MTP(multtest)
MTP()所属R语言包：multtest
A function to perform resampling-based multiple hypothesis testing
一个函数来执行重采样为基础的多种假设检验
译者：生物统计家园网 机器人LoveR
描述----------Description----------
A user-level function to perform multiple testing procedures (MTP). A variety of t- and F-tests, including robust versions of most tests, are implemented. Single-step and step-down minP and maxT methods are used to control the chosen type I error rate (FWER, gFWER, TPPFP, or FDR). Bootstrap and permutation null distributions are available. Additionally, for t-statistics, one may wish to sample from an appropriate multivariate normal distribution with mean zero and correlation matrix derived from the vector influence function. Arguments are provided for user control of output. Gene selection in microarray experiments is one application.
执行多个测试程序（MTP）的一个用户级功能。的各种T-和F-测试，包括强劲大多数测试版本，实施。用于控制所选择的类型我错误率（或FWER，gFWER，TPPFP，FDR）的单步和降压minP和maxT方法。引导和置换空分布。此外，为t-统计，不妨品尝从一个适当的多元正态分布零均值和矢量影响功能的相关矩阵。输出控制，为用户提供参数。基因芯片实验选择的是一个应用程序。
用法----------Usage----------
MTP(X, W = NULL, Y = NULL, Z = NULL, Z.incl = NULL, Z.test = NULL,
na.rm = TRUE, test = "t.twosamp.unequalvar", robust = FALSE,
standardize = TRUE, alternative = "two.sided", psi0 = 0,
typeone = "fwer", k = 0, q = 0.1, fdr.method = "conservative",
alpha = 0.05, smooth.null = FALSE, nulldist = "boot.cs",
B = 1000, ic.quant.trans = FALSE, MVN.method = "mvrnorm",
penalty = 1e-06, method = "ss.maxT", get.cr = FALSE, get.cutoff = FALSE,
get.adjp = TRUE, keep.nulldist = TRUE, keep.rawdist = FALSE,
seed = NULL, cluster = 1, type = NULL, dispatch = NULL, marg.null = NULL,
marg.par = NULL, keep.margpar = TRUE, ncp = NULL, perm.mat = NULL,
keep.index = FALSE, keep.label = FALSE)
参数----------Arguments----------
参数：X
A matrix, data.frame or ExpressionSet containing the raw data. In the case of an ExpressionSet, exprs(X) is the data of interest and pData(X) may contain outcomes and covariates of interest. For most currently implemented tests (exception: tests involving correlation parameters), one hypothesis is tested for each row of the data.
A矩阵，数据框或ExpressionSet包含原始数据。在一个ExpressionSet的情况下，exprs(X)是利益和pData(X)可能包含的成果和利益的协变量的数据。对于目前实施的测试（例外：涉及相关参数的测试），测试一个假设每个数据行。
参数：W
A vector or matrix containing non-negative weights to be used in computing the test statistics. If a matrix, W must be the same dimension as X with one weight for each value in X. If a vector, W may contain one weight for each observation (i.e. column) of X or one weight for each variable (i.e. row) of X. In either case, the weights are duplicated appropriately. Weighted F-tests are not available. Default is 'NULL'.
含有非负权重向量或矩阵可以用于计算检验统计量。如果一个矩阵，W必须X每个X值重量相同的维度。如果一个向量，W可能包含一个X重量为每个观察X或为每个变量（即行）重量（即列）。在任何情况下，复制适当的权重。加权F测试不可用。默认为“NULL”。
参数：Y
A vector, factor, or Surv object containing the outcome of interest. This may be class labels (F-tests and two sample t-tests) or a continuous or polycotomous dependent variable (linear regression based t-tests), or survival data (Cox proportional hazards based t-tests). For block.f and f.twoway tests, class labels must be ordered by block and within each block ordered by group. If X is an ExpressionSet, Y can be a character string referring to the column of pData(X) to use as outcome. Default is 'NULL'.
一个向量，影响因素，或Surv对象，其中包含的利益的结果。这可能是标签类（F检验和两样本t检验）或连续或polycotomous的因变量（基于线性回归的t-检验），或生存数据（Cox比例风险为基础的t-检验）。 block.f和f.twoway测试，类的标签，必须由块内每个块组责令下令。 X如果是ExpressionSet，Y可以是一个字符串，指的pData(X)使用结果列。默认为“NULL”。
参数：Z
A vector, factor, or matrix containing covariate data to be used in the regression (linear and Cox) models. Each variable should be in one column, so that nrow(Z)=ncol(X). If X is an ExpressionSet, Z can be a character string referring to the column of pData(X) to use as covariates. The variables Z.incl and Z.adj allow one to specify which covariates to use in a particular test without modifying the input Z. Default is 'NULL'.
一个向量，因子，或矩阵协数据回归（线性和Cox）模型。每个变量应该是在一列，因此，nrow(Z)=ncol(X)。如果X是ExpressionSet，Z可以是一个字符串指的pData(X)使用作为协列。变量Z.incl和Z.adj允许之一来指定，而无需修改输入Z协变量使用在一个特定的测试。默认为“NULL”。
参数：Z.incl
The indices of the columns of Z (i.e. which variables) to include in the model. These can be numbers or column names (if the columns are names). Default is 'NULL'.
Z（即变量）列的指数，包括在模型中。这些可以是数字或列名（列名）。默认为“NULL”。
参数：Z.test
The index or names of the column of Z (i.e. which variable) to use to test for association with each row of X in a linear model. Only used for test="lm.XvsZ", where it is necessary to specify which covariate's regression parameter is of interest. Default is 'NULL'.
