metahdep.HBLM(metahdep)
metahdep.HBLM()所属R语言包：metahdep
metahdep.HBLM
metahdep.HBLM
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs a meta-analysis by fitting a hierarchical Bayes linear model, allowing for hierarchical dependence.
通过分层贝叶斯线性模型拟合，允许分层依赖执行了一项荟萃分析。
用法----------Usage----------
metahdep.HBLM(theta, V, X, M = NULL, dep.groups = NULL,
meta.name = "meta-analysis", center.X = FALSE,
delta.split = FALSE, n = 10, m = 10,
two.sided = FALSE)
参数----------Arguments----------
参数：theta
A vector of effect size estimates from multiple studies.
规模效应向量估计，从多个研究。
参数：V
The variance/covariance matrix for theta. Typically, this will be block diagonal (to represent any sampling dependence).
theta方差/协方差矩阵。通常情况下，这将是对角块（表示任何采样依赖）。
参数：X
A matrix of covariates for theta. At the very least, this must consist of an intercept term. Other covariates can be included, but there must be more rows than columns in this covariate matrix.
theta协变量矩阵。至少，这必须由截距项。可以包含其他变，但必须有更多的行比协矩阵在此列。
参数：M
(optional) Used when delta.split=TRUE. A block-diagonal matrix describing the hierarchical dependence for the studies (theta). One of two ways to specify this is by using the metahdep.format() function; the other is to use the get.M() function.
（可选）时使用delta.split=TRUE。块对角矩阵，描述层次的研究依赖（theta）。指定两种方式之一是通过使用metahdep.format()函数，另一种是使用get.M()功能。
参数：dep.groups
(optional) Used when delta.split=TRUE. A list of vectors/scalars describing the hierarchical dependence groups for the studies (theta). This is an alternative to passing an M matrix.
（可选）时使用delta.split=TRUE。研究（theta）描述的层次依赖组的矢量/标量列表。这是一个通过M矩阵替代。
参数：meta.name
(optional) A name field for bookkeeping. This can be any character string.
（可选）簿记名外地。这可以是任何字符串。
参数：center.X
(optional) A logical value specifying whether or not to center the columns of X. If TRUE, then the mean from each column will be subtracted from every element in that column (but not for the intercept). This changes the interpretation of the intercept coefficient estimate from the model fit.
（可选）一个逻辑值，指明是否中心X列。如果TRUE，然后从每列平均将会从每个元素中减去该列中（但不拦截）。这改变截距系数的估计，从模型的拟合解释。
参数：delta.split
(optional) A logical value specifying whether or not to account for hierarchical dependence (i.e., perform delta-splitting). If TRUE, then the user needs to pass either a dependence matrix M, or a dep.groups list; i.e., one of M or dep.groups is REQUIRED when delta.split=TRUE.
（可选）指定层次依赖与否的帐户（即一个逻辑值，执行Delta分裂）。如果TRUE，那么用户需要通过一个依赖性矩阵M或dep.groups列表;即，M或dep.groups时，需要一个delta.split=TRUE。
参数：n
(optional) An even integer telling how many steps to use when doing the numerical integration over tau, the square root of the between-study hierarchical variance. The integration is done on the log-logistic prior, split into the 4 quartiles. This number n specifies how many steps to take within each quartile.
（可选）偶数告诉多少步骤做了头，研究之间的层次方差的平方根数值积分时使用。log后勤事先分成4四分，整合完成。这个数字n指定在每个四分多少要采取的步骤。
参数：m
(optional) An even integer telling how many steps to use when doing the numerical integration over varsigma (given tau), the between-study hierarchical covariance. This is only used when delta.split=TRUE. The integration is done on the uniform prior, for each value of tau.
（可选）告诉多少步骤来使用时，做以上varsigma数值积分（定头），研究之间的层次协偶数。这是只用时delta.split=TRUE。整合完成统一之前，每头价值。
参数：two.sided
(optional) A logical value to determine whether to return the 2-sided p-values or default [one-sided positive] posterior probabilities for the parameter estimates.
（可选）一个逻辑值，以确定是否返回双面p值或默认片面的积极参数估计的后验概率。
Details
详情----------Details----------
Takes a vector of effect size estimates, a variance/covariance matrix, and a covariate matrix, and fits a hierarchical Bayes linear model. If delta.split=TRUE, then it performs delta-splitting to account for hierarchical dependence among studies. The main parameters (beta) are given normal priors, the square root of the hierarchical variance (tau) is given a log-logistic prior, and the hierarchical covariance (varsigma) is given a uniform prior; see the Stevens and Taylor reference for details. When a meta-analysis is to be performed for gene expression data (on a per-gene basis), the metahdep() function calls this metahdep.HBLM function for each gene separately.
