fitGG(gaga)
fitGG()所属R语言包：gaga
Fit GaGa hierarchical model
适合GAGA层次模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Fits GaGa or MiGaGa hierarchical models, either via a fully Bayesian approach or via maximum likelihood.
适合GAGA或分层模型MiGaGa，要么通过充分贝叶斯方法或通过的可能性最大。
用法----------Usage----------
fitGG(x, groups, patterns, equalcv = TRUE, nclust = 1, method = "quickEM", B, priorpar, parini, trace = TRUE)
参数----------Arguments----------
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：patterns
Matrix indicating which groups are put together under each pattern, i.e. the hypotheses to consider for each gene. colnames(patterns) must match the group levels specified in groups. Defaults to two hypotheses: null hypothesis of all groups being equal and full alternative of all groups being different. The function buildPatterns can be used to construct a matrix with all possible patterns.
矩阵表示，这组每个模式下，即假设考虑每一个基因。 colnames(patterns)必须符合指定的组groups水平。默认两个假设：所有团体所有不同群体的平等和充分的替代的零假设。的功能buildPatterns可以用来构建所有可能的模式矩阵。
参数：equalcv
equalcv==TRUE fits model assuming constant CV across groups. equalcv==FALSE compares cv as well as mean expression levels between groups
equalcv==TRUE符合模型假设恒定跨组的简历。 equalcv==FALSE比较CV以及群体之间的平均表达水平
参数：nclust
Number of clusters in the MiGaGa model. nclust corresponds to the GaGa model.
数聚类在MiGaGa模型。 nclust对应的GAGA模型。
参数：method
method=='MH' fits a fully Bayesian model via Metropolis-Hastings posterior sampling. method=='Gibbs' does the same using Gibbs sampling. method=='SA' uses Simulated Annealing to find the posterior mode. method=='EM' finds maximum-likelihood estimates via the expectation-maximization algorithm, but this is currently only implemented for nclust>1. method=='quickEM' is a quicker implementation that only performs 2 optimization steps (see details).
method=='MH'适合通过都市黑斯廷斯后抽样充分贝叶斯模型。 method=='Gibbs'不一样的使用Gibbs抽样。 method=='SA'使用模拟退火找到后的模式。 method=='EM'发现通过期望最大化算法的最大似然估计，但目前仅nclust>1实施。 method=='quickEM'是一个更快的执行，只执行2个优化步骤（见详情）。
参数：B
Number of iterations. For method=='MH' and method=='Gibbs', B is the number of MCMC iterations (defaults to 1000). For method=='SA', B is the number of iterations in the Simulated Annealing scheme (defaults to 200). For method=='EM', B is the maximum number of iterations (defaults to 20).
迭代次数。 method=='MH'和method=='Gibbs'，B是的MCMC迭代（默认为1000）的数量。 method=='SA'，B是迭代模拟退火计划（默认为200）。 method=='EM'，B是最大迭代次数（默认为20）。
参数：priorpar
List with prior parameter values. It must have components a.alpha0,b.alpha0,a.nu,b.nu,a.balpha,b.balpha,a.nualpha,b.nualpha,p.probclus and p.probpat. If missing they are set to non-informative values that are usually reasonable for RMA and GCRMA normalized data.
与之前的参数值列表。它必须有组件a.alpha0,b.alpha0,a.nu,b.nu,a.balpha,b.balpha,a.nualpha,b.nualpha,p.probclus和p.probpat。如果缺少它们设置到非信息值，通常为RMA和GCRMA归数据合理。
参数：parini
list with components a0, nu, balpha, nualpha, probclus and probpat indicating the starting values for the hyper-parameters. If not specified, a method of moments estimate is used.
与组件列表a0，nu，balpha，nualpha，probclus和probpat显示为超参数的初始值。如果没有指定，矩估计的方法是使用。
参数：trace
For trace==TRUE the progress of the model fitting routine is printed.
trace==TRUE印模型拟合程序的进展。
Details
详情----------Details----------
An approximation is used to sample faster from the posterior distribution of the gamma shape parameters and to compute the normalization constants (needed to evaluate the likelihood). These approximations are implemented in rcgamma and mcgamma.
一个近似采样速度更快，从的伽玛形状参数的后验分布，并计算标准化常数（需要评估的可能性）。实施这些近似rcgamma和mcgamma。
The cooling scheme in method=='SA' uses a temperature equal to 1/log(1+i), where i is the iteration number.
在method=='SA'冷却方案使用温度等于1/log(1+i)，其中i是迭代次数。
The EM implementation in method=='quickEM' is a quick EM algorithm that usually delivers hyper-parameter estimates very similar to those obtained via the slower method=='EM'. Additionally, the GaGa model inference has been seen to be robust to moderate changes in the hyper-parameter estimates in most datasets.
在method=='quickEM'的EM实现是一个快速的，通常提供非常相似，通过速度较慢的method=='EM'获得的超参数估计的EM算法。此外，GaGa的模型推断已被看作是强大，中度在大多数数据集的超参数估计的变化。
值----------Value----------
An object of class gagafit, with components
一个组件对象类gagafit
参数：parest
Hyper-parameter estimates. Only returned if method=='EBayes', for method=='Bayes' one must call the function parest after fitGG
超参数估计。仅返回如果method=='EBayes'，method=='Bayes'必须调用函数parest后fitGG
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 log-likelihood evaluated at each MCMC iteration. For method=='EBayes' it is the log-likelihood evaluated at the maximum.
method=='Bayes'它是评价的MCMC迭代在每个log的可能性。 method=='EBayes'这是对数似然的最高评价。
参数：nclust
Same as input argument.
作为输入参数相同。
参数：patterns
Same as input argument, converted to object of class gagahyp.
相同输入参数，转换为对象的类gagahyp。
作者（S）----------Author(s)----------
David Rossell
参考文献----------References----------
flexible hierarchical model for microarray
参见----------See Also----------
parest to estimate hyper-parameters and compute posterior probabilities after a GaGa or MiGaGa fit. findgenes to find differentially expressed genes. classpred to predict the group that a new sample
parest估计超参数和后GAGA或MiGaGa适合计算后验概率。 findgenes寻找差异表达的基因。 classpred预测组，一个新的样本
举例----------Examples----------
library(gaga)
set.seed(10)
n <- 100; m <- c(6,6)
a0 <- 25.5; nu <- 0.109
balpha <- 1.183; nualpha <- 1683
probpat <- c(.95,.05)
xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha,equalcv=TRUE)
x <- exprs(xsim)
#Frequentist fit: EM algorithm to obtain MLE[frequentist适合：EM算法获得MLE的]
groups <- pData(xsim)$group[c(-6,-12)]
patterns <- matrix(c(0,0,0,1),2,2)
colnames(patterns) <- c('group 1','group 2')
gg1 <- fitGG(x[,c(-6,-12)],groups,patterns=patterns,method='EM',trace=FALSE)
gg1 <- parest(gg1,x=x[,c(-6,-12)],groups)
gg1
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 18:20:58
