Diagnostic plots for globaltest(globaltest)
Diagnostic plots for globaltest()所属R语言包：globaltest
Global Test diagnostic plots
全球诊断测试图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plots to visualize the result of a Global Test in terms of the contributions of the covariates and the subjects.
在协变量和科目的贡献方面的全球测试的结果可视化的图谋。
用法----------Usage----------
covariates(object,
what = c("p-value", "statistic", "z-score", "weighted"),
cluster = "average", alpha = 0.05, sort = TRUE, zoom = FALSE,
legend = TRUE, colors, alias, help.lines = FALSE,
cex.labels = 0.6, pdf, trace)
features(...)
subjects(object,
what = c("p-value", "statistic", "z-score", "weighted"),
cluster = "average", sort = TRUE, mirror = TRUE,
legend = TRUE, colors, alias, help.lines = FALSE,
cex.labels = 0.6, pdf)
参数----------Arguments----------
参数：object
A gt.object, usually created by a call to gt. The object must contain only a single test result, unless the pdf argument is used. See the help page of gt.object on reducing such an object in case it contains more than one test.
一个gt.object，调用gt由通常创建。该对象必须只包含一个单一的测试结果，除非pdf参数。参见帮助页面gt.object上减少此类情况中的对象，它包含多个测试。
参数：what
Gives a choice between various presentations of the same plot. See below under details.
给出了一个选择之间同积的各种介绍。细节见下文。
参数：cluster
The type of hierarchical clustering performed for the dendrogram. Default is average linkage clustering. For other options, see hclust. Setting cluster = "none" or cluster = FALSE suppresses the dendrogram altogether.
层次聚类的类型进行的聚类分析。默认是平均联动聚类。对于其他选项，请参阅hclust。设置cluster = "none"或cluster = FALSE完全抑制树状图。
参数：alpha
Parameter between 0 and 1. Sets the level of family-wise error control in the multiple testing procedure performed on the dendrogram. See below under details.
0和1之间的参数。设置家庭明智的树状图上执行多个测试过程中的差错控制的水平。细节见下文。
参数：sort
If TRUE, the plot sorts the bars with the most significant covariates and subjects to the left, as far as is possible within the constraints of the dendrogram (if present).
如果TRUE，图各种各样的条形最显着的变项和科目的左侧，尽可能树状约束（如果存在）是可能的范围内。
参数：zoom
If TRUE, discards non-significant branches from the dendrogram with the corresponding covariates. This is especially useful for large sets to "zoom" in on the significant results. If no dendrogram is requested, zoom = TRUE discards all covariates that are not significant after Holm multiple testing correction.
如果TRUE，丢弃非显着与相应的协变量的树状分支。大集“放大”在显着的效果，这是特别有用的。如果没有树状要求，zoom = TRUE丢弃所有不霍尔姆多个测试校正后显着的变项。
参数：legend
If TRUE, draws a legend in the plot. To override the default labels of the legend, legend may also be given as a character vector with the labels of the legend.
如果TRUE，提请在小区的一个传奇。要覆盖默认标签的传说，legend也可以得到传说中的标签作为特征向量。
参数：colors
The colors to be used for the bars. See rgb for details on color specification.
用于条形的颜色。看到rgb颜色规范的详细信息。
参数：alias
Optional alternative labels for the bars in the plots. Should be a character vector of the same length as the number of covariates or subjects, respectively.
图条形的可选替代标签。应该的特征向量的长度相同，分别为协变量或对象的数量。
参数：help.lines
If TRUE, prints grey dotted lines that help connect the dendrogram to the bars.
如果TRUE，打印灰色的虚线连接的树状条形。
参数：cex.labels
Magnification factor for the x-axis labels.
放大倍数为x轴的标签。
参数：pdf
Optional filename (character) of the pdf file to which the plots are to be written. If a filename is provided in pdf, many covariates or subjects plots of multiple tests can be made with a single call to covariates or subjects, writing the results to a pdf file.
