anotaResidOutlierTest(anota)
anotaResidOutlierTest()所属R语言包：anota
Test for normality of residuals
残差正态试验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
One assumption when performing APV is that the residuals from the regressions are normally distributed. anota assesses this by comparing the Q-Q plots of the residuals to envelopes derived by sampling from the normal distribution.
表演波动时的一个假设是，从回归的残差服从正态分布。 anota评估比较残差的QQ图从正态分布抽样而得的信封。
用法----------Usage----------
anotaResidOutlierTest(anotaQcObj=NULL, confInt=0.01, iter=5,
generateSingleGraph=FALSE, nGraphs=200, generateSummaryGraph=TRUE,
residFitPlot=TRUE, useProgBar=TRUE)
参数----------Arguments----------
参数：anotaQcObj
The object returned by anotaPerformQc.
对象返回anotaPerformQc。
参数：confInt
Controls how many samples from the normal distribution will be used to generate the envelope to which the residuals are compared. Default is 0.01 which will generate 99 samples from the normal distribution to compare to the actual residuals.
控制如何从正态分布的样本将被用来产生残差比较的信封。默认值是0.01，这将产生99样本来自正态分布比较实际的残差。
参数：iter
How many times should the analysis be performed? Default is 5 meaning that 5 sets of samples (each with the size controlled by confInt) will be generated. Notice that the summary plotting is only performed for the last set but the percentage of outliers for each iteration can be found in the output object.
多少次，应该进行分析？默认值是5的意义，将产生5套样品与控制由confInt的每个大小。注意总结图只进行了最后一组，但每个迭代的离群值的百分比，可以发现在输出对象。
参数：generateSingleGraph
The analysis is performed per identifier and plots can be generated for each identifier. However, due to the high number of identifiers, a large number of plots will typically be generated. Default is FALSE.
每标识符进行分析和图，可以为每个标识符生成。然而，由于标识符的高数量，大量的图通常会产生。默认为false。
参数：nGraphs
If generateSingleGraph is set to TRUE, nGraphs controls for how many identifiers such single gene graphs will be generated.
，如果generateSingleGraph设置为TRUE，nGraphs控制将产生多少标识符等单一基因图。
参数：generateSummaryGraph
The function can generate a summary graph that shows the envelopes generated by sampling from the normal distribution compared to the obtained values for all genes. Default is TRUE, thus the graph is generated but only from the last iteration.
该功能可以生成汇总图显示所有基因的获得值相比，从正态分布抽样产生的信封。默认值为true，因此图形的生成，但只能从最后一次迭代。
参数：residFitPlot
Generates an output of the fitted values and residuals. Default is TRUE, generate the plot.
产生一个输出的拟合值和残差。默认值为true，生成的图。
参数：useProgBar
Should the progress bar be shown. Default is TRUE, show progress bar.
应该显示进度条。默认值为true，显示进度栏。
Details
详情----------Details----------
The anotaResidOutlierTest function assesses whether the residuals from the per identifier linear regressions of translationally active mRNA level~cytosolic mRNA level+phenoType are normally distributed. anota generates normal Q-Q plots of the residuals. If the residuals are normally distributed, the data quantiles will form a straight diagonal line from bottom left to top right. Because there are typically relatively few data points, anota calculates "envelopes" based on a set of samplings from the normal distribution using the same number of data points as for the true data (Venables and Ripley 1999).To enable a comparison both the actual and the sampled data are centered (mean=0) and scaled (sd=1). The data (both true and sampled) are then sorted and the true sample is compared to the envelopes of the sampled data at each sort position. The result is presented as a Q-Q plot of the true data where the envelopes of the sampled data are indicated. If there are 99 samplings we expect that 1/100 values to be outside the envelopes obtained from the samplings. Thus it is possible to assess if approximately the expected number of outlier residuals are obtained. The result is presented as both a graphical output and an output object.
