matest(maanova)
matest()所属R语言包：maanova
Statistical test for Microarray experiment
微阵列实验的统计测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This is the function to perform F or T test on one or multiple experimental factor(s). Permutation test will be carried upon request.
这是要执行的功能上的一个或多个实验因子（S）F或T检验。将被要求进行排列测试。
用法----------Usage----------
matest(data, anovaobj, term, Contrast, n.perm=1000, nnodes=1,
critical=.9, test.type = c("ttest", "ftest"),
shuffle.method=c("sample", "resid"),
MME.method=c("REML","noest","ML"),
test.method=c(1,1),pval.pool=TRUE, verbose=TRUE)
参数----------Arguments----------
参数：data
An object of class madata.
对象类madata。
参数：anovaobj
An object of class fitmaanova.
对象类fitmaanova。
参数：term
The term(s) to be tested. It can be multiple terms. Note that the tested term must be fixed. If the term to be tested is a random term, it will be converted to a fixed term than do test.
术语（S）进行测试。它可以是多个方面。注意：必须是固定的，测试的任期。如果要测试的任期是一个随机的任期，将转换为固定期限比测试。
参数：Contrast
The contrast matrix for the term. The number of columns equals to the number of levels in the term. The number of rows is the number of T-test you want to carry. Note that it must be a matrix. Use PairContrast to make all possible pairwise comparison or matrix command to make it manually. Note that the the hypothesis test can be formulated as H0: Lb=0 versus alternative. This contrast matrix is L. For testing a covariate, use a one by one contrast matrix of 1.
术语的对比度矩阵。列数等于在术语水平。行数是要执行的T-检验。请注意，它必须是一个矩阵。使用PairContrast要尽一切可能的成对比较或matrix命令，以手动方式使其。请注意，假设检验，可以制定为H0：LB = 0与替代。这种反差矩阵测试协变量的研究，一个一个对比矩阵1。
参数：n.perm
An integer for number of permutations.
排列数的整数。
参数：nnodes
Number of nodes in the MPI cluster. If 1, the permutation test will be running on the local computer.
在MPI的聚类节点的数量。如果为1，置换测试将在本地计算机上运行。
参数：critical
percentile of F-distribution used to get a subset to calculate p-value. Default is 90th percentile of F-distribution, and permutation analysis is conducted based on genes whose test statistics are smaller than 90th percentile of the F-distribution.
F分布百分用来得到一个子集来计算p值。默认是F分布的第90百分位，和基因的检验统计量小于第90百分位的F-分布的基础上，进行排列分析。
参数：test.type
Test type. It could be F-test or T-test. If the Contrast matrix is missing, this should be a "ftest" and the contrast matrix is generated automatically to cover the whole linear space except for testing covariates. If the Contrast matrix is given, this could be "ftest" or "ttest". The default is "ttest" (for backward compatability). For T-test, the code will do a series of T-test, where each T-test corresponds to a row in the contrast matrix.
测试类型。这可能是F-检验或t检验。如果对比矩阵丢失，这应该是“FTEST”和对比度矩阵自动生成覆盖整个测试协变量的线性空间。如果对比矩阵，这可能是的“FTEST”或“TTEST”。默认是“TTEST”（向后兼容性）。 T-检验，代码做了一系列的T检验，每个T-测试对比矩阵的行对应。
参数：shuffle.method
Data shuffling method. "sample" for sample shuffling and "resid" for residual shuffling. Read "Data Shuffling" section for detail.
数据洗牌方法。 “样品”为样品洗牌“重油”洗牌剩余。阅读“数据洗牌”的详细信息部分。
参数：MME.method
The method used to solve the Mixed Model Equations. See fitmaanova for detail. This parameter only applies for mixed effects model permutation test. Default method is "REML". The variance components for observed data will be used for permuted data. It will greatly increase the speed but you may lose power in statistical test in some cases.
所采用的方法来解决混合模型方程。看到fitmaanova细节。此参数只适用于混合效应模型置换测试。默认的方法是“REML法”。置换的数据将被用于观测数据的方差分量。这将大大提高速度，但你可能会失去在某些情况下，统计检验的权力。
参数：test.method
An integer vector of two elements to indicate which F test to carry. Default is c(1,1), which means do F1 and Fs test.
两个元素的指示进行的F检验的整数向量。默认为C（1,1），这意味着F1和FS测试。
参数：pval.pool
A logical value to indicate whether to use pooled permutation F values to calculate the P values.
一个逻辑值，指示是否使用集中排列的F值来计算P值。
参数：verbose
A logical value to indicate whether to display some message for calculation progress.
一个逻辑值，指明是否显示计算进度的一些消息。
Details
详情----------Details----------
If user provide a comparison matrix, this function will perform T-test on the given comparison(s). Otherwise, this function will perform F-test for the given term.