索引或列名Z（即变量）使用测试协会与各行X在一个线性模型。仅用于test="lm.XvsZ"，指定协变量的回归参数的利益是有必要的。默认为“NULL”。
参数：na.rm
Logical indicating whether to remove observations with an NA. Default is 'TRUE'.
逻辑表明是否要删除与钠的意见。默认是“TRUE”。
参数：test
Character string specifying the test statistics to use, by default 't.twosamp.unequalvar'. See details (below) for a list of tests.
字符串，指定使用的测试统计，默认“t.twosamp.unequalvar。看到一个测试列表的详细信息（下同）。
参数：robust
Logical indicating whether to use the robust version of the chosen test, e.g. Wilcoxon singed rank test for robust one-sample t-test or rlm instead of lm in linear models. Default is 'FALSE'.
逻辑说明是否使用健壮的版本选择测试，如签字为强大的单样本t检验或rlmlm线性模型，而不是秩秩和检验。默认为“假”。
参数：standardize
Logical indicating whether to use the standardized version of the test statistics (usual t-statistics are standardized). Default is 'TRUE'.
逻辑指示是否使用标准化版本的测试统计数据（通常t-统计标准化）。默认是“TRUE”。
参数：alternative
Character string indicating the alternative hypotheses, by default 'two.sided'. For one-sided tests, use 'less' or 'greater' for null hypotheses of 'greater than or equal' (i.e. alternative is 'less') and 'less than or equal', respectively.
字符串表示替代假说，默认情况下，“two.sided”。片面的测试，使用“大于或等于”零假设（即另一种方法是“少”），“少”或“大”和“大于或等于”，分别。
参数：psi0
The hypothesized null value, typically zero (default). Currently, this should be a single value, which is used for all hypotheses.
虚拟的空值，通常为零（默认值）。目前，这应该是一个单一的价值，这是所有假设。
参数：typeone
Character string indicating which type I error rate to control, by default family-wise error rate ('fwer'). Other options include generalized family-wise error rate ('gfwer'), with parameter k giving the allowed number of false positives, and tail probability of the proportion of false positives ('tppfp'), with parameter q giving the allowed proportion of false positives. The false discovery rate ('fdr') can also be controlled.
字符串指示哪种类型，我的错误率控制，默认家庭明智的错误率（fwer）。其他选项包括家庭明智的广义错误率参数（“gfwer），k给予允许数量的误报，误报（”tppfp）的比例和尾部概率，参数<X >误报允许的比例。也可以控制错误发现率（FDR）。
参数：k
The allowed number of false positives for gFWER control. Default is 0 (FWER).
允许的误报gFWER控制的数量。默认是0（FWER）。
参数：q
The allowed proportion of false positives for TPPFP control. Default is 0.1.
允许的误报TPPFP控制的比例。默认值是0.1。
参数：fdr.method
Character string indicating which FDR controlling method should be used when typeone="fdr". The options are "conservative" (default) for the more conservative, general FDR controlling procedure and "restricted" for the method which requires more assumptions.
应使用字符串表示其中FDR控制方法时typeone="fdr"。选项是比较保守，一般控制程序FDR的“保守”（默认）和“限制”的方法，它需要更多的假设。
参数：alpha
The target nominal type I error rate, which may be a vector of error rates. Default is 0.05.
目标名义I型错误率，这可能是一个错误率的向量。默认值是0.05。
参数：smooth.null
Indicator of whether to use a kernel density estimate for the tail of the null distributon for computing raw pvalues close to zero. Only used if 'rawp' would be zero without smoothing. Default is 'FALSE'.
指标是否使用核密度估计计算接近零的原料pvalues空分布特征的尾巴。仅用于“rawp如果不平滑零。默认为“假”。
参数：nulldist
Character string indicating which resampling method to use for estimating the joint test statistics null distribution, by default the non-parametric bootstrap with centering and scaling ('boot.cs'). The old default 'boot' will still compile and will correspond to 'boot.cs'. Other null distribution options include 'perm', 'boot.ctr', 'boot.qt', and 'ic', corresponding to the permutation distribution, centered-only bootstrap distribution, quantile-transformed bootstrap distribution, and influence curve multivariate normal joint null distribution, respectively. More details below.
字符串表示使用重采样方法估算默认情况下，联合测试空分布统计，与中心和缩放（“boot.cs）非参数引导。旧的默认的引导仍将编译，将符合“boot.cs。其他空分布选项包括烫发，boot.ctr，boot.qt“，”IC“，对应的排列分布，中心只引导分布，分量转化的引导分布，影响曲线多元正常的关节空分布，分别为。更多细节如下。
参数：B
The number of bootstrap iterations (i.e. how many resampled data sets), the number of permutations (if nulldist is 'perm'), or the number of samples from the multivariate normal distribution (if nulldist is 'ic') Can be reduced to increase the speed of computation, at a cost to precision. Default is 1000.
引导迭代的数量（即多少重采样的数据集），排列（nulldist如果是“烫”），或多元正态分布的样本数量（nulldist如果是可降低IC）的运算速度，在精确的成本增加。默认值是1000。
参数：ic.quant.trans
If nulldist='ic', a logical indicating whether or not a marginal quantile transformation using a t-distribution or user-supplied marginal distribution (stored in perm.mat) should be applied to the multivariate normal null distribution. Defaults for marg.null and marg.par exist, but can also be specified by the user (see below). Default is 'FALSE'.
