kendall.global(vegan)
kendall.global()所属R语言包：vegan
Kendall coefficient of concordance
肯德尔和谐系数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function kendall.global computes and tests the coefficient of concordance among several judges (variables, species) through a permutation test.
函数kendall.global通过置换检验的几位法官（变量，种）之间的一致性系数计算和测试。
Function kendall.post carries out a posteriori tests of the contributions of individual judges (variables, species) to the overall concordance of their group through permutation tests.
功能kendall.post进行后验测试的贡献，他们的集团的整体一致通过排列测验个别法官（变量，物种）。
If several groups of judges are identified in the data table, coefficients of concordance (kendall.global) or a posteriori tests (kendall.post) will be computed for each group separately. Use in ecology: to identify significant species associations.
如果法官的几组识别的数据表中，系数的一致性（kendall.global）或后验测试（kendall.post）将被计算为每个组分开。在生态学识别显着的种间关联。
用法----------Usage----------
kendall.global(Y, group, nperm = 999, mult = "holm")
kendall.post(Y, group, nperm = 999, mult = "holm")
参数----------Arguments----------
参数：Y
Data file (data frame or matrix) containing quantitative or semiquantitative data. Rows are objects and columns are judges (variables). In community ecology, that table is often a site-by-species table.
数据文件（数据框或矩阵），其中包含定量或半定量的数据。行对象和列的法官（变量）。在社会生态中，该表通常是一个网站种类表。
参数：group
A vector defining how judges should be divided into groups. See example below. If groups are not explicitly defined, all judges in the data file will be considered as forming a single group.
一个向量，，确定法官应如何分成小组。见下面的例子。如果组没有明确的定义，在数据文件中的所有法官将被视为形成一个组。
参数：nperm
Number of permutations to be performed. Default is 999.
要执行的排列数目。默认值是999。
参数：mult
Correct P-values for multiple testing using the alternatives described in p.adjust and in addition "sidak" (see Details). The Bonferroni correction is overly conservative; it is not recommended. It is included to allow comparisons with the other methods.
正确的P-值的多个测试使用的替代品在p.adjust除了"sidak"（详见）。 Bonferroni校正过于保守，不建议。它是包括允许与其他方法比较。
Details
详细信息----------Details----------
Y must contain quantitative data. They will be transformed to ranks within each column before computation of the coefficient of concordance.
Y必须包含定量数据。他们将被改造成和谐系数计算每列队伍前。
The search for species associations described in Legendre (2005) proceeds in 3 steps:
搜索的种间关联勒让德（2005年）中描述的3个步骤进行：
(1) Correlation analysis of the species. A possible method is to compute Ward's agglomerative clustering of a matrix of correlations among the species. In detail: (1.1) compute a Pearson or Spearman correlation matrix (correl.matrix) among the species; (1.2) turn it into a distance matrix: mat.D = as.dist(1-correl.matrix); (1.3) carry out Ward's hierarchical clustering of that matrix using hclust: clust.ward = hclust(mat.D, "ward"); (1.4) plot the dendrogram: plot(clust.ward, hang=-1); (1.5) cut the dendrogram in two groups, retrieve the vector of species membership: group.2 = cutree(clust.ward, k=2). (1.6) After steps 2 and 3 below, you may have to come back and try divisions of the species into k = 3, 4, 5, ... groups.
（1）相关性分析的品种。一种可能的方法是计算病区的聚类的物种之间的相关性的矩阵。详细步骤如下：（1.1）计算的物种之间的Pearson或Spearman相关系数矩阵（correl.matrix）（1.2）把它变成一个距离矩阵：mat.D = as.dist(1-correl.matrix);（1.3）进行病房的层次聚类矩阵使用hclust：clust.ward = hclust(mat.D, "ward");（1.4）图树状图：plot(clust.ward, hang=-1);，削减树状图（1.5）两组，检索矢量物种成员：group.2 = cutree(clust.ward, k=2)。 （1.6）下面的步骤2和步骤3后，你可能要回来，尝试分裂的品种为K = 3，4，5，...组。
(2) Compute global tests of significance of the 2 (or more) groups using the function kendall.global and the vector defining the groups. Groups that are not globally significant must be refined or abandoned.
（2）计算全球测试的2组（或多个）使用的功能kendall.global和矢量定义的基团的意义。组是不是全局意义的，必须加以完善或放弃。
(3) Compute a posteriori tests of the contribution of individual species to the concordance of their group using the function kendall.post and the vector defining the groups. If some species have negative values for "Spearman.mean", this means that these species clearly do not belong to the group, hence that group is too inclusive. Go back to (1.5) and cut the dendrogram more finely. The left and right groups can be cut separately, independently of the levels along the dendrogram; write your own vector of group membership if cutree does not produce the desired groups.
