getPriors(baySeq)
getPriors()所属R语言包：baySeq
Estimates prior parameters for the underlying distributions of
前参数估计为基础分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
These functions estimate, via maximum likelihood methods, the parameters of the underlying distributions for the different methods of describing the 'count' data.
这些功能，通过最大似然方法，描述的“计数”数据的不同方法的基本分布参数估计。
用法----------Usage----------
getPriors.Dirichlet(cD, samplesize = 1e5, perSE = 1e-1, maxit = 1e6,
verbose = TRUE)
getPriors.Pois(cD, samplesize = 1e5, perSE = 1e-1, takemean = TRUE,
maxit = 1e5, weights = NULL, verbose = TRUE, cl)
getPriors.NB(cD, samplesize = 1e5, samplingSubset = NULL,
equalDispersions = TRUE, estimation = "QL", verbose = TRUE, zeroML =
FALSE, cl, ...)
参数----------Arguments----------
参数：cD
A countData object.
一个countData对象。
参数：samplesize
How large a sample should be taken in estimating the priors?
如何应采取大样本估计先验？
参数：samplingSubset
If given, the priors will be sampled only from the subset specified.
如果给定的，先验将采样只能从指定的子集。
参数：perSE
What should the relative standard error of the estimated parameters fall below?
应该怎样估计参数的相对标准误差低于？
参数：maxit
Over how many iterations (at most) should we take samples and re-estimate the priors in order to achieve convergence?
多少次迭代（最多），我们应该采取样本，并重新评估，以达到收敛的先验？
参数：takemean
If TRUE (recommended), we take the mean of the estimated priors to define a gamma distribution. If FALSE, we use all estimated priors to define an empirical distribtion on the parameters of the gamma distribution.
如果是TRUE（推荐），我们采取平均估计先验定义一个伽玛分布。如果为FALSE，我们使用所有估计的先验定义伽玛分布参数的经验distribtion。
参数：weights
If not NULL, specifies a weighting on the random sampling of rows of the 'cD' object used to estimate parameters of the underlying distribution.
如果不为空，指定'cD'使用的底层分布的参数估计对象的行随机抽样的加权。
参数：equalDispersions
Should we assume equal dispersions of data across all groups in the 'cD' object? Defaults to TRUE; see Details.
我们应该承担平等的跨越在'cD'对象的所有组的数据分散？默认为true;查看详情。
参数：estimation
Defaults to "QL", indicating quasi-likelihood estimation of priors. Currently, the only other possibilities are "ML", a maximum-likelihood method, and "edgeR", the moderated dispersion estimates produced by the 'edgeR' package. See Details.
默认为“QL的”，表示拟似然估计的先验。目前，唯一的可能性是“毫升”，最大似然法，和“磨边机”，“磨边”包生产放缓分散估计。查看详细信息。
参数：verbose
Should status messages be displayed? Defaults to TRUE.
应该会显示状态消息？默认为true。
参数：zeroML
Should parameters from zero data (rows that within a group are all zeros) be estimated using maximum likelihood methods (which will result in zeros in the parameters? See Details.
如果从零数据（行，组内的所有零），使用最大似然方法（这将导致在参数零详细参数估计。
参数：cl
A SNOW cluster object.
雪聚类对象。
参数：...
Additional parameters to be passed to the estimateTagwiseDisp function if 'estimation = "edgeR"'.
要传递给estimateTagwiseDisp函数的附加参数，如果'estimation = "edgeR"'。
Details
详情----------Details----------
These functions empirically estimate prior parameters for different distributions used in estimating posterior likelihoods of each count belonging to a particular group. The choice of which function to use for estimating the prior parameters will depend on the choice of which method is being used to estimate the posterior likelihoods (see getLikelihoods).
这些功能凭经验估计事先估计每项罪名后，属于特定组的似然性的不同分布参数。其中正常使用前参数估计的选择将取决于选择其中的方法被用来估计后的似然性（见getLikelihoods）。
For priors estimated for the negative binomial methods, three options are available. Differences in the options focus on the way in which the dispersion is estimated for the data. In simulation studies, quasi-likelihood methods ('estimation = "QL"') performed best and so these are used by default. Alternatives are maximum-likelihood methods ('estimation = "ML"'), and the 'edgeR' packages moderated dispersion estimates ('estimation = "edgeR"').