需要规模效应估计向量，方差/协方差矩阵，和协矩阵，适合分层贝叶斯线性模型。如果delta.split=TRUE，然后执行分裂Delta占研究之间的层次依赖。层次的方差（头）的平方根的主要参数（β）是正常的先验，给出一个log后勤事先和层次协（varsigma）给出一个统一之前，详见史蒂文斯和泰勒参考。当是基因表达数据的每一个基因的基础上进行了一项荟萃分析，metahdep()函数调用这个metahdep.HBLM每个基因分别功能。
值----------Value----------
A list, with the following named components:
一个列表，与下面的命名组件：
参数：beta.hats
A vector of model estimates for the covariates given by X (it may be length 1 i.e. scalar)
X（也可能是长度为1，即标量）给予协变量的模型估计向量
参数：cov.matrix
The variance/covariance matrix for the beta.hats vector
beta.hats向量的方差/协方差矩阵
参数：beta.hat.p.values
The p-value(s) for the beta.hats estimate(s)
P-beta.hats估计值（S）（S）
参数：tau.hat
The posterior mean for tau (not tau-square). An estimate for tau-square is E(square(tau) [given data]) = tau.var + square(tau.hat)
头后平均（不是头方）。一个头方估计是E（方（头）给定的数据）= tau.var +方（tau.hat）
参数：tau.var
The posterior variance for tau (not tau-square).
头后方差（不是头方）。
参数：varsigma.hat
The posterior mean for varsigma.
后意味着varsigma。
参数：varsigma.var
The posterior variance for varsigma.
为varsigma后的差额。
参数：tau.varsigma.cov
The posterior covariance for tau and varsigma.
的头和varsigma的后的协方差。
参数：name
An optional name field
可选名称字段
作者（S）----------Author(s)----------
John R. Stevens, Gabriel Nicholas
参考文献----------References----------
cancer studies in humans and other species, Journal of the American Statistical Association, 78(382), 293-308.
for meta-analysis, in D. K. Stangl and D. A. Berry (Eds.), Meta-analysis in medicine and health policy, pp. 127-178. New York: Marcel Dekker.
from Multiple Microarray Studies, Proceedings of Conference on Applied Statistics in Agriculture, pp. 133-147.
Bioinformatics, 25(19):2619-2620.
Journal of Educational and Behavioral Statistics, 34(1):46-73.
举例----------Examples----------
###[＃]
### Example 1: gene expression data[＃例1：基因表达数据]
### - this uses one gene from the HGU.prep.list object[＃＃ - 这使用从HGU.prep.list对象的一个基因]
# load data and extract components for meta-analysis (for one gene)[数据加载和提取物元分析组件（一个基因）]
data(HGU.prep.list)
gene.data <- HGU.prep.list[[7]]
theta <- gene.data@theta
V <- gene.data@V
X <- gene.data@X
M <- gene.data@M
dep.grps <- list(c(1:2),c(4:6))
gene.name <- gene.data@gene
# fit a regular HBLM (no hierarchical dependence)[适合的定期HBLM（没有层次依赖）]
results <- metahdep.HBLM(theta, V, X, meta.name=gene.name,
center.X=TRUE, two.sided=TRUE)
results
# fit hierarchical dependence model (with delta-splitting), [适应层次的依赖模式（Delta分裂），]
# using two different methods for specifying the dependence structure[使用两种不同的方法指定依赖结构]
results.dsplitM <- metahdep.HBLM(theta, V, X, M, delta.split=TRUE,
meta.name=gene.name, center.X=TRUE, two.sided=TRUE)
results.dsplitM
results.dsplitd <- metahdep.HBLM(theta, V, X, dep.groups=dep.grps,
delta.split=TRUE, meta.name=gene.name, center.X=TRUE, two.sided=TRUE)
results.dsplitd
###[＃]
### Example 2: glossing data[＃例2：粉饰数据]
### - this produces part of Table 5 in the Stevens and Taylor JEBS paper.[＃＃ - 产生在史蒂文斯和泰勒JEBS纸表5部分。]
data(gloss)
dep.groups <- list(c(2,3,4,5),c(10,11,12))
HBLM.ds <- metahdep.HBLM(gloss.theta, gloss.V, gloss.X, center.X=TRUE,
two.sided=TRUE, delta.split=TRUE, dep.groups=dep.groups, n=20, m=20)
round(cbind(HBLM.ds$beta.hats, HBLM.ds$beta.hat.p.values),4)
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 2012-2-26 00:42:14