如果nulldist='ic'，逻辑指示是否使用t分布或用户提供的边际分布（存储perm.mat）应适用于多元的正常空分布的边际位数改造。 marg.null和marg.par存在，但也可以由用户（见下文）中指定的默认值。默认为“假”。
参数：MVN.method
If nulldist='ic', one of 'mvrnorm' or 'Cholesky' designating how correlated normal test statistics are to be generated. Selecting 'mvrnorm' uses the function of the same name found in the MASS library, whereas 'Cholesky' relies on a Cholesky decomposition. Default is 'mvrnorm'.
如果nulldist='ic'，mvrnorm或乔列斯基“，指定相关正常的检验统计量是如何产生的。选择“mvrnormMASS库中发现了同样的名称使用的功能，而”乔列斯基依赖Cholesky分解。默认是mvrnorm“。
参数：penalty
If nulldist='ic' and MVN.method='Cholesky', the value in penalty is added to all diagonal elements of the estimated test statistics correlation matrix to ensure that the matrix is positive definite and that internal calls to 'chol' do not return an error. Default is 1e-6.
如果nulldist='ic'和MVN.method='Cholesky'，penalty添加到所有估计检验统计量的相关矩阵对角线元素，以确保该矩阵是正定，内部通话<X >不返回一个错误。默认是1E-6。
参数：method
The multiple testing procedure to use. Options are single-step maxT ('ss.maxT', default), single-step minP ('ss.minP'), step-down maxT ('sd.maxT'), and step-down minP ('sd.minP').
使用多个测试程序。选项是单步的“ss.maxT，maxT（默认），单步minP（”ss.minP），降压maxT（“sd.maxT），和降压minP（”sd.minP的）。
参数：get.cr
Logical indicating whether to compute confidence intervals for the estimates. Not available for F-tests. Default is 'FALSE'.
逻辑指示是否计算置信区间估计。不提供F检验。默认为“假”。
参数：get.cutoff
Logical indicating whether to compute thresholds for the test statistics. Default is 'FALSE'.
逻辑指示是否计算为检验统计量的阈值。默认为“假”。
参数：get.adjp
Logical indicating whether to compute adjusted p-values. Default is 'TRUE'.
逻辑表示是否调整p值计算。默认是“TRUE”。
参数：keep.nulldist
Logical indicating whether to return the computed bootstrap or influence curve null distribution, by default 'TRUE'. Not available for nulldist='perm'. Note that this matrix can be quite large.
逻辑表示是否返回计算机的引导或影响曲线空分布，默认情况下，“TRUE”。不nulldist=烫发的。请注意，这个矩阵可以相当大。
参数：keep.rawdist
Logical indicating whether to return the computed non-null (raw) bootstrap distribution, by default 'FALSE'. Not available when using nulldist='perm' or 'ic'. Note that this matrix can become quite large. If one wishes to use subsequent calls to update or EBupdate in which one updates choice of bootstrap null distribution, keep.rawdist must be TRUE. To save on memory, update only requires that one of keep.nulldist or keep.rawdist be 'TRUE'.
逻辑表示是否返回默认的假计算非空（原材料）引导分布。不使用时nulldist=烫发或IC。请注意，这个矩阵可以变得相当大。如果希望使用的后续调用update或EBupdate在一个更新的引导空分布的选择，keep.rawdist必须是TRUE。为了节省内存，update只要求keep.nulldist或keep.rawdist是真之一。
参数：seed
Integer or vector of integers to be used as argument to set.seed to set the seed for the random number generator for bootstrap resampling. This argument can be used to repeat exactly a test performed with a given seed. If the seed is specified via this argument, the same seed will be returned in the seed slot of the MTP object created. Else a random seed(s) will be generated, used and returned. Vector of integers used to specify seeds for each node in a cluster used to to generate a bootstrap null distribution.
整数或整数向量被用来作为参数set.seed设置为引导重采样的随机数发生器的种子。这种说法，可以使用完全重复一个给定的种子进行了测试。如果通过此参数指定的种子，将返回相同的种子，在中期计划创建的对象的种子插槽。其他随机种子（S）将产生，使用和返回。向量整数，用于指定聚类中的每个节点用于生成一个bootstrap空分布的种子。
参数：cluster
Integer for number of nodes to create or a cluster object created through the package snow. With cluster=1, bootstrap is implemented on single node. Supplying a cluster object results in the bootstrap being implemented in parallel on the provided nodes. This option is only available for the bootstrap procedure. With default value of 1, bootstrap is executed on single CPU.
为创建的节点数量的整数或包雪通过创建一个聚类对象。用cluster=1，引导实现对单个节点。在所提供的节点上并行实施引导提供聚类对象的结果。此选项仅在引导过程中可用。默认值为1，单CPU执行引导。
参数：type
Interface system to use for computer cluster. See snow package for details.
接口系统使用的计算机聚类。看到snow细节包。
参数：dispatch
The number or percentage of bootstrap iterations to dispatch at a time to each node of the cluster if a computer cluster is used. If dispatch is a percentage, B*dispatch must be an integer. If dispatch is an integer, then B/dispatch must be an integer. Default is 5 percent.
引导迭代的数量或百分比的派遣，在聚类的每个节点的时间，如果一个计算机聚类使用。如果调度是一个百分比，B*dispatch必须是一个整数。如果调度是一个整数，然后B/dispatch必须是一个整数。默认是5％。
参数：marg.null
If nulldist='boot.qt', the marginal null distribution to use for quantile transformation. Can be one of 'normal', 't', 'f' or 'perm'. Default is 'NULL', in which case the marginal null distribution is selected based on choice of test statistics. Defaults explained below. If 'perm', the user must supply a vector or matrix of test statistics corresponding to another marginal null distribution, perhaps one created externally by the user, and possibly referring to empirically derived marginal permutation distributions, although the statistics could represent any suitable choice of marginal null distribution.
