CADM.global(ape)
CADM.global()所属R语言包：ape
Congruence among distance matrices
同余之间的距离矩阵
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function CADM.global compute and test the coefficient of concordance among several distance matrices through a permutation test.
功能CADM.global计算和测试的一致性系数之间的距离矩阵，通过一个置换检验。
Function CADM.post carries out a posteriori permutation tests of the contributions of individual distance matrices to the overall concordance of the group.
功能CADM.post进行后验置换的个人的距离矩阵的贡献，本集团的整体一致性测试。
Use in phylogenetic analysis: to identify congruence among distance matrices (D) representing different genes or different types of data. Congruent D matrices correspond to data tables that can be used together in a combined phylogenetic or other type of multivariate analysis.
系统发育分析：识别之间的距离矩阵（D）代表不同的基因或不同类型的数据的一致性。全等ð矩阵对应的组合的系统发育或其他类型的多变量分析中，可以一起使用的数据表。
用法----------Usage----------
CADM.global(Dmat, nmat, n, nperm=99, make.sym=TRUE, weights=NULL,
silent=FALSE)
CADM.post (Dmat, nmat, n, nperm=99, make.sym=TRUE, weights=NULL,
mult="holm", mantel=FALSE, silent=FALSE)
参数----------Arguments----------
参数：Dmat
A text file listing the distance matrices one after the other, with or without blank lines in-between. Each matrix is in the form of a square distance matrix with 0's on the diagonal.
列出的距离矩阵的一前一后，带或不带中的空白行之间的文本文件。每个矩阵是与0的对角线上的一个方形的距离矩阵的形式。
参数：nmat
Number of distance matrices in file Dmat.
文件DMAT的距离矩阵。
参数：n
Number of objects in each distance matrix. All matrices must have the same number of objects.
每个距离矩阵中的对象数。所有矩阵必须具有相同数量的对象。
参数：nperm
Number of permutations for the tests of significance.
为显着性检验的排列数。
参数：make.sym
TRUE: turn asymmetric matrices into symmetric matrices by averaging the two triangular portions. FALSE: analyse asymmetric matrices as they are.
TRUE：将非对称矩阵为对称矩阵的平均两个三角部分。 FALSE：分析非对称矩阵，因为它们。
参数：weights
A vector of positive weights for the distance matrices. Example: weights = c(1,2,3). NULL (default): all matrices have same weight in the calculation of W.
一个积极的权重向量的距离矩阵。例：权重= C（1,2,3）。 NULL（默认）：所有矩阵具有相同重量的计算W.
参数：mult
Method for correcting P-values in multiple testing. The methods are "holm" (default), "sidak", and "bonferroni". The Bonferroni correction is overly conservative; it is not recommended. It is included to allow comparisons with the other methods.
用于校正的P值在多个测试方法。其方法是“冬青”（默认），“通富”，和“邦费罗尼”的。 Bonferroni校正过于保守，不建议。它是包括允许与其他方法比较。
参数：mantel
TRUE: Mantel statistics will be computed from ranked distances, as well as permutational P-values. FALSE (default): Mantel statistics and tests will not be computed.
TRUE：计算曼特尔统计，从排名第一的距离，以及permutational P-值。 FALSE（默认值）：的曼特尔统计和测试，将不会被计算。
参数：silent
TRUE: informative messages will not be printed, but stopping messages will. Option useful for simulation work. FALSE: informative messages will be printed.
TRUE：信息性消息不会被打印出来，但停止的消息。该选项用于模拟工作。 FALSE：信息性消息将被打印出来。
Details
详细信息----------Details----------
Dmat must contain two or more distance matrices, listed one after the other, all of the same size, and corresponding to the same objects in the same order. Raw data tables can be transformed into distance matrices before comparison with other such distance matrices, or with data that have been obtained as distance matrices, e.g. serological or DNA hybridization data. The distances will be transformed to ranks before computation of the coefficient of concordance and other statistics.
Dmat必须包含两个或更多的距离矩阵，列出的一前一后，所有的大小相同，并以相同的顺序相同的对象相对应。前比较，与其他这样的距离矩阵，或已得到距离矩阵的数据，例如，信息数据表，可以转化成距离矩阵血清学或DNA杂交技术数据。的距离将被改造前的职级系数计算的一致性和其他统计数据。
CADM.global tests the global null hypothesis that all matrices are incongruent. If the global null is rejected, function CADM.post can be used to identify the concordant (H0 rejected) and discordant matrices (H0 not rejected) in the group. If a distance matrix has a negative value for the Mantel.mean statistic, that matrix clearly does not belong to the group. Remove that matrix (if there are more than one, remove first the matrix that has the most strongly negative value for Mantel.mean) and run the analysis again.
CADM.global测试全球性的零假设，即所有的矩阵是不一致的。被拒绝，如果全球空函数CADM.post可用于识别的一致（H0否决），不和谐的矩阵（不拒绝H0）组中的。如果距离矩阵为负值的Mantel.mean统计，矩阵显然不属于该组的。删除该矩阵（如果有不止一个，首先删除矩阵具有最强烈的负值Mantel.mean），并再次运行分析。
The corrections used for multiple testing are applied to the list of P-values (P) produced in the a posteriori tests; they take into account the number of tests (k) carried out simulatenously (number of matrices, parameter nmat).
被施加到多个测试所用的改正的P-值（P）的后验测试中产生的列表;他们考虑到测试的数量（十一）开展simulatenously（矩阵的数量，参数nmat ）。
The Holm correction is computed after ordering the P-values in a list with the smallest value to the left. Compute adjusted P-values as:
霍尔姆校正计算订货后的P-列表中的值与最小的值到左边。计算P-值调整为：
where i is the position in the ordered list. Final step: from left to right, if an adjusted P_corr in the ordered list is smaller than the one occurring at its left, make the smallest one equal to the largest one.
