mantel(vegan)
mantel()所属R语言包：vegan
Mantel and Partial Mantel Tests for Dissimilarity Matrices
相异矩阵的曼特尔及部分曼特尔的测试
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function mantel finds the Mantel statistic as a matrix correlation between two dissimilarity matrices, and function mantel.partial finds the partial Mantel statistic as the partial matrix correlation between three dissimilarity matrices. The significance of the statistic is evaluated by permuting rows and columns of the first dissimilarity matrix.
函数mantel发现作为一个矩阵的两个相异矩阵，和功能之间的相关性的Mantel统计mantel.partial发现3相异矩阵之间的相关性的部分矩阵的部分曼特尔统计。的统计信息的意义是通过置换第一相异矩阵的行和列的评价。
用法----------Usage----------
mantel(xdis, ydis, method="pearson", permutations=999, strata)
mantel.partial(xdis, ydis, zdis, method = "pearson", permutations = 999,
strata)
参数----------Arguments----------
参数：xdis, ydis, zdis
Dissimilarity matrices or a dist objects.
的相异矩阵或一个dist对象。
参数：method
Correlation method, as accepted by cor: "pearson", "spearman" or "kendall".
相关法，作为接受cor："pearson"，"spearman"或"kendall"。
参数：permutations
Number of permutations in assessing significance.
号码的排列在评估的意义。
参数：strata
An integer vector or factor specifying the strata for permutation. If supplied, observations are permuted only within the specified strata.
一个整数向量或因素确定地层的置换。如果提供，观测置换仅在指定的阶层。
Details
详细信息----------Details----------
Mantel statistic is simply a correlation between entries of two dissimilarity matrices (some use cross products, but these are linearly related). However, the significance cannot be directly assessed, because there are N(N-1)/2 entries for just N observations. Mantel developed asymptotic test, but here we use permutations of N rows and columns of dissimilarity matrix. See permutations for additional details on permutation tests in Vegan.
“曼特尔统计仅仅是的两个相异矩阵（一些使用交叉产品，但这些是线性相关的）的条目之间的相关性。然而，其意义不能直接评估，因为有N(N-1)/2项只是N观察。的曼特尔开发渐近测试，但在这里我们使用N的相异度矩阵的行和列的排列。见permutations排列测试，素食主义者的更多细节。
Partial Mantel statistic uses partial correlation conditioned on the third matrix. Only the first matrix is permuted so that the correlation structure between second and first matrices is kept constant. Although mantel.partial silently accepts other methods than "pearson", partial correlations will probably be wrong with other methods.
部分的曼特尔统计使用的第三个矩阵的偏相关条件。只有第一矩阵的置换，使得第二和第一矩阵之间的相关结构是保持恒定。虽然mantel.partial默默接受其他方法比"pearson"，局部相关性可能会是错误的其他方法。
The function uses cor, which should accept alternatives pearson for product moment correlations and spearman or kendall for rank correlations.
该函数使用cor，它应该接受的替代品pearson积矩相关系数和spearman或kendall等级相关。
值----------Value----------
The function returns a list of class mantel with following components:
该函数返回一个列表类mantel的下列组件：
参数：Call
Function call.
函数调用。
参数：method
Correlation method used, as returned by cor.test.
相关方法，返回的cor.test。
参数：statistic
The Mantel statistic.
曼特尔的统计数据。
参数：signif
Empirical significance level from permutations.
实证的显着性水平排列。
参数：perm
A vector of permuted values.
置换值的矢量。
参数：permutations
Number of permutations.
的排列数目。
注意----------Note----------
Legendre & Legendre (1998) say that partial Mantel correlations often are difficult to interpret.
勒让德和勒让德（1998）说，部分曼特尔的相关性，往往是难以解释的。
（作者）----------Author(s)----------
Jari Oksanen
参考文献----------References----------
current implementation is based on Legendre and Legendre.
Edition. Elsevier.
参见----------See Also----------
cor for correlation coefficients, protest (&ldquo rocrustes test”) for an alternative with ordination diagrams, anosim and mrpp for comparing dissimilarities against classification. For dissimilarity matrices, see vegdist or dist. See bioenv for selecting
cor相关系数，protest（“Procrustes测试”）的替代与协调图，anosim和mrpp比较对分类的不同点。对于相异矩阵，请参阅vegdist或dist。见bioenv选择
实例----------Examples----------
## Is vegetation related to environment?[＃植被相关的环境？]
data(varespec)
data(varechem)
veg.dist <- vegdist(varespec) # Bray-Curtis[布雷柯蒂斯]
env.dist <- vegdist(scale(varechem), "euclid")
mantel(veg.dist, env.dist)
mantel(veg.dist, env.dist, method="spear")
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。
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 2012-10-1 15:07:41