如果nulldist='boot.qt'，边际空分布使用位数的转变。可以是一个正常，T，F或烫发。默认为“NULL”，在这种情况下，边际空分布选择的基础上选择测试统计。默认下面的解释。如果烫发，用户必须提供一个检验统计量的向量或矩阵对应到另一个边缘空分布，也许有外部创建的用户，可能指的是经验得出的边缘排列分布，虽然统计数字可以代表任何合适的选择边际空分布。
参数：marg.par
If nulldist='boot.qt', the parameters defining the marginal null distribution in marg.null to be used for quantile transformation. Default is 'NULL', in which case the values are selected based on choice of test statistics and other available parameters (e.g., sample size, number of groups, etc.). Defaults explained below. User can override defaults, in which case a matrix of marginal null distribution parameters can be accepted. Providing numeric (vector) values will apply the same null distribution defined by the parameter to all hypotheses, while providing a matrix of values allows the user to perform multiple testing using parameters which may vary with each hypothesis, as may be desired in common-quantile minP procedures. In this way, theoretical factors or factors affecting sample size or missingness may be assessed.
如果nulldist='boot.qt'，参数定义marg.null的边际空分布将用于位数的转变。默认是“空”，根据测试统计数据和其他可用的参数（例如，样本大小，组数等）的选择值，在这种情况下，被选中。默认下面的解释。用户可以覆盖默认值，在这种情况下，边际空分布参数的矩阵可以接受的。提供数字（矢量）值将适用于所有假设的参数定义相同的空分布，同时提供了一个矩阵的值，使用户能够执行多个测试，使用参数可能会有所不同，每一种假说，可所需的共同位数minP程序。在这种方式，样本大小或missingness的理论因素或影响因素进行评估。
参数：keep.margpar
If nulldist='boot.qt', a logical indicating whether the (internally created) matrix of marginal null distribution parameters should be returned. Default is 'TRUE'.
如果nulldist='boot.qt'，表明边际空分布参数的内部创建矩阵是否应该返回一个逻辑。默认是“TRUE”。
参数：ncp
If nulldist='boot.qt', a value for a possible noncentrality parameter to be used during marginal quantile transformation. Default is 'NULL'.
如果nulldist='boot.qt'，一个可能noncentrality参数的值必须在边际位数改造。默认为“NULL”。
参数：perm.mat
If nulldist='boot.qt' and marg.null='perm', a matrix of user-supplied test statistics from a particular distribution to be used during marginal quantile transformation. The statistics may represent empirically derived marginal permutation values, may be theoretical values, or may represent a sample from some other suitable choice of marginal null distribution.
如果nulldist='boot.qt'和marg.null='perm'，用户提供的测试统计数据，从一个特定分布的矩阵被用于在边际位数改造。的统计数字可能代表经验得出的边际置换值，可能是理论值，或可以代表一个样本，从其他一些选择合适的边际空分布。
参数：keep.index
If nulldist='ic' and test='t.cor' or test='z.cor', the index returned is a matrix with the indices of the first and second variables considered for pairwise correlations. If there are p hypotheses, this arguments returns t(combn(p,2)). For all other choices of test statistic, the index is not returned, as they correspond to the original order of the hypotheses in X.
如果nulldist='ic'和test='t.cor'或test='z.cor'，索引返回的是一个与成对相关考虑的第一个和第二个变量指数矩阵。如果有P的假设，这个参数返回t(combn(p,2))。为检验统计量的所有其他选择，指数没有回来，因为它们对应假设X的原始为了。
参数：keep.label
Default is 'FALSE'. A logical indicating whether or not the label in Y should be returned as a slot in the resulting MTP object. Typically not necessary, although useful if one is using update and wants to use marginal null distribution defaults with nulldist='boot.qt' (e.g., with F-tests).
默认为“假”。 Y应在中期计划的对象插槽返回的标签与否的一个逻辑说明。通常没有必要的，虽然有用的，如果是使用update和nulldist='boot.qt'（例如，与F-测试）要使用边际空分布的默认值。
Details
详情----------Details----------
A multiple testing procedure (MTP) is defined by choices of test statistics, type I error rate, null distribution and method for error rate control. Each component is described here. For two-sample t-tests, the group with the smaller-valued label is substracted from the group with the larger-valued label. That is, differences in means are calculated as "mean of group 2 - mean of group 1" or "mean of group B - mean of group A". For paired t-tests, the arrangement of group indices does not matter, as long as the columns are arranged in the same corresponding order between groups. For example, if group 1 is coded as 0, and group 2 is coded as 1, for 3 pairs of data, it does not matter if the label Y is coded as "0,0,0,1,1,1", "1,1,1,0,0,0" "0,1,0,1,0,1" or "1,0,1,0,1,0", the paired differences between groups will be calculated as "group 2 - group 1". See references for more detail.