其中i是有序列表中的位置。最后步骤：由左到右，如果调整P_corr有序列表中的是小于1发生在它的左边，使最小的一个等于最大的一个。
The Sidak correction is:
的通富校正：
The Bonferonni correction is:
Bonferonni校正是：
值----------Value----------
CADM.global produces a small table containing the W, Chi2, and Prob.perm statistics described in the following list. CADM.post produces a table stored in element A_posteriori_tests, containing Mantel.mean, Prob, and Corrected.prob statistics in rows; the columns correspond to the k distance matrices under study, labeled Dmat.1 to Dmat.k. If parameter mantel is TRUE, tables of Mantel statistics and P-values are computed among the matrices.
CADM.global产生一个小的表含W，χ^ 2，在下面的列表中描述的Prob.perm统计的。 CADM.post产生元件存储在一个表A_posteriori_tests，，含有Mantel.mean，概率，和统计行中Corrected.prob;列对应于所研究的k距离矩阵，标记Dmat.1 DMAT。 K。如果参数mantel为TRUE，曼特尔统计数字和P值的表之间的矩阵计算。
参数：W
Kendall's coefficient of concordance, W (Kendall and Babington Smith 1939; see also Legendre 2010).
肯德尔和谐系数，W（Kendall和巴宾顿·史密斯1939勒让德2010）。
参数：Chi2
Friedman's chi-square statistic (Friedman 1937) used in the permutation test of W.
弗里德曼的卡方统计量（弗里德曼1937年）中使用的置换检验W.
参数：Prob.perm
Permutational probability.
Permutational概率。
参数：Mantel.mean
Mean of the Mantel correlations, computed on rank-transformed distances, between the distance matrix under test and all the other matrices in the study.
均值的Mantel相关性，计算等级变换的距离，下测试与在研究中的所有其他的矩阵之间的距离矩阵。
参数：Prob
Permutational probabilities, uncorrected.
Permutational概率，裸。
参数：Corrected prob
Permutational probabilities corrected using the method selected in parameter mult.
Permutational概率纠正使用参数mult选择的方法。
参数：Mantel.cor
Matrix of Mantel correlations, computed on rank-transformed distances, among the distance matrices.
曼特尔的相关性，计算矩阵的秩转化的距离，之间的距离矩阵。
参数：Mantel.prob
One-tailed P-values associated with the Mantel correlations of the previous table. The probabilities are computed in the right-hand tail. H0 is tested against the alternative one-tailed hypothesis that the Mantel correlation under test is positive. No correction is made for multiple testing.
单尾P-值与上表中的Mantel相关性。的概率的计算在右手的尾巴。 H0的测试是针对替代单尾假设下测试是积极的Mantel相关。不改正的，由多个测试。
（作者）----------Author(s)----------
Pierre Legendre, Universite de Montreal
参考文献----------References----------
revisited. Australian and New Zealand Journal of Statistics, 46, 615–629.
实例----------Examples----------
# Examples 1 and 2: 5 genetic distance matrices computed from simulated DNA[实施例1和实施例2：5从模拟DNA计算的遗传距离矩阵]
# sequences representing 50 taxa having evolved along additive trees with[沿添加剂树木序列来自50个类群的进化]
# identical evolutionary parameters (GTR+ Gamma + I). Distance matrices were[相同的进化参数（GTR +γ+ I）。距离矩阵]
# computed from the DNA sequence matrices using a p distance corrected with the[使用AP的距离从该DNA序列矩阵计算与校正]
# same parameters as those used to simulate the DNA sequences. See Campbell et[用于模拟的DNA序列的那些相同的参数。 Campbell等]
# al. (2009) for details.[人。 （2009）的详细信息。]
# Example 1: five independent additive trees. Data provided by V. Campbell.[例1：5个独立的添加剂树木。数据提供五坎贝尔。]
data(mat5Mrand)
res.global <- CADM.global(mat5Mrand, 5, 50)
# Example 2: three partly similar trees, two independent trees.[实施例2：三个部分类似的树，两个独立的树木。]
# Data provided by V. Campbell.[数据提供五坎贝尔。]
data(mat5M3ID)
res.global <- CADM.global(mat5M3ID, 5, 50)
res.post <- CADM.post(mat5M3ID, 5, 50, mantel=TRUE)
# Example 3: three matrices respectively representing Serological[实施例3：三个矩阵分别代表的血清学]
# (asymmetric), DNA hybridization (asymmetric) and Anatomical (symmetric)[DNA杂交技术（非对称），（不对称）和解剖（对称）]
# distances among 9 families. Data from Lapointe et al. (1999).[9个家庭之间的距离。从拉波因特等人的数据。 （1999年）。]
data(mat3)
res.global <- CADM.global(mat3, 3, 9, nperm=999)
res.post <- CADM.post(mat3, 3, 9, nperm=999, mantel=TRUE)
# Example 4, showing how to bind two D matrices (cophenetic matrices[例4，显示出如何绑定两个D矩阵（cophenetic矩阵的]
# in this example) into a file using rbind(), then run the global test.[在这个例子中）使用rbind（）到一个文件中，然后运行全球测试。]
a <- rtree(5)
b <- rtree(5)
A <- cophenetic(a)
B <- cophenetic(b)
x <- rownames(A)
B <- B[x, x]
M <- rbind(A, B)
CADM.global(M, 2, 5)
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 2012-9-11 21:56:35
