ebplots(EBarrays)
ebplots()所属R语言包：EBarrays
Various plotting routines in the EBarrays package
各种绘图例程在EBarrays包
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Various plotting routines, used for diagnostic purposes
各种绘图例程，用于诊断目的
用法----------Usage----------
checkCCV(data, useRank = FALSE, f = 1/2)
checkModel(data, fit, model = c("gamma", "lognormal", "lnnmv"),
number = 9, nb = 10, cluster = 1, groupid = NULL)
checkVarsQQ(data, groupid, ...)
checkVarsMar(data, groupid, xlab, ylab, ...)
plotMarginal(fit, data, kernel = "rect", n = 100,
bw = "nrd0", adjust = 1, xlab, ylab,...)
plotCluster(fit, data, cond = NULL, ncolors = 123, sep=TRUE,
transform=NULL)
## S3 method for class 'ebarraysEMfit'
plot(x, data, plottype="cluster", ...)
参数----------Arguments----------
参数：data
data, as a “matrix” or “ExpressionSet”
数据，作为一个“矩阵”或“ExpressionSet”
参数：useRank
logical. If TRUE, ranks of means and c.v.-s are used in the scatterplot
逻辑。如果TRUE，手段和CV-S的行列中使用散点图
参数：f
passed on to lowess
通过lowess
参数：fit, x
object of class “ebarraysEMfit”, typically produced by a call to emfit
对象类“ebarraysEMfit”，通常调用emfit由生产，
参数：model
which theoretical model use for Q-Q plot. Partial string matching is allowed
其中使用的Q-Q图的理论模型。允许部分字符串匹配
参数：number
number of bins for checking model assumption.
检查模型假设桶的数量。
参数：nb
number of data rows included in each bin for checking model assumption
数据行数，包括检查模型假设在每个垃圾桶
参数：cluster
check model assumption for data in that cluster
检查在该聚类的数据模型假设
参数：groupid
an integer vector indicating which group each sample belongs to. groupid for samples not included in the analysis should be 0.
一个整数向量表示每个样本属于哪个组。对于未列入分析的样本组的groupid应该是0。
参数：kernel, n, bw, adjust
passed on to density
通过density
参数：cond
a vector specifying the condition for each replicate
矢量指定为每个复制的条件
参数：ncolors
different number of colors in the plot
图不同颜色
参数：xlab, ylab
labels for x-axis and y-axis
X轴和Y轴的标签
参数：sep
whether or not to draw horizontal lines between clusters
是否提请聚类之间的水平线
参数：transform
a function to transform the original data in plotting
一个功能转换的原始数据绘制
参数：plottype
a character string specifying the type of the plot. Available options are "cluster" and "marginal". The default plottype "cluster" employs function 'plotCluster' whereas the "marginal" plottype uses function 'plotMarginal'.
一个字符串指定的图类型。可用的选项是“聚类”和“边缘”。的的默认plottype“聚类”采用函数plotCluster“，而”边缘“plottype使用函数plotMarginal”。
参数：...
extra arguments are passed to the qqmath, histogram and xyplot call used to produce the final result
额外的参数传递给qqmath，histogram和xyplot调用用来产生最终结果
Details
详情----------Details----------
checkCCV checks the constant coefficient of variation assumption made in the GG and LNN models. checkModel generates QQ plots for subsets of (log) intensities in a small window. They are used to check the Log-Normal assumption on observation component of the LNN and LNNMV models and the Gamma assumption on observation component of the GG model. checkVarsQQ generates QQ plot for gene specific sample variances. It is used to check the assumption of a scaled inverse chi-square prior on gene specific variances, made in the LNNMV model. checkVarsMar is another diagnostic tool to check this assumption. The density histogram of gene specific sample variances and the density of the scaled inverse chi-square distribution with parameters estimated from data will be plotted. checkMarginal generates predictive marginal distribution from fitted model and compares with estimated marginal (kernel) density of data. Available for the GG and LNN models only. plotCluster generate heatmap for gene expression data with clusters
checkCCV检查在GG和LNN型模型的变化假设的常数。 checkModel（log）的强度，在一个小窗口的子集生成的QQ图。它们被用来检查观察的LNN型和LNNMV模型的组件和GG的模型上观察组件的伽玛假设的对数正态分布的假设。 checkVarsQQ生成QQ特定基因样本方差的图。它是用来检查一个规模逆卡方事先对特定变异基因的作出在LNNMV模型，假设。 checkVarsMar是另一种诊断工具来检查这个假设。将绘制的基因样本方差和规模从数据估计参数逆卡方分布密度的密度直方图。 checkMarginal生成拟合模型的预测从边缘分布，并与估计边际（内核）的数据密度比较。只为GG和LNN型模型。 plotCluster生成与聚类的基因表达数据的热图
值----------Value----------
checkModel, checkVarsQQ and checkVarsMar return an object of class “trellis”, using function in the Lattice package. Note that in certain situations, these may need to be explicitly "print"-ed to have any effect.
checkModel，checkVarsQQ和checkVarsMar返回一个“网格”类的对象，使用在莱迪思包功能。请注意，在某些情况下，这些可能需要明确的print有任何影响。
作者（S）----------Author(s)----------
Ming Yuan, Ping Wang, Deepayan Sarkar, Michael Newton, and Christina Kendziorski
参考文献----------References----------
On differential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology 8:37-52.
On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:3899-3914.
Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.
Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5: 155-176.
gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.
参见----------See Also----------
emfit, lowess
emfit，lowess
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 16:52:01