一个多个测试程序（MTP）的定义是由选择的测试统计，键入我的错误率，空分布和错误率控制的方法。这里所描述的每个组件。为两样本t检验值较小的标签组中减去从较大值的标签组。也就是说，“意味着第2组 - 第1组”的意思是计算方式的差异“是指B组 - 组意味着”。配对t检验，组指标的安排没有关系，只要列在同一组之间的对应顺序排列。例如，如果第1组的编码为0，2组3对数据编码为1，它并不重要，如果标签Y编码为“0,0,0,1， 1,1“，”1,1,1,0,0,0“，”0,1,0,1,0,1“或”1,0,1,0,1,0“，配对组之间的差异将被计算为“第2组 - 第1组”。查看更详细的参考。
Test statistics are determined by the values of test:
检验统计量确定值test：
t.onesamp: one-sample t-statistic for tests of means;
t.onesamp：t-统计样本测试手段;
t.twosamp.equalvar: equal variance two-sample t-statistic for tests of differences in means (two-sample t-statistic);
t.twosamp.equalvar：等于方差双样本t-统计测试方法的差异（两样本t-统计）;
t.twosamp.unequalvar: unequal variance two-sample t-statistic for tests of differences in means (two-sample Welch t-statistic);
t.twosamp.unequalvar：不平等的方差双样本t-统计测试方法的差异（两样本韦尔奇t-统计）;
t.pair: two-sample paired t-statistic for tests of differences in means;
t.pair：两样本配对t-统计量的测试方法的差异;
f: multi-sample F-statistic for tests of equality of population means (assumes constant variance across groups, but not normality);
F：多样品测试F-统计人口的手段（假定常数方差，各团体，但不正常）平等;
f.block: multi-sample F-statistic for tests of equality of population means in a block design (assumes constant variance across groups, but not normality). This test is not available with the bootstrap null distribution;
f.block：多人口平等的测试样品F-统计是指在一个块设计（假定常数方差，各团体，但不正常）。这项测试是不引导空分布;
f.twoway: multi-sample F-statistic for tests of equality of population means in a block design (assumes constant variance across groups, but not normality). Differs from f.block in requiring multiple observations per group*block combintation. This test uses the means of each group*block combination as response variable and test for group main effects assuming a randomized block design;
f.twoway：F-统计多人口平等的测试样品是指在一个块设计（假定常数方差，各团体，但不正常）。从f.block要求每个组块combintation多个观测会。这个测试使用*作为响应变量和假设随机区组设计组的主要影响测试各组块组合的手段;
lm.XvsZ: t-statistic for tests of regression coefficients for variable Z.test in linear models, each with a row of X as outcome, possibly adjusted by covariates Z.incl from the matrix Z (in the case of no covariates, one recovers the one-sample t-statistic, t.onesamp);
lm.XvsZ：测试变量Z.test线性模型，每一个结果的X行，可能由协变量调整的Z.incl从矩阵Z（回归系数的t-统计在没有协变量的情况下，恢复一个样本的t-统计，t.onesamp）;
lm.YvsXZ: t-statistic for tests of regression coefficients in linear models, with outcome Y and each row of X as covariate of interest, with possibly other covariates Z.incl from the matrix Z;
lm.YvsXZ：t-统计线性模型回归系数的测试，结果Y和X的每个行利息协与其他可能的变项，Z.incl矩阵Z;
coxph.YvsXZ: t-statistic for tests of regression coefficients in Cox proportional hazards survival models, with outcome Y and each row of X as covariate of interest, with possibly other covariates Z.incl from the matrix Z.
coxph.YvsXZ：T-Cox比例风险生存模型回归系数的测试统计，结果Y和X的每个行利息协与其他可能的变项，Z.incl矩阵Z的。
t.cor t-statistics for tests of pairwise correlation parameters for all variables in X. Note that the number of hypotheses can become quite large very fast. This test is only available with the influence curve null distribution.
t.cor t-统计量为十注中的所有变量的成对相关参数的测试假设的数量会变得相当大，速度非常快。这项测试是只影响曲线空分布。
z.cor Fisher's z-statistics for tests of pairwise correlation parameters for all variables in X. Note that the number of hypotheses can become quite large very fast. This test is only available with the influence curve null distribution.
z.cor费舍尔的Z-统计成对相关参数的测试，在X注的所有变量，假设的数量会变得相当大，速度非常快。这项测试是只影响曲线空分布。
When robust=TRUE, non-parametric versions of each test are performed. For the linear models, this means rlm is used instead of lm. There is not currently a robust version of test=coxph.YvsXZ. For the t- and F-tests, data values are simply replaced by their ranks. This is equivalent to performing the following familiar named rank-based tests. The conversion after each test is the formula to convert from the MTP test to the statistic reported by the listed R function (where num is the numerator of the MTP test statistics, n is total sample size, nk is group k sample size, K is total number of groups or treatments, and rk are the ranks in group k).
当robust=TRUE，非参数进行每个测试版本。对于线性模型，这意味着，rlm是用来代替lm。目前还没有一个test=coxph.YvsXZ强大的版本。对于T-F检验，数据值是简单地取代他们的行列。这是相当于执行下列熟悉的命名的排名为基础的测试。每次测试后的转换是从MTP测试报告的统计，上市的R函数（公式转换，其中num是MTP测试统计的分子，n为总样本数，NK是K组样本大小，K是总数，团体或治疗，和RK K组的行列）。
t.onesamp or t.pair: Wilcoxon signed rank, wilcox.test with y=NULL or paired=TRUE, <br>
t.onesamp或t.pair：Wilcoxon秩，wilcox.testy=NULL或paired=TRUE参考
t.twosamp.equalvar: Wilcoxon rank sum or Mann-Whitney, wilcox.test, <br>
t.twosamp.equalvar：秩或Mann-Whitney，wilcox.test，参考
f: Kruskal-Wallis rank sum, kruskal.test, <br>
F：克鲁斯卡尔 - 沃利斯秩，kruskal.test，参考
f.block: Friedman rank sum, friedman.test, <br>
f.block：弗里德曼排名的总和，friedman.test，参考
f.twoway: Friedman rank sum, friedman.test, <br>
f.twoway：弗里德曼排名的总和，friedman.test，参考
The implemented MTPs are based on control of the family-wise error rate, defined as the probability of any false positives. Let Vn denote the (unobserved) number of false positives. Then, control of FWER at level alpha means that Pr(Vn>0)<=alpha. The set of rejected hypotheses under a FWER controlling procedure can be augmented to increase the number of rejections, while controlling other error rates. The generalized family-wise error rate is defined as Pr(Vn>k)<=alpha, and it is clear that one can simply take an FWER controlling procedure, reject k more hypotheses and have control of gFWER at level alpha. The tail probability of the proportion of false positives depends on both the number of false postives (Vn) and the number of rejections (Rn). Control of TPPFP at level alpha means Pr(Vn/Rn>q)<=alpha, for some proportion q. Control of the false discovery rate refers to the expected proportion of false positives (rather than a tail probability). Control of FDR at level alpha means E(Vn/Rn)<=alpha.