如果用户提供了一个比较矩阵，此功能将执行在给定的比较（S）T-检验。否则，此函数将执行给定的期限为F-检验。
There are three types of tests available. All three tests are based on the gene-specific ANOVA model. F1 is the usual F statistic, Fs is based on the James-Stein shrinkage estimates of the error variance.
可用的测试有三种类型。所有这三个测试是基于对特定基因的变异数模型。 F1是一般的F统计，财政司司长是根据詹姆斯·斯坦收缩估计误差方差。
Permutation tests can run on MPI cluster. This feature is only available for Unix/Linux system. Several other R packages (such like SNOW, Rmpi, etc.) are needed for using cluster. You may need help from your system administrator to setup LAM-MPI correctly. For detailed information on LAM-MPI cluster setup and the cluster usage in R, read "MPI\_README" distributed with the package.
排列测试，可以运行基于MPI的聚类。此功能仅适用于Unix / Linux系统。几个其他R包（例如像雪，Rmpi等）都需要使用聚类。您可能需要从您的系统管理员的帮助下，正确设置的LAM-MPI。 LAM-MPI的聚类设置和研发聚类使用的详细信息，请阅读“的MPI \ _README”包分配。
值----------Value----------
An object of class matest, which is a list of the following components:
类对象matest，这是以下几部分组成名单：
参数：model
Input model object.
输入模型对象。
参数：term
The input term(s) to be tested.
输入项（S）进行测试。
参数：dfde
Denominator's degree of freedom for the test.
分母的自由度的测试。
参数：dfnu
Numerator's degree of freedom for the test. Note that this is always 1 for T-test.
测试分子的自由度。请注意，这始终是T-检验1。
参数：obsAnova
An object of fitmaanova, which is the ANOVA model fitting result on the original data.
fitmaanova，这是方差分析模型对原始数据的拟合结果的对象。
参数：Contrast
The contrast matrix used in the test.
在测试中使用的对比矩阵。
参数：n.perm
Number of permutations.
排列数。
参数：shuffle
Shuffle style
Shuffle的风格
参数：pval.pool
Use pooled P value or not.
使用汇集P值或不。
参数：F1, Fs
Objects of four different F tests results. All or any of them could be there according to the requested F test method. Each of them contains the following fields:
四种不同的F检验结果的对象。全部或任何他们可能是有根据的要求的F检验方法。他们每个人都包含以下字段：
FobsF value for the observed data.
FobsF值的观测数据。
PtabTabulated P values for the observed data.
观测数据PtabTabulated P值。
PvalpermNominal permutation P values for each gene. This field will be unavailable if user do not do permutation test.
每一个基因的排列PvalpermNominal P值。这一领域将是不可用的，如果用户不这样做排列测试。
PvalmaxFWER one-step adjusted P values from the permutation test.
PvalmaxFWER一步调整置换检验的P值。
All the F values and P values are matrices. The number of rows in the matrices equals to the number of genes. For F-test, the number of columns will be one. For T-test, the number of columns equals to the number of tests carried.
所有的F值和P值矩阵。矩阵中的行数等于基因数量。对于F-检验，列数会之一。对于T-检验，列数等于进行测试。
数据改组----------Data Shuffling----------
Data shuffling method is a crucial part in the permutation test. Currently there are two shuffling method available, residual shuffling and sample shuffling.
数据洗牌方法是置换测试中的重要组成部分。目前有两个洗牌方法，剩余洗牌和样品洗牌。
Residual shuffling is to shuffle the null model residuals within gene without replacement.
剩余洗牌洗牌空模型的残差基因内无需更换。
Sample shuffling is to shuffle the samples based on the nesting relationship among the experimental factors in the model. For sample shuffling, you need to make sure you have a good sample size. Otherwise the result may be biased.
样品洗牌洗牌的基础上，模型中的实验因素之间的嵌套关系的样本。洗牌的样品，你需要确保你有一个很好的样本大小。否则，结果可能会失之偏颇。
作者（S）----------Author(s)----------
Hao Wu
参考文献----------References----------
expression in cDNA Microarray experiments, Genome Biology 4:210.
Improved statistical tests for differential gene expression by shrinking variance components, to be submitted.
参见----------See Also----------
makeModel, fitmaanova
makeModel，fitmaanova
举例----------Examples----------
# load in abf1 data[在ABF1数据加载]
data(abf1)
## Not run: [＃无法运行：]
fit.full.mix <- fitmaanova(abf1, formula = ~Strain+Sample,
random = ~Sample)
ftest.all = matest(abf1, fit.full.mix, test.method=c(1,1),
shuffle.method="sample", term="Strain", n.perm= 100)
C = matrix(c(1,-1,0,1,0,-1), ncol=3, byrow=T)
ftest.pair = matest(abf1, fit.full.mix, Contrast = C,
term="Strain", n.perm=100)
## End(Not run)[＃结束（不运行）]
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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 2012-2-25 23:55:51
