generatePvals(gMCP)
generatePvals()所属R语言包：gMCP
generatePvals
generatePvals
译者：生物统计家园网 机器人LoveR
描述----------Description----------
compute adjusted p-values either for the closed test defined by the graph or for each elementary hypotheses within each intersection hypotheses
计算调整的p值或者为封闭测试图形定义的或在各个交叉点假设为每个基本假设
用法----------Usage----------
generatePvals(g, w, cr, p , adjusted = TRUE ,
hint = generateWeights(g, w), exhaust=FALSE)
参数----------Arguments----------
参数：g
graph defined as a matrix, each element defines how much of the local alpha reserved for the hypothesis corresponding to its row index is passed on to the hypothesis corresponding to its column index
作为一个矩阵的定义的图，每个元素定义多少的保留本地的α，对应于它的行索引的假设被传递到对应于它的列的索引的假设
参数：w
vector of weights, defines how much of the overall alpha is initially reserved for each elementary hypothesis
的权重向量，定义多少的总体α为每个基本假设最初保留
参数：cr
correlation matrix if p-values arise from one-sided tests with multivariate normal distributed test statistics for which the correlation is partially known. Unknown values can be set to NA. (See details for more information)
相关矩阵p-值出现一边倒的测试与多元正态分布的检验统计量的相关部分。未知的值可以被设置为NA。 （详见更多信息）
参数：p
vector of observed unadjusted p-values, that belong to test-statistics with a joint multivariate normal null distribution with (partially) known correlation matrix cr
矢量观察到的未经调整的p值，属于一个共同的多元正常的空分布（部分）被称为相关矩阵cr测试统计
参数：adjusted
logical, if TRUE (default) adjusted p-values for the closed test are returned, else a matrix of p-values adjusted only for each intersection hypothesis is returned
逻辑，如果TRUE（默认）p-值调整为封闭测试返回，否则返回p-值的矩阵的各个交叉点的假设仅用于调整
参数：hint
if intersection hypotheses weights have already been computed (output of generateWeights) can be passed here otherwise will be computed during execution
如果路口假设的权重已经计算（generateWeights）的输出可以通过这里，否则将被计算在执行过程中
参数：exhaust
if FALSE (default) the p-values are additionally adjusted for the case that non-exhaustive weights are specified. (See details)
如果FALSE（默认），p值是另外的情况下，指定非完整权重调整。 （查看详细资料）
Details
详细信息----------Details----------
It is assumed that under the global null hypothesis (Φ^{-1}(1-p_1),...,Φ^{-1}(1-p_m)) follow a multivariate normal distribution with correlation matrix cr where Φ^{-1} denotes the inverse of the standard normal distribution function.
据推测，全球零假设下(Φ^{-1}(1-p_1),...,Φ^{-1}(1-p_m))遵循多元正态分布，相关矩阵cr其中Φ^{-1}表示标准正态分布函数的逆。
For example, this is the case if p_1,..., p_m are the raw p-values from one-sided z-tests for each of the elementary hypotheses where the correlation between z-test statistics is generated by an overlap in the observations (e.g. comparison with a common control, group-sequential analyses etc.). An application of the transformation Φ^{-1}(1-p_i) to raw p-values from a two-sided test will not in general lead to a multivariate normal distribution. Partial knowledge of the correlation matrix is supported. The correlation matrix has to be passed as a numeric matrix with elements of the form: cr[i,i] = 1 for diagonal elements, cr[i,j] = ρ_{ij}, where ρ_{ij} is the known value of the correlation between Φ^{-1}(1-p_i) and Φ^{-1}(1-p_j) or NA if the corresponding correlation is unknown. For example cr[1,2]=0 indicates that the first and second test statistic are uncorrelated, whereas cr[2,3] = NA means that the true correlation between statistics two and three is unknown and may take values between -1 and 1. The correlation has to be specified for complete blocks (ie.: if cor(i,j), and cor(i,k) for i!=j!=k are specified then cor(j,k) has to be specified as well) otherwise the corresponding intersection null hypotheses tests are not uniquely defined and an error is returned.
