getPosteriors(baySeq)
getPosteriors()所属R语言包：baySeq
An internal function in the baySeq package for calculating
一个内部函数在计算baySeq包
译者：生物统计家园网 机器人LoveR
描述----------Description----------
For likelihoods of the data given a set of models, this function calculates the posterior likelihoods of the models given the data. An internal function of baySeq, which should not in general be called by the user.
似然性的数据给出了一套模型，该函数计算后给出的数据模型的似然性。一个baySeq内部功能，一般不应由用户调用。
用法----------Usage----------
getPosteriors(ps, prs, pET = "none", marginalise = FALSE, groups, priorSubset = NULL, maxit = 100, accuracy =
1e-5, cl = cl)
参数----------Arguments----------
参数：ps
A matrix containing likelihoods of the data for each count (rows) under each model (columns).
每个每种模式下的计数（行）（列）的数据矩阵，包含似然性。
参数：prs
(Initial) prior probabilities for each of the models.
为每个模型的先验概率（初始）。
参数：pET
What type of prior re-estimation should be attempted? Defaults to "none"; "BIC" and "iteratively" are also available.
应该尝试什么类型之前重新估计？也可默认为“无”;“的BIC”和“反复”。
参数：marginalise
Should an attempt be made to numerically marginalise over a prior distribution iteratively estimated from the posterior distribution? Defaults to FALSE, as in general offers little performance gain and increases computational cost considerably.
应该试图进行数值边缘化了从后验分布的迭代估计先验分布？默认为FALSE，一般会提供小的性能增益，大大提高了计算成本。
参数：groups
Group structure from which likelihoods in 'ps' were defined.
集团结构'ps'似然性的定义。
参数：priorSubset
If 'estimatePriors = TRUE', what subset of the data should be used to re-estimate the priors? Defaults to NULL, implying all data will be used.
如果'estimatePriors = TRUE'，什么样的数据子集，应重新估计的先验？默认为null，这意味着所有的数据将被使用。
参数：maxit
What is the maximum number of iterations that should be tried if we are bootstrapping prior probabilities from the data?
什么是应该尝试，如果我们引导先验概率从数据迭代的最大数量是多少？
参数：accuracy
How small should the difference in estimated priors be before we stop bootstrapping.
小应如何估计先验的区别之前，我们停止引导。
参数：cl
A SNOW cluster object.
雪聚类对象。
Details
详情----------Details----------
An internal function, that will not in general be called by the user. It takes the log-likelihoods of the data given the models being tested and returns the posterior likelihoods of the models.
一个内部函数，一般不会由用户调用。它需要被测试的车型数据记录的可能性和返回后的模型似然性。
The function may attempt to estimate the prior likelihoods either by using the Bayesian Information Criterion ('pET = "BIC"') to identify the proportion of the data best explained by each model and taking these proportions as prior. Alternatively, an iterative re-estimation of priors is possible ('pET = "iteratively"', in which an inital estimate for the prior likelihoods of the models is used to calculated the posteriors and then the priors are updated by taking the mean of the posterior likelihoods for each model across all data.
该函数可能会尝试通过使用贝叶斯信息准则（'pET = "BIC"'），以确定最好的解释了每个模型的数据比例，这些比例作为前事先似然性估计。另外，一个迭代的先验估计是可能的（'pET = "iteratively"'，其中>初始模型前似然性估计是用来计算的后验概率，然后以平均后的似然性的先验更新为每个模型的所有数据。
值----------Value----------
A list containing posteriors: estimated posterior likelihoods of the model for each count (log-scale) priors: estimated (or given) prior probabilities of the model
一个列表，其中包含后验：每个计数（记录级）先验模型估计的后验似然性估计模型的先验概率（或）
作者（S）----------Author(s)----------
Thomas J. Hardcastle
参考文献----------References----------
参见----------See Also----------
getLikelihoods
getLikelihoods
举例----------Examples----------
# Simulate some log-likeihoods of data given models (each model[模拟一些给定的数据模型的loglikeihoods（每个模型]
# describes one column of the 'ps' object).[介绍“PS”对象的一列）。]
ps <- log(rbind(
cbind(runif(10000, 0, 0.1), runif(10000, 0.3, 0.9)),
cbind(runif(10000, 0.4, 0.9), runif(1000, 0, 0.2))))
# get posterior log-likelihoods of model, estimating prior likelihoods[登录似然性的模型后，估计之前的似然性]
# of each model from the data.[每个数据模型。]
pps <- getPosteriors(ps, prs <- c(0.5, 0.5), pET = "none", cl =
NULL)
pps$priors
pps$posteriors[1:10,]
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 12:32:26