可选的文件名（character）的PDF文件，该图被写入。如果一个文件名pdf，很多covariates或subjects多个测试图，可以用一个单一的通话covariates或subjects，写结果到一个PDF文件。
参数：trace
If TRUE, prints progress information. Note that printing progress information involves printing of backspace characters, which is not compatible with use of Sweave. Defaults to gt.options()$trace.
如果TRUE，打印进度信息。请注意，打印进度信息涉及印刷退格字符，这是不兼容与Sweave使用的。默认为gt.options ()$trace。
参数：mirror
If TRUE, plots the reverse of the scores for the subjects with negative residual response, so that "good" scores are positive for all subjects.
如果TRUE，图反向为负的剩余反应的科目的成绩，使“良好”的成绩是所有科目的积极。
参数：...
All arguments of features are identical to those of covariates.
论据所有features的是相同covariates。
Details
详情----------Details----------
These two diagnostic plots decompose the test statistics into the contributions of the different covariates and subjects to make the influence of these covariates and subjects visible.
这两个诊断图分解成不同的协变量和对象，使这些协变量和对象可见的影响贡献的检验统计量。
The covariates plot exploits the fact that the global test statistic for a set of alternative covariates can be written as a weighted sum of the global test statistics for each single contributing covariate. By displaying these component global test results in a bar plot the covariates plot gives insight into the subset of covariates that is most responsible for the significant test result. The plot can show the p-values of the component tests on a reversed log scale (the default); their test statistics, with stripes showing their mean and standard deviation under the null hypothesis; the z-scores of these test statistics, standardized to mean zero and standard deviation one; or the weighted test statistics, where the test statistics are multiplied by the relative weight that each covariate carries in the overall test. See the Vignette for more details.
covariates图利用一套替代协变量的全球测试统计，可以作为一个全球测试每个单一的贡献协统计的加权总和的书面事实。通过这些组件的全球测试结果显示，在条形图covariates图洞察到协变量子集，这是最显着的测试结果负责。图可以显示反log规模（默认）的组件测试p-values;他们的测试statistics，带条纹的零假设下的均值和标准差;z-scores 这些测试统计，标准化意味着为零，标准偏差;weighted测试统计，检验统计量乘以相对权重，每个协进行整体测试。看到更多细节的小插曲。
The dendrogram of the covariates plot is based on correlation distance if the directional argument was set to TRUE in the call to gt, and uses absolute correlation distance otherwise. The coloring of the dendrogram is based on the multiple testing procedure of Meinshausen (2008): this procedure controls the family-wise error rate on all 2n-1 hypotheses associated with the subsets of covariates induced by the clustering graph. All significant subsets are colored black; non-significant ones remain grey. This coloring serves as an additional aid to find the subsets of the covariates most contributing to a significant test result.
如果covariates参数设置为directional在调用TRUE树状的gt图相关距离的基础上，否则使用绝对相关距离。基于树状图的着色的Meinshausen多个测试程序（2008）：此过程控制家庭明智的误差率在所有的2n-1聚类图引起的协变量子集相关的假说。所有重要的子集为黑色;非显着的保持灰色。此着色提供额外援助，以找到最有贡献的一个显着的测试结果协变量子集。
The features function is a synonym for covariates, using exactly the same arguments.
features功能是为covariates的代名词，完全使用相同的参数。
The subjects plot exploits the fact that the global test can be written as a sum of contributions of each individual. Each of these contributions is itself a test statistic for the same null hypothesis as the full global test, but one which puts a greater weight on the observed information of a specific subject. These test statistic of subject i is significant if, for the other subjects, similarity in the alternative covariates to subject i tends to coincide with similarity in residual response to subject i. Like the covariates plot, the subjects plot can show the p-values of these component tests on a reversed log scale (the default); their test statistics, with stripes showing their mean and standard deviation under the null hypothesis; the z-scores of these test statistics, standardized to mean zero and standard deviation one; or the weighted test statistics, where the test statistics are multiplied by the relative weight that each covariate carries in the overall test. Setting mirror=FALSE reverses the bars of subjects with a negative residual response (not applicable if p-values are plotted). The resulting statistics values have the additional interpretation that they are proportional to the first order estimates of the linear predictors of each subject under the alternative, i.e. subjects with positive values have higher means under the alternative than under the null, and subjects with negative values have lower means under the alternative than under the null. See the Vignette for more details.