anotaResidOutlierTest功能评估是否从每标识符线性回归翻译活跃的mRNA水平~胞浆mRNA水平+型残差正态分布。 anota残差产生正常的QQ图。如果残差是正态分布，数据位数，将形成一个从底部直左右上角的对角线。因为通常有相对较少的数据点，anota计算一组使用相同数量的真实数据（1999年维纳布尔斯和雷普利）的数据点从正态分布抽样的基础上的“信封”。为了使比较两个实际采样数据中心（0）和缩放（SD = 1）。然后排序的数据（包括真实的和采样）和真正的样品相比，在每个排序位置的采样数据的信封。结果是作为QQ的真实数据采样数据的信封表示的图。如果有99抽样调查，我们预计，1/100的值是从抽样调查获得的信封外。因此，它是可能的，以评估是否得到约离群残差预期。结果是作为一个图形输出和输出对象。
值----------Value----------
anotaResdiOutlierTest generates a graphical output ("ANOTA_residual_distribution_summary.pdf") showing the Q-Q plots from all genes as well as the envelopes from the sampled data. The obtained percentage of outliers is shown at each rank position and all combined. Optionally, when the generateSingleGraph is set to TRUE, the function also generates individual plots (stored as "ANOTA_residual_distributions_single.pdf") for n genes (set by nGraphs). When residFitPlot is set to TRUE an output comparing the fitted values to the residuals is generated (stored as "ANOTA_residuals_vs_fitted.jpeg"). An output list object with the following slots is also generated:
anotaResdiOutlierTest生成一个图形输出（“ANOTA_residual_distribution_summary.pdf”），以及从所有基因的信封从采样数据显示QQ图。离群获得的百分比显示在每个排名位置和所有组合。或者，当generateSingleGraph设置为TRUE，函数也产生N基因的个别图（存储“ANOTA_residual_distributions_single.pdf”）（由nGraphs设置）。当residFitPlot被设置为TRUE的输出比较拟合值的残差产生（存储“ANOTA_residuals_vs_fitted.jpeg”）。以下插槽的输出列表中的对象也产生：
参数：confInt
The selected confInt (see function arguments).
的的选择confInt（见函数参数）。
参数：inputResiduals
The residuals used.
使用残差。
参数：rnormIter
The number of sampled data sets.
采样数据集。
参数：outlierMatrixLog
A logical matrix describing which residuals were outliers in the last iteration of the analysis.
一个逻辑矩阵描述，其中残差在最后一次迭代分析离群。
参数：meanOutlierPerIteration
The fraction outliers per iteration.
每次迭代的分数离群。
参数：obtainedComparedToExpected
The ratio of the expected number of outlier residuals compared to the expected number of outliers given the selected confInt.
离群残值的预期数的比率比预期数量的离群给选定的confInt的。
参数：nExpected
Number of expected outlier residuals.
数预期离群残差。
参数：nObtained
Number of obtained outliers residuals.
数量获得离群残差。
作者（S）----------Author(s)----------
Ola Larsson <a href="mailto la.larsson@ki.se">ola.larsson@ki.se</a>, Nahum Sonenberg
<a href="mailto:nahum.sonenberg@mcgill.ca">nahum.sonenberg@mcgill.ca</a>, Robert Nadon <a href="mailto:robert.nadon@mcgill.ca">robert.nadon@mcgill.ca</a>
源----------Source----------
Modern Applied Statistics with S-PLUS. Venables, B.N. and
现代应用统计，S-PLUS。维纳布尔斯，B.N.和
参见----------See Also----------
anotaPerformQc, anotaGetSigGenes, anotaPlotSigGenes
anotaPerformQc，anotaGetSigGenes，anotaPlotSigGenes
举例----------Examples----------
## See example for \code{\link{anotaPlotSigGenes}}[＃请参阅\代码例如{\链接{anotaPlotSigGenes}的}]
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 2012-2-25 12:01:43