（3）计算后验测试的贡献，个别品种的一致性组的功能kendall.post和矢量定义的组。如果某些物种有负值为“Spearman.mean”，这意味着这些物种显然不属于该组，因此该组的是太包容性的。回到（1.5）和更细切树状图。左，右分别可削减，独立的树状图沿水平;写你自己的向量组成员cutree如果不会产生所需的组。
The corrections used for multiple testing are applied to the list of P-values (P); they take into account the number of tests (k) carried out simultaneously (number of groups in kendall.global, or number of species in kendall.post). The corrections are performed using function p.adjust; see that function for the description of the correction methods. In addition, there is 艩id谩k correction which defined as P_{corr} = 1 -(1 - P)^k.
多个测试所用的改正被应用到列表中的P值（P），他们考虑到测试的数量（K）同步进行（组数kendall.global，或种数的 kendall.post“）。使用功能p.adjust;看到该函数的描述的修正方法进行更正。此外，是定义为P_{corr} = 1 -(1 - P)^kŠidák校正。
值----------Value----------
A table containing the following information in rows. The columns correspond to the groups of "judges" defined in vector "group". When function Kendall.post is used, there are as many tables as the number of predefined groups.
一个表，其中包含以下信息行。列对应于定义的“法官”矢量“本集团”组。当函数Kendall.post时，有许多预定义的组数表。
参数：W
Kendall's coefficient of concordance, W.
肯德尔系数的一致性，W.
参数：F
F statistic. F = W*(m-1)/(1-W) where m is the number of judges.
F统计量。 F = W *（M-1）/（1-W），其中m是法官的人数。
参数：Prob.F
Probability associated with the F statistic, computed from the F distribution with nu1 = n-1-(2/m) and nu2 = nu1*(m-1); n is the number of objects.
概率与F统计量相关联的，从F分布计算NU1 =正-1-（2 / m）和NU2 = NU1 *第（m-1），n为的对象的数量。
参数：Corrected prob.F
Probabilities associated with F, corrected using the method selected in parameter mult. Shown only if there are more than one group.
与F相关的概率，纠正使用参数mult选择的方法。所示，仅当有一个以上的基团。
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），用于置换检验W.
参数：Prob.perm
Permutational probabilities, uncorrected.
Permutational概率，裸。
参数：Corrected prob.perm
Permutational probabilities corrected using the method selected in parameter mult. Shown only if there are more than one group.
Permutational概率纠正使用参数mult选择的方法。所示，仅当有一个以上的基团。
参数：Spearman.mean
Mean of the Spearman correlations between the judge under test and all the other judges in the same group.
平均法官根据测试，在同一组中的所有其他法官之间的Spearman相关。
参数：W.per.species
Contribution of the judge under test to the overall concordance statistic for that group.
法官根据测试，该组的整体一致性统计的贡献。
（作者）----------Author(s)----------
F. Guillaume Blanchet, University of Alberta, and Pierre
Legendre, Universit茅 de Montr茅al
参考文献----------References----------
implicit in the analysis of variance. Journal of the American Statistical Association 32: 675-701.
rankings. Annals of Mathematical Statistics 10: 275-287.
concordance revisited. Journal of Agricultural, Biological, and Environmental Statistics 10: 226-245.
Research Design. SAGE Publications (in press).
the behavioral sciences. 2nd edition. McGraw-Hill, New York.
参见----------See Also----------
cor, friedman.test, hclust, cutree, kmeans,
cor，friedman.test，hclust，cutree，kmeans，
实例----------Examples----------
data(mite)
mite.hel <- decostand(mite, "hel")
# Reproduce the results shown in Table 2 of Legendre (2005), a single group[重现德（Legendre）在表2中所示的结果（2005年），一个单一的基]
mite.small <- mite.hel[c(4,9,14,22,31,34,45,53,61,69),c(13:15,23)]
kendall.global(mite.small, nperm=49)
kendall.post(mite.small, mult="holm", nperm=49)
# Reproduce the results shown in Tables 3 and 4 of Legendre (2005), 2 groups[重现德（Legendre）在表3和表4所示的结果（2005年），2组]
group <-c(1,1,2,1,1,1,1,1,2,1,1,1,1,1,1,2,1,2,1,1,1,1,2,1,2,1,1,1,1,1,2,2,2,2,2)
kendall.global(mite.hel, group=group, nperm=49)
kendall.post(mite.hel, group=group, mult="holm", nperm=49)
# NOTE: 'nperm' argument usually needs to be larger than 49.[注：的“nperm”的说法，通常需要大于49。]
# It was set to this low value for demonstration purposes.[这样低的值设置为示范的目的。]
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-10-1 15:07:14
