postprob(EBarrays)
postprob()所属R语言包：EBarrays
Calculates posterior probabilities for expression patterns
为表达模式计算后验概率
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Takes the output from emfit and calculates the posterior probability of each of the hypotheses, for each gene.
注意到从emfit输出和每个假设的后验概率，计算每个基因。
用法----------Usage----------
postprob(fit, data, ...)
参数----------Arguments----------
参数：fit
output from emfit
从emfit输出
参数：data
a numeric matrix or an object of class “ExpressionSet” containing the data, typically the same one used in the emfit fit supplied below.
数字矩阵或一个类“ExpressionSet”的包含数据，通常使用同一个emfit的对象拟合提供以下。
参数：...
other arguments, ignored
其他参数，忽略
值----------Value----------
An object of class “ebarraysPostProb”. Slot joint is an three dimensional array of probabilities. Each element gives the posterior probability that a gene belongs to certain cluster and have certain pattern. cluster is a matrix of probabilities with number of rows given by the number of genes in data and as many columns as the number of clusters for the fit. pattern is a matrix of probabilities with number of rows given by the number of genes in data and as many columns as the number of patterns for the fit. It additionally contains a slot "hypotheses" containing these hypotheses.
对象一个类“ebarraysPostProb”。槽joint是三个维数组的概率。每个元素赋予后验概率，一个基因属于某些聚类，并有一定的模式。 cluster是由基因data和许多适合的聚类数列的数给出的行数矩阵的可能性。 pattern是基因data和许多适合的模式数量列数行数矩阵的概率。此外，它包含槽“假说”，含有这些假设。
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
emfit
举例----------Examples----------
data(sample.ExpressionSet) ## from Biobase[＃从BIOBASE]
eset <- exprs(sample.ExpressionSet)
patterns <- ebPatterns(c("1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
"1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2"))
gg.fit <- emfit(data = eset, family = "GG", hypotheses = patterns, verbose = TRUE)
prob <- postprob(gg.fit,eset)
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 16:52:22