家庭明智的错误率定义为任何误报的概率，控制的基础上实施台胞证。让VN表示误报（未观察）。然后，在水平阿尔法FWER控制意味着，镨（VN> 0）<=α。可增强下FWER控制程序拒绝假说，同时控制其他的错误率增加的拒绝。广义的家庭明智的错误率被定义为镨（VN> K）<=阿尔法，它是明确的，可以简单地采取一个FWER控制程序，拒绝K的假设和有水平阿尔法gFWER的控制。尾概率的误报的比例取决于两个虚假postives数（VN）和拒绝（RN）。阿尔法级控制TPPFP意味着镨（VN / RN> Q）<=α，一些比例q。虚假的发现率的控制是指误报（而不是尾概率）的预期比例。FDR控制水平阿尔法意味着E（下VN / RN）<=α。
In practice, one must choose a method for estimating the test statistics null distribution. We have implemented several versions of an ordinary non-parametric bootstrap estimator and a permutation estimator (which makes sense in certain settings, see references). The non-parametric bootstrap estimator (default) provides asymptotic control of the type I error rate for any data generating distribution, whereas the permutation estimator requires the subset pivotality assumption. One draw back of both methods is the discreteness of the estimated null distribution when the sample size is small. Furthermore, when the sample size is small enough, it is possible that ties will lead to a very small variance estimate. Using standardize=FALSE allows one to avoid these unusually small test statistic denominators. Parametric bootstrap estimators are another option (not yet implemented). For asymptotically linear estimators, such as those commonly probed using t-statistics, another choice of null distribution is provided when sampling from a multivariate normal distribution with mean zero and correlation matrix derived from the vector influence function. Sampling from a multivariate normal may alleviate the discreteness of the bootstrap and permutation distributions, although accuracy in estimation of the test statistics correlation matrix will be of course also affected by sample size.
在实践中，必须选择一个估算的测试统计，空分布的方法。我们已经实施了多个版本，一个普通的非参数的bootstrap估计和置换估计（这是有道理的，在某些设置，见参考文献）。非参数自举估计（默认）提供渐近分布产生的任何数据的I型错误率控制，而置换估计需要子集pivotality假设。一个退缩的两种方法是当样本规模很小，估计空分布的离散。此外，当样本大小是足够小，它是可能的关系，将导致一个非常小的方差估计。使用standardize=FALSE允许一个避免这些不寻常的小测试统计分母。参数Bootstrap估计的是另一种选择（尚未实施）。为，如常用探讨使用t-统计量的渐近线性估计，空分布的另一种选择是从多元正态分布零均值和矢量影响功能的相关矩阵进行采样时提供。从一个多元的正常采样可以减轻引导和排列分布的离散性，虽然在测试统计相关矩阵的估计精度，当然也受到样本大小的影响。
For the nonparametric bootstrap distribution with marginal null quantile transformation, the following defaults for marg.null and marg.par are available based on choice of test statistics, sample size 'n', and various other parameters:
对于非参数引导分布与边际空位数改造，marg.null和marg.par以下默认可根据检验统计量，样本大小N，以及其他各种参数的选择：
t.onesamp: t-distribution with df=n-1;
t.onesamp：DF = N-1的t分布;
t.twosamp.equalvar: t-distribution with df=n-2;
t.twosamp.equalvar：t-分布与DF = N-2;
t.twosamp.unequalvar: N(0,1);
t.twosamp.unequalvar N（0,1）;
t.pair: t-distribution with df=n-1, where n is the number of unique samples, i.e., the number of observed differences between paired samples;
t.pair：t-分布与DF = N-1，其中n是独特的样本数量，即观察到的差异之间的配对样本数量;
f: F-distribution with df1=k-1, df2=n-k, for k groups;
F：F分布与DF1，DF2 K-1 = NK，K组;
f.block: NA. Only available with permutation distribution;
f.block：不适用。仅排列分布;
f.twoway: F-distribution with df1=k-1,df2=n-k*l, for k groups and l blocks;
f.twoway：F分布与DF1，DF2 K-1 = NK * L，K组和l块;
lm.XvsZ: N(0,1);
lm.XvsZ N（0,1）;
lm.YvsXZ: N(0,1);
lm.YvsXZ N（0,1）;
coxph.YvsXZ: N(0,1);
coxph.YvsXZ N（0,1）;
t.cor t-distribution with df=n-2;
t.cor T-分布，DF = N-2;
z.cor N(0,1).
z.cor氮（0,1）。
The above defaults, however, can be overridden by manually setting values of marg.null and marg.par. In the case of nulldist='ic', and ic.quant.trans=TRUE, the defaults are the same as above except that 'lm.XvsZ' and 'lm.YvsXZ' are replaced with t-distributions with df=n-p.