subjects图利用每一个人的贡献的总和，全球测试，可作为书面的事实。这些捐款本身就是一个完整的全球测试一样虚无假设的测试统计，但有一个特定主题的观测资料，这使一个更大的重量。这些主体i检验统计显着的，如果其他科目，在相似的替代品协变量受i往往受i残余响应的相似性不谋而合。 covariates图subjects图一样，可以显示这些组件测试p-values上扭转log的规模（默认）;他们的测试statistics条纹，显示其意味着零假设下的标准偏差;z-scores这些测试统计，标准化意味着为零，标准偏差;或weighted测试统计，检验统计量乘以相对权重由每个协变量进行整体测试。设置mirror=FALSE反转负剩余响应（不适用，如果p-values绘制）科目的条形。由此产生的statistics值有额外的解释，他们是成正比的一阶线性预测每个主题下的替代估计，即正面的价值观科目下比空下的替代手段，科目负值较低下比空下的替代手段。看到更多细节的小插曲。
The dendrogram of the subjects plot is always based on correlation distance. There is no analogue to Meinshausen's multiple testing method for this dendrogram, so multiple testing is not performed.
subjects图树状总是基于相关距离。有没有这个树状Meinshausen多个测试方法的模拟，所以不执行多个测试。
值----------Value----------
If called to make a single plot, the covariates function returns an object of class gt.object. Several methods are available to access this object: see gt.object. The subjects function returns a matrix. If called to make multiple plots, both functions return NULL.
如果调用使一个单一的图，covariates函数返回一个对象类gt.object。有几种方法可用来访问这个对象：看到gt.object。 subjects函数返回一个矩阵。如果调用多个图，这两个函数返回NULL。
注意----------Note----------
The term "z-score" is not meant to imply a normal distribution, but just refers to a studentized score. The z-scores of the subjects plot are asymptotically normal under the null hypothesis; the z-scores of the covariates plot are asymptotically distributed as a chi-squared variable with one degree of freedom.
“Z-得分”并非意味着一个正常的分布，但仅仅是指一个学生化得分。 subjects图的Z-分数的渐近正态性假设下;covariates图的Z-分数的渐近分布作为一个卡方变量与一个自由度。
作者（S）----------Author(s)----------
Jelle Goeman: <a href="mailto:j.j.goeman@lumc.nl">j.j.goeman@lumc.nl</a>; Livio Finos.
参考文献----------References----------
<h3>See Also</h3> Diagnostic plots: <code>covariates</code>, <code>subjects</code>.
举例----------Examples----------
# Simple examples with random data here[简单的例子，与这里的随机数据]
# Real data examples in the Vignette[真实数据的例子中的小插曲]
# Random data: covariates A,B,C are correlated with Y[随机数据：协变量，B，C是与Y相关]
set.seed(1)
Y <- rnorm(20)
X <- matrix(rnorm(200), 20, 10)
X[,1:3] <- X[,1:3] + Y
colnames(X) <- LETTERS[1:10]
# Preparation: test[制备方法：测试]
res <- gt(Y,X)
# Covariates[协变量]
covariates(res)
covariates(res, what = "w")
covariates(res, zoom = TRUE)
# Subjects[主题]
subjects(res)
subjects(res, what = "w", mirror = FALSE)
# Change legend, colors or labels[改变图例，颜色或标签]
covariates(res, legend = c("upregulated", "downregulated"))
covariates(res, col = rainbow(2))
covariates(res, alias = letters[1:10])
# Extract data from the plot[从图中提取数据]
out <- covariates(res)
result(out)
extract(out)
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 2012-2-25 20:42:43