例如，是这样的话，如果p_1,..., p_m原料从单面的z z-检验统计数据之间的相关性被观测中的重叠所产生的基本假设为每个测试（例如p-值比较，与一个共同的控制，基团的顺序分析等）。改造中的应用Φ^{-1}(1-p_i)一个双面的测试不会导致多元正态分布的原p值。支持部分的相关矩阵的知识。必须通过相关矩阵作为一个数字矩阵元素的形式：cr[i,i] = 1对角线上的元素，cr[i,j] = ρ_{ij}，其中ρ_{ij}是已知值之间的相关性Φ^{-1}(1-p_i) 和Φ^{-1}(1-p_j)或NA，如果相应的关系是未知的。例如河[1,2] = 0表示的第一和第二的检验统计量是不相关的，而河[2,3] = NA表示的真实统计数字2和3之间的相关性是未知的，并且可能需要之间的值-1和1。的相关性，必须指定完整的块（即：如果COR（I，J），和肺心病（I，K）为i = J = K，然后指定相应（J，K）被指定为），否则对应的交叉点零假设测试是不是唯一的定义，并返回一个错误。
The parametric tests in (Bretz et al. (2011)) are defined such that the tests of intersection null hypotheses always exhaust the full alpha level even if the sum of weights is strictly smaller than one. This has the consequence that certain test procedures that do not test each intersection null hypothesis at the full level alpha may not be implemented (e.g., a single step Dunnett test). If exhaust is set to FALSE (default) the parametric tests are performed at a reduced level alpha of sum(w) * alpha and p-values adjusted accordingly such that test procedures with non-exhaustive weighting strategies may be implemented. If set to TRUE the tests are performed as defined in Equation (3) of
的参数测试（Bretz等人（2011））定义路口虚无假设的测试总是用尽α水平，即使权重的总和，是严格小于1的。这样做的结果，一定不测试的测试程序，每个交叉点的零假设在满级阿尔法可能无法实现（例如，一个单一的步骤Dunnett检验）。如果exhaust设置为FALSE（默认）在一个较低水平阿尔法的总和（W）*α和p-值进行相应的调整，参数测试用非详尽的加权策略的测试程序可以被实现。如果设置为TRUE执行测试时，如公式（3）中定义的
值----------Value----------
If adjusted is set to true returns a vector of adjusted p-values. Any elementary null hypothesis is rejected if its corresponding adjusted p-value is below the predetermined alpha level. For adjusted set to false a matrix with p-values adjusted only within each intersection hypotheses is returned. The intersection corresponding to each line is given by conversion of the line number into binary (eg. 13 is binary 1101 and corresponds to (H1,H2,H4)). If any adjusted p-value within a given line falls below alpha, then the
如果调整设置为true，返回一个矢量，调整后的P-值。任何基本被拒绝零假设，如果其相应的调整p值低于预定的α水平。调整设置为false时，只有在每一个路口假设p值调整矩阵返回。相应于每行的交叉点由下式给出的行号转换成二进制（例如，13是二进制的1101，并且对应于（H1，H2，H4））。如果有任何一个给定的线内的调整后的p-值低于α，然后
（作者）----------Author(s)----------
Florian Klinglmueller
参考文献----------References----------
实例----------Examples----------
## Define some graph as matrix[＃定义一些图形矩阵]
g <- matrix(c(0,0,1,0, 0,0,0,1, 0,1,0,0, 1,0,0,0), nrow = 4,byrow=TRUE)
## Choose weights[＃选择的权重]
w <- c(.5,.5,0,0)
## Some correlation (upper and lower first diagonal 1/2)[＃一些相关性（上和下侧的第一（对角线长），1/2）]
c <- diag(4)
c[1:2,3:4] <- NA
c[3:4,1:2] <- NA
c[1,2] <- 1/2
c[2,1] <- 1/2
c[3,4] <- 1/2
c[4,3] <- 1/2
## p-values as Section 3 of Bretz et al. (2011),[＃p的值作为第3 Bretz等。 （2011）]
p <- c(0.0121,0.0337,0.0084,0.0160)
## Boundaries for correlated test statistics at alpha level .05:[＃界限为α水平0.05的相关试验的统计：]
generatePvals(g,w,c,p)
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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 2012-9-18 22:08:41