上述违约，但是，可以通过手动设置marg.null和marg.par值覆盖。在nulldist='ic'，ic.quant.trans=TRUE，默认是，“lm.XvsZ和lm.YvsXZ”取代T-DF = NP分布除上述相同。
Given observed test statistics, a type I error rate (with nominal level), and a test statistics null distribution, MTPs provide adjusted p-values, cutoffs for test statistics, and possibly confidence regions for estimates. Four methods are implemented, based on minima of p-values and maxima of test statistics. Only the step down methods are currently available with the permutation null distribution.
鉴于试验观察统计，我的错误率（标称电平）的类型，和检验统计量的零分布，台胞证提供调整为检验统计量的p值，临界值，并估计可能置信区域。四种方法实现，根据p值和检验统计量的极大极小。只有向下的方法步骤，目前可与空分布排列。
Computation times using a bootstrap null distribution are slower when weights are used for one and two-sample tests. Computation times when using a bootstrap null distribution also are slower for the tests lmXvsZ, lmYvsXZ, coxph.YvsXZ.
使用引导空分布的计算时间慢时一和两样本测试使用权。计算时间时使用引导空分布也为测试慢lmXvsZ，lmYvsXZ，coxph.YvsXZ。
To execute the bootstrap on a computer cluster, a cluster object generated with makeCluster in the package snow may be used as the argument for cluster. Alternatively, the number of nodes to use in the computer cluster can be used as the argument to cluster. In this case, type must be specified and a cluster will be created. In both cases, Biobase and multtest will be loaded onto each cluster node if these libraries are located in a directory in the standard search path. If these libraries are in a non-standard location, it is necessary to first create the cluster, load Biobase and multtest on each node and then to use the cluster object as the argument to cluster. See documentation for snow package for additional information on creating and using a cluster.
计算机聚类，生成的聚类对象上执行引导makeCluster包中的snow可用于聚类参数。另外，使用计算机聚类的节点数目，可以用来作为聚类参数。在这种情况下，type必须指定将创建一个聚类。在这两种情况下，Biobase和multtest将加载到每个聚类节点上，如果这些库位于在一个标准的搜索路径中的目录。如果这些库是在非标准的位置，这是首先需要创建聚类，加载Biobase和multtest每个节点上，然后使用的聚类对象的聚类参数。 snow包聚类上创建和使用的附加信息，请参阅文件。
Finally, note that the old argument csnull is now DEPRECATED as of multtest v. 2.0.0 given the expanded null distribution options described above. Previously, this argument was an indicator of whether the bootstrap estimated test statistics distribution should be centered and scaled (to produce a null distribution) or not. If csnull=FALSE, the (raw) non-null bootstrap estimated test statistics distribution was returned. If the non-null bootstrap distribution should be returned, this object is now stored in the 'rawdist' slot when keep.rawdist=TRUE in the original MTP function call.
最后，请注意，旧的参数csnull现在multtest诉2.0.0鉴于以上所述的选项扩大空分布不赞成使用。此前，这种说法是引导估计检验统计量的分布是否应为本，缩放（产生一个空分布）或不指示器。如果csnull=FALSE，（原）非空引导估计检验统计量的分布退回。如果非空的引导分布应予以退货，这个对象现在存储在“rawdist”槽时keep.rawdist=TRUE原来MTP函数调用。
值----------Value----------
An object of class MTP, with the following slots:
一个对象的类MTP，以下插槽：
参数：<code>statistic</code>
Object of class numeric, observed test statistics for each hypothesis, specified by the values of the MTP arguments test, robust, standardize, and psi0.
Object类的numeric，每一种假说，观察测试统计的MTP参数的值指定test，robust，standardize，<X >
参数：<code>estimate</code>
For the test of single-parameter null hypotheses using t-statistics (i.e., not the F-tests), the numeric vector of estimated parameters corresponding to each hypothesis, e.g. means, differences in means, regression parameters.
对于单参数的空假设，参数估计的数字矢量对应每一种假说，t-统计量（即F检验）测试，例如方法，手段的差异，回归参数。
参数：<code>sampsize</code>
Object of class numeric, number of columns (i.e. observations) in the input data set.
numeric类的对象，在输入数据集的列数（即观察）。
参数：<code>rawp</code>
Object of class numeric, unadjusted, marginal p-values for each hypothesis.
对象的类numeric，未经调整，边际每个假设P-值。
参数：<code>adjp</code>
Object of class numeric, adjusted (for multiple testing) p-values for each hypothesis (computed only if the get.adjp argument is TRUE).
对象的类numeric，调整（多个测试）为每个假设P-值（仅计算get.adjp参数为TRUE）。
参数：<code>conf.reg</code>
For the test of single-parameter null hypotheses using t-statistics (i.e., not the F-tests), the numeric array of lower and upper simultaneous confidence limits for the parameter vector, for each value of the nominal Type I error rate alpha (computed only if the get.cr argument is TRUE).
为测试单参数为null，数字数组上下置信限参数向量的同时，t-统计量（即F检验）假设每个标称值类型错误率alpha（get.cr参数计算仅是TRUE）。
参数：<code>cutoff</code>
The numeric matrix of cut-offs for the vector of test statistics for each value of the nominal Type I error rate alpha (computed only if the get.cutoff argument is TRUE).
截止权衡名义类型的每个值的向量测试统计的数字矩阵，我的错误率alpha（仅计算get.cutoff参数是TRUE）。
参数：<code>reject</code>
Object of class 'matrix', rejection indicators (TRUE for a rejected null hypothesis), for each value of the nominal Type I error rate alpha.