先验估计为负二项分布方法，三个选项可供选择。在选项重点分散的数据估计方法上的差异。在模拟研究中，拟似然方法（'estimation = "QL"'）表现最好，所以这些默认使用。替代品的最大似然方法（'estimation = "ML"'），“磨边”包缓和分散估计（'estimation = "edgeR"'）。
The priors estimated for the negative binomial methods ('getPriors.NB') may assume that the dispersion of data for a given row is identical for all group structures defined in 'cD@groups' ('equalDispersions = TRUE'). Alternatively, the dispersions may be estimated individually for each group structure ('equalDispersions = FALSE'). Unless there is a strong reason for believing that the data are differently dispersed between groups, 'equalDispersions = TRUE' is recommended. If 'estimation = "edgeR"' then this parameter is ignored and dispersion is assumed identical for all group structures.
先验估计为负二项分布方法（'getPriors.NB'）可能会认为，对于一个给定的行数据的分散中定义的所有组结构是一致的'cD@groups'（'equalDispersions = TRUE'）。另外，分散剂，可估计分别为各组结构（'equalDispersions = FALSE'）。除非有一个强有力的理由相信，不同群体之间的分散数据，'equalDispersions = TRUE'建议。如果'estimation = "edgeR"'那么这个参数将被忽略和分散假设所有组结构完全相同。
If all counts in a given row for a given group are zero, then maximum and quasi-likelihood estimation methods will result in a zero parameter for the mean. In analyses where segment length is a factor, this makes it hard to differentiate between (for example) a region which contains no reads but is only ten bases long and one which likewise contains no reads but is ten megabases long. If 'zeroML' is FALSE, therefore, the dispersion is set to 1 and the mean estimated as the value that leaves the likelihood of zero data at fifty percent.
如果在一个给定的组给定的行数为零，那么最大拟似然估计方法将导致在一个均值为零的参数。在分析段的长度是一个因素，这使得它很难区分（例如）一个区域，其中包含没有读取，但只有十碱基和一个同样包含没有读，但长10碱基。 'zeroML'如果是FALSE，因此，分散设置为1的平均估计值，叶零数据的可能性在百分之五十。
A 'cluster' object is recommended in order to estimate the priors for the negative binomial distribution. Passing NULL to this variable will cause the function to run in non-parallel mode.
一个“聚类”的对象，建议以先验估计为负二项分布。这个变量传递NULL会导致在非并行模式运行的功能。
getPriors.Dirichlet and getPriors.Pois will issue warnings if the estimation of any priors fails to achieve less than the relative standard error specified in the maximum number of iterations.
getPriors.Dirichlet和getPriors.Pois将发出警告，如果任何先验估计未能达到比相对标准指定的错误的最大迭代次数少。
值----------Value----------
A countData object.
一个countData对象。
作者（S）----------Author(s)----------
Thomas J. Hardcastle
参考文献----------References----------
参见----------See Also----------
countData, getLikelihoods
countData，getLikelihoods
举例----------Examples----------
# See vignette for more examples.[看到更多的例子的小插曲。]
# If we do not wish to parallelise the functions we set the cluster[如果我们不希望我们以parallelise的功能设置聚类]
# object to NULL.[对象为NULL。]
cl <- NULL
# Alternatively, if we have the 'snow' package installed we[另外，如果我们有雪包安装]
# can parallelise the functions. This will usually (not always) offer[可以parallelise的功能。这通常（并不总是）报价]
# significant performance gain.[显着的性能增益。]
## Not run: try(library(snow))[＃无法运行（库（雪））]
## Not run: try(cl <- makeCluster(4, "SOCK"))[＃不运行：尝试（CL < - makeCluster（4，“袜子”））]
# load test data[加载测试数据]
data(simData)
# Create a {countData} object from test data.[创建一个测试数据{countData}对象。]
replicates <- c("simA", "simA", "simA", "simA", "simA", "simB", "simB", "simB", "simB", "simB")
groups <- list(NDE = c(1,1,1,1,1,1,1,1,1,1), DE = c(1,1,1,1,1,2,2,2,2,2))
CD <- new("countData", data = simData, replicates = replicates, groups = groups)
#estimate library sizes for countData object[估计countData对象库大小]
CD@libsizes <- getLibsizes(CD)
# Get priors for negative binomial method[负二项分布方法获取先验]
CDPriors <- getPriors.NB(CD, samplesize = 10^5, estimation = "QL", cl = cl)
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。
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发表于 2012-2-25 12:32:36
