicable with sigma2.method = "var". The d.method = "fast" is
计算探针的可靠性和用户指定的参考阵列（cind）和数据集的其他阵列之间的差异表达爱国估计。该模型假定每个成绩单与高斯噪声，这是每个探针（方差由sigma2的规定）的具体目标（即probeset）P级的意见。 （亲和力）的平均高斯噪声模型的参数取消了在计算探针水平的差异表达。 RPA.pointestimate给出点估计为d和sigma2。 “前”参数是不适用sigma2.method =“VAR”。 d.method =“快”是
值----------Value----------
An instance of class 'rpa'. This is an extended list containing the following elements:
类爱国的一个实例。这是一个扩展的列表，其中包含以下内容：
参数：d
A matrix of probesets x arrays. Specifies the estimated 'true' underlying differential gene expression signal over the arrays (vs. the reference array 'cind') for each investigated probeset. Note that the reference array is not included.
probesets x阵列矩阵。指定估计真正的“基础差超过每个调查probeset阵列（与参考阵列”cind）基因表达的信号。请注意，不包括参考阵列。
参数：sigma2
A list. Each element corresponds to a probeset, and contains a vector that gives the estimated variance for each probe in that probeset. This corresponds to the parameter tau^2 in the vignette and manuscript.
一个列表。每个元素对应一个probeset，并包含一个向量，使每个探针在估计方差，probeset。此对应的参数头^ 2中的小插曲和手稿。
参数：cind
Specifies which of the arrays in the abatch (the affybatch object to be analyzed) has been used as the reference for computing probe-level differential expression.
指定阵列中abatch（在affybatch对象进行分析）已被用作计算探针水平的差异表达的参考。
参数：affinity
Probe affinity effects.
探针亲和力的影响。
参数：sets
A character vector listing the investigated probesets.
一个特征向量列出调查probesets。
注意----------Note----------
sigma2.method = "robust" and d.method = "fast" are recommended. With small sample size and informative priors, d.method = "basic" may be preferable, with large sample size d.method = "fast"
sigma2.method =“稳健”和d.method =“快”的建议。小样本和先验信息，d.method =“基本”，可能是可取的，与大样本d.method的“快”
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> rpa.plot, rpa, set.priors, rpa2eset, RPA.preprocess, AffyBatch,
举例----------Examples----------
## Load example data set[＃加载示例数据集]
#require(affydata)[要求（affydata）]
#data(Dilution)[数据（稀释）]
## Compute RPA for whole data set [＃计算整个数据集的爱国]
## ... slow, not executed here [＃...速度慢，不执行]
# rpa.results <- RPA.pointestimate(Dilution)[rpa.results < - RPA.pointestimate（稀释）]
## Visualize the results for one of the probe sets[＃探针集可视化的结果]
#rpa.plot(set, rpa.results)[rpa.plot（套，rpa.results）]
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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发表于 2012-2-26 13:21:34
