parest(gaga)
parest()所属R语言包:gaga
Parameter estimates and posterior probabilities of differential expression for GaGa and MiGaGa model
参数估计和后验概率为Gaga和MiGaGa模型的差异表达
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Obtains parameter estimates and posterior probabilities of differential expression after a GaGa or MiGaGa model has been fit with the function fitGG.
获得后,已与适合的功能fitGG1 GAGA或MiGaGa模型的参数估计和后验概率的差异表达。
用法----------Usage----------
parest(gg.fit, x, groups, burnin, alpha=.05)
参数----------Arguments----------
参数:gg.fit
GaGa or MiGaGa fit (object of type gagafit, as returned by fitGG).
GAGA或MiGaGa合适的类型gagafit,fitGG返回的对象。
参数:x
ExpressionSet, exprSet, data frame or matrix containing the gene expression measurements used to fit the model.
ExpressionSet,exprSet,数据框或矩阵包含用于拟合模型的基因表达测量。
参数:groups
If x is of type ExpressionSet or exprSet, groups should be the name of the column in pData(x) with the groups that one wishes to compare. If x is a matrix or a data frame, groups should be a vector indicating to which group each column in x corresponds to.
x如果类型ExpressionSet或exprSet,groups应该是列名pData(x)一个愿望比较组。 x如果是一个矩阵或一个数据框,groups应该是哪一组x中的每一列对应的向量。
参数:burnin
Number of MCMC samples to discard. Ignored if gg.fit was fit with the option method=='EBayes'.
丢弃的MCMC样本数量。如果gg.fit适合的选项method=='EBayes'忽略。
参数:alpha
If gg.fit was fit with the option method=='Bayes', parest also computes 1-alpha posterior credibility intervals.
如果gg.fit适合的选项method=='Bayes',parest也计算1-alpha后的信誉间隔。
Details
详情----------Details----------
If gg.fit was fit via MCMC posterior sampling (option method=='Bayes'), parest discards the first burnin iterations and uses the rest to obtain point estimates and credibility intervals for the hyper-parameters. To compute posterior probabilities of differential expression the hyper-parameters are fixed to their estimated value, i.e. not averaged over MCMC iterations.
如果gg.fit适合通过后抽样的MCMC(选项method=='Bayes')parest丢弃第一burnin迭代和使用,其余获得点估计值和可信区间的超参数。计算后的差异表达的概率,其估计价值超参数是固定的,即不平均的MCMC迭代。
值----------Value----------
An object of class gagafit, with components:
类gagafit组件,对象:
参数:parest
Hyper-parameter estimates.
超参数估计。
参数:mcmc
Object of class mcmc with posterior draws for hyper-parameters. Only returned if method=='Bayes'.
Object类的mcmc后路提请超参数。如果method=='Bayes'只返回。
参数:lhood
For method=='Bayes' it is the posterior mean of the log-likelihood. For method=='EBayes' it is the log-likelihood evaluated at the maximum.
method=='Bayes'这是对数似然后平均。 method=='EBayes'这是对数似然的最高评价。
参数:nclust
Number of clusters.
簇的数目。
参数:patterns
Object of class gagahyp indicating which hypotheses (expression patterns) were tested.
类的对象gagahyp表明假设(表达方式)进行了测试。
参数:pp
Matrix with posterior probabilities of differential expression for each gene. Genes are in rows and expression patterns are in columns (e.g. for 2 hypotheses, 1st column is the probability of the null hypothesis and 2nd column for the alternative).
每一个基因的差异表达的后验概率矩阵。行中的基因表达模式在列(2假说例如,第一列是零假设的概率和替代的第二列)。
作者(S)----------Author(s)----------
David Rossell
参考文献----------References----------
flexible hierarchical model for microarray
参见----------See Also----------
fitGG to fit a GaGa or MiGaGa model, findgenes to find differentially expressed genes and posmeansGG to obtain posterior expected expression values.
fitGG适合GAGA或MiGaGa模型,findgenes寻找差异表达的基因和posmeansGG获得后预计表达式的值。
举例----------Examples----------
#Not run[不运行]
#library(EBarrays); data(gould)[库(EBarrays);数据(古尔德)]
#x <- log(exprs(gould)[,-1]) #exclude 1st array[X < - log(exprs(古尔德)[-1])#排除第一阵列]
#groups <- pData(gould)[-1,1][集团< - PDATA(古尔德)[-1,1]]
#patterns <- rbind(rep(0,3),c(0,0,1),c(0,1,1),0:2) #4 hypothesis[< - rbind模式(REP(0,3),C(0,0,1),C(0,1,1),0点02分)#4假说]
#gg <- fitGG(x,groups,patterns,method='EBayes')[GG(X,团体,模式,方法=EBayes)< - fitGG]
#gg[GG]
#gg <- parest(gg,x,groups)[< - parest GG(GG,X组)]
#gg[GG]
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注:
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