Object类的'matrix'，抑制指标（拒绝零假设为真），每个标称值类型错误率alpha。
参数：<code>rawdist</code>
The numeric matrix for the estimated nonparametric non-null test statistics distribution (returned only if keep.rawdist=TRUE and if nulldist is one of 'boot.ctr', 'boot.cs', or 'boot.qt'). This slot must not be empty if one wishes to call update to change choice of bootstrap-based null distribution.
估计非参数非空试验的统计分布的数值矩阵（返回只有keep.rawdist=TRUE如果nulldist是一个“boot.ctr，boot.cs或boot.qt，” ）。这个插槽不能是空的，如果人们想打检测update改变引导为主的空分布的选择。
参数：<code>nulldist</code>
The numeric matrix for the estimated test statistics null distribution (returned only if keep.nulldist=TRUE); option not currently available for permutation null distribution, i.e., nulldist='perm'). By default (i.e., for nulldist='boot.cs'), the entries of nulldist are the null value shifted and scaled bootstrap test statistics, with one null test statistic value for each hypothesis (rows) and bootstrap iteration (columns).
估计检验统计量的零分布的数字矩阵（返回只有keep.nulldist=TRUE）;置换空分布，即nulldist='perm'）目前尚未提供的选项。默认情况下（即nulldist='boot.cs'），参赛nulldist转移和缩放的引导测试统计每个假设一个空测试统计值（行）和引导迭代（列）的空值， 。
参数：<code>nulldist.type</code>
Character value describing which choice of null distribution was used to generate the MTP results. Takes on one of the values of the original nulldist argument in the call to MTP, i.e., 'boot.cs', 'boot.ctr', 'boot.qt', 'ic', or 'perm'.
字符值描述空分布的选择是用来产生中期计划的结果。注意到原nulldist在中期计划，即“boot.cs，boot.ctr，boot.qt”，“IC”，或“烫发”的呼叫参数值之一。
参数：<code>marg.null</code>
If nulldist='boot.qt', a character value returning which choice of marginal null distribution was used by the MTP. Can be used to check default values or to ensure manual settings were correctly applied.
如果nulldist='boot.qt'，一个字符值返回边际空分布的选择是中期计划。可用于检查默认值，以确保手动设置正确适用。
参数：<code>marg.par</code>
If nulldist='boot.qt', a numeric matrix returning the parameters of the marginal null distribution(s) used by the MTP. Can be used to check default values or to ensure manual settings were correctly applied.
如果nulldist='boot.qt'返回的中期计划（S）的边际空分布的参数，数字矩阵。可用于检查默认值，以确保手动设置正确适用。
参数：<code>call</code>
Object of class call, the call to the MTP function.
对象的类call，中期计划功能的调用。
参数：<code>seed</code>
An integer or vector for specifying the state of the random number generator used to create the resampled datasets. The seed can be reused for reproducibility in a repeat call to MTP. This argument is currently used only for the bootstrap null distribution (i.e., for nulldist="boot.xx"). See ?set.seed for details.
整数或指定用于创建重采样的数据集随机数发生器的状态向量。种子可重复使用的重复性在重复调用MTP。这种说法目前只能用于引导空分布（即nulldist="boot.xx"）。看到?set.seed详情。
注意----------Note----------
Thank you to Peter Dimitrov for suggestions about the code.
谢谢彼得·季米特洛夫有关代码的建议。
作者（S）----------Author(s)----------
Katherine S. Pollard and Houston N. Gilbert with design contributions from Sandra Taylor, Sandrine Dudoit and Mark J. van der Laan.
参考文献----------References----------
http://www.bepress.com/sagmb/vol3/iss1/art15/
http://www.bepress.com/sagmb/vol3/iss1/art14/
http://www.bepress.com/sagmb/vol3/iss1/art13/
http://www.bepress.com/ucbbiostat/paper121
http://www.bepress.com/sagmb/vol5/iss1/art14/
参见----------See Also----------
EBMTP, MTP-class, MTP-methods, mt.minP, mt.maxT, ss.maxT, fwer2gfwer
EBMTP，MTP-class，MTP-methods，mt.minP，mt.maxT，ss.maxT，fwer2gfwer
举例----------Examples----------
#data [数据]
set.seed(99)
data<-matrix(rnorm(90),nr=9)
group<-c(rep(1,5),rep(0,5))
#fwer control with centered and scaled bootstrap null distribution [fwer控制中心和规模化的引导空分布]
#(B=100 for speed)[（二= 100的速度）]
m1<-MTP(X=data,Y=group,alternative="less",B=100,method="sd.minP")
print(m1)
summary(m1)
par(mfrow=c(2,2))
plot(m1,top=9)
#fwer control with quantile transformed bootstrap null distribution[fwer位数转化引导空分布的控制]
#default settings = N(0,1) marginal null distribution[默认设置为N（0,1）边际空分布]
m2<-MTP(X=data,Y=group,alternative="less",B=100,method="sd.minP",
nulldist="boot.qt",keep.rawdist=TRUE)
#fwer control with quantile transformed bootstrap null distribution[fwer位数转化引导空分布的控制]
#marginal null distribution and df parameters manually set, [边际空分布和df参数的手动设置，]
#first all equal, then varying with hypothesis[首先人人平等，然后与假设不同]
m3<-update(m2,marg.null="t",marg.par=10)
mps<-matrix(c(rep(9,5),rep(10,5)),nr=10,nc=1)
m4<-update(m2,marg.null="t",marg.par=mps)
m1@nulldist.type
m2@nulldist.type
m2@marg.null
m2@marg.par
m3@nulldist.type
m3@marg.null
m3@marg.par
m4@nulldist.type
m4@marg.null
m4@marg.par
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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。
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 2012-2-26 07:40:00
