plotHistDensity(GeneSelectMMD)
plotHistDensity()所属R语言包：GeneSelectMMD
Plot of histogram and density estimate of the pooled gene expression levels.
图直方图和密度汇集的基因表达水平的估计。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Plot of histogram of pooled gene expression levels, composited with density estimate based on the mixture of marginal distributions. The density estimate is based on the assumption that the marginal correlations between subjects are zero.
图直方图密度复合汇集的基因表达水平，估计为基础的边际分布的混合物。密度的估计是基于假设边缘学科之间的相关零。
用法----------Usage----------
plotHistDensity(obj.gsMMD,
plotFlag="case",
plotComponent=FALSE,
myxlab="expression level",
myylab="density",
mytitle="Histogram of gene expression levels\nimposed with estimated density (case)",
x.legend=NULL,
y.legend=NULL,
numPoints=500,
mycol=1:4,
mylty=1:4,
mylwd=rep(3,4),
cex.main=2,
cex.lab=1.5,
cex.axis=1.5,
cex=2,
bty="n")
参数----------Arguments----------
参数：obj.gsMMD
an object returned by gsMMD, gsMMD.default, gsMMD2, or gsMMD2.default
返回的对象gsMMD，gsMMD.default，gsMMD2或gsMMD2.default
参数：plotFlag
logical. Indicate the plot will based on which type of subjects.
逻辑。说明该图将根据哪种类型的科目。
参数：plotComponent
logical. Indicate if components of the mixture of marginal distribution will be plotted.
逻辑。表明，如果将绘制的边缘分布的混合物的组成部分。
参数：myxlab
label for x-axis
x轴的标签
参数：myylab
label for y-axis
Y轴的标签
参数：mytitle
title of the plot
图称号
参数：x.legend
the x-corrdiates of the legend
X-corrdiates的传说
参数：y.legend
the y-corrdiates of the legend
Y-corrdiates的传说
参数：numPoints
logical. Indicate how many genes will be plots.
逻辑。指出多少基因将图。
参数：mycol
color for the density estimates (overall and components)
密度估计的颜色（整体和零部件）
参数：mylty
line styles for the density estimates (overall and components)
线条样式的密度估计（整体和组成部分）
参数：mylwd
line width for the density estimates (overall and components)
线密度的宽度估计（整体和零部件）
参数：cex.main
font for main title
主标题字体
参数：cex.lab
font for x- and y-axis labels
x和y轴标签的字体
参数：cex.axis
font for x- and y-axis
x和y轴的字体
参数：cex
font for texts
为文本字体
参数：bty
the type of box to be drawn around the legend. The allowed values are '"o"' and '"n"' (the default).
框中键入要绘制各地的传说。允许的值是“O”和“N”（默认）。
Details
详情----------Details----------
For a given type of subjects, we pool their expression levels together if the marginal correlations among subjects are zero. We then draw a histogram of the pooled expression levels. Next, we composite density estimates of gene expression levels for the overal distribution and the 3 component distributions.
对于一个给定类型的科目，我们集中表达水平，如果边缘学科之间的相关为零。我们再画一个直方图汇集的表达水平。下一步，我们的复合材料的全部测试分布的基因表达水平和3组分的分布密度估计。
值----------Value----------
A list containing coordinates of the density estimates:
一个列表，其中包含坐标的密度估计：
参数：x
sorted pooled gene expression levels for cases or controls.
排序汇集的情况下或控制的基因表达水平。
参数：x2
a subset of x specified by the sequence: seq(from=1,to=len.x, by=delta), where len.x is the length of the vector x, and delta=floor(len.x/numpoints).
x，其中seq(from=1,to=len.x, by=delta)是长度，矢量len.x和x的：一个delta=floor(len.x/numpoints).指定的序列子集
参数：y
density estimate corresponding to x2
相应的密度估计x2
参数：y1
weighted density estimate for gene cluster 1
1基因簇的加权密度估计
参数：y2
weighted density estimate for gene cluster 2
基因簇2加权密度估计
参数：y3
weighted density estimate for gene cluster 3
基因簇3加权密度估计
注意----------Note----------
The density estimate is obtained based on the assumption that the marginal correlation among subjects is zero. If the estimated marginal correlation obtained by gsMMD is far from zero, then do not use this plot function.
主体之间的边际关系是零假设的基础上得到的密度估计。如果得到的估计边际相关gsMMD远从零，则不要使用此绘图功能。
作者（S）----------Author(s)----------
Weiliang Qiu <a href="mailto:stwxq@channing.harvard.edu">stwxq@channing.harvard.edu</a>,
Wenqing He <a href="mailto:whe@stats.uwo.ca">whe@stats.uwo.ca</a>,
Xiaogang Wang <a href="mailto:stevenw@mathstat.yorku.ca">stevenw@mathstat.yorku.ca</a>,
Ross Lazarus <a href="mailto:ross.lazarus@channing.harvard.edu">ross.lazarus@channing.harvard.edu</a>
参考文献----------References----------
A Marginal Mixture Model for Selecting Differentially Expressed Genes across Two Types of Tissue Samples. The International Journal of Biostatistics. 4(1):Article 20. http://www.bepress.com/ijb/vol4/iss1/20
举例----------Examples----------
## Not run: [＃无法运行：]
library(ALL)
data(ALL)
eSet1 <- ALL[1:100, ALL$BT == "B3" | ALL$BT == "T2"]
mem.str <- as.character(eSet1$BT)
nSubjects <- length(mem.str)
memSubjects <- rep(0,nSubjects)
# B3 coded as 0 (control), T2 coded as 1 (case)[B3的编码为0（对照组），T2的编码为1（情况）]
memSubjects[mem.str == "T2"] <- 1
obj.gsMMD <- gsMMD(eSet1, memSubjects, transformFlag = TRUE,
transformMethod = "boxcox", scaleFlag = TRUE, quiet = FALSE)
plotHistDensity(obj.gsMMD, plotFlag = "case",
mytitle = "Histogram of gene expression levels for T2\nimposed with estimated density (case)",
plotComponent = TRUE,
x.legend = c(0.8, 3),
y.legend = c(0.3, 0.4),
numPoints = 500)
## End(Not run)[＃结束（不运行）]
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 19:00:53
