rankindex(vegan)
rankindex()所属R语言包：vegan
Compares Dissimilarity Indices for Gradient Detection
比较相异指数梯度检测
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Rank correlations between dissimilarity indices and gradient separation.
排名之间的相关性的相异指数和梯度分离。
用法----------Usage----------
rankindex(grad, veg, indices = c("euc", "man", "gow", "bra", "kul"),
stepacross = FALSE, method = "spearman", ...)
参数----------Arguments----------
参数：grad
The gradient variable or matrix.
梯度变量或矩阵。
参数：veg
The community data matrix.
的社区数据矩阵。
参数：indices
Dissimilarity indices compared, partial matches to alternatives in vegdist. Alternatively, it can be a (named) list of functions returning objects of class 'dist'.
相异指数相比，部分比赛在vegdist的替代品。此外，它可以是一个（命名）的功能列表返回对象类dist的。
参数：stepacross
Use stepacross to find a shorter path dissimilarity. The dissimilarities for site pairs with no shared species are set NA using no.shared so that indices with no fixed upper limit can also be analysed.
使用stepacross找到更短的路径差异性。没有共享的物种的异同网站对NA使用no.shared“这样，没有固定的上限指标也可以进行分析。
参数：method
Correlation method used.
相关方法。
参数：...
Other parameters to stepacross.
其他参数stepacross。
Details
详细信息----------Details----------
A good dissimilarity index for multidimensional scaling should have a high rank-order similarity with gradient separation. The function compares most indices in vegdist against gradient separation using rank correlation coefficients in cor.test. The gradient separation between each point is assessed as Euclidean distance for continuous variables, and as Gower metric for mixed data using function daisy when grad has factors.
一个很好的相异指数为多维尺度应该有一个高的排名顺序与梯度分离相似。 vegdist的反梯度分离使用秩相关系数在cor.test的功能比较指数。每个点之间的梯度分离被评定为欧氏距离为连续变量，如高尔度量数据好坏参半，使用功能daisygrad有因素。
The indices argument can accept any dissimilarity indices besides the ones calculated by the vegdist function. For this, the argument value should be a (possibly named) list of functions. Each function must return a valid 'dist' object with dissimilarities, similarities are not accepted and should be converted into dissimilarities beforehand.
indices参数可以接受任何相异指数除了vegdist函数的计算。对于这一点，参数值应该是一个（可能命名）的函数列表。每个函数必须返回一个有效的dist的对象相异，不接受相似，应事先转换成不同点。
值----------Value----------
Returns a named vector of rank correlations.
返回一个命名为向量的等级相关。
注意----------Note----------
There are several problems in using rank correlation coefficients. Typically there are very many ties when n(n-1)/2 gradient separation values are derived from just n observations. Due to floating point arithmetics, many tied values differ by machine epsilon and are arbitrarily ranked differently by rank used in cor.test. Two indices which are identical with certain transformation or standardization may differ slightly (magnitude 10^{-15}) and this may lead into third or fourth decimal instability in rank correlations. Small differences in rank correlations should not be taken too seriously. Probably this method should be replaced with a sounder method, but I do not yet know which... You may experiment with mantel, anosim or even protest.
使用秩相关系数有几个问题。通常情况下，有很多关系时n(n-1)/2来自梯度分离值只是n观察。由于浮点算术，许多重复值不同的机器精度，并随意排名不同的rank在cor.test使用。两个指数具有一定的转换或标准化是相同的，可能略有不同（大小10^{-15}），这可能会导致第三或第四位小数不稳定的等级相关。排名的相关性小的差异不应该太认真对待。也许这种方法应该更换一个更完善的方法，但我还不知道哪...您可以尝试mantel，anosim或protest。
Earlier version of this function used method = "kendall", but that is far too slow in large data sets.
使用此功能method = "kendall"，但过于缓慢，在大型数据集的早期版本。
The functions returning dissimilarity objects should be self contained, because the ... argument passes additional parameters to stepacross and not to the functions supplied via the indices argument.
应该是自包含的功能返回相异对象的，因为...参数传递额外的参数给stepacross“，而不是通过indices参数提供的功能。
（作者）----------Author(s)----------
Jari Oksanen, with additions from Peter Solymos
参考文献----------References----------
L. (1987). Compositional dissimilarity as a robust measure of
参见----------See Also----------
vegdist, stepacross, no.shared, monoMDS, cor, Machine, and for alternatives anosim, mantel and
vegdist，stepacross，no.shared，monoMDS，cor，Machine，替代品anosim，mantel和
实例----------Examples----------
data(varespec)
data(varechem)
## The next scales all environmental variables to unit variance.[＃下一个尺度单位方差的所有环境变量。]
## Some would use PCA transformation.[＃有些人会使用PCA的转换。]
rankindex(scale(varechem), varespec)
rankindex(scale(varechem), wisconsin(varespec))
## Using non vegdist indices as functions[＃使用非vegdist的指数作为功能]
funs <- list(Manhattan=function(x) dist(x, "manhattan"),
Gower=function(x) cluster:::daisy(x, "gower"),
Ochiai=function(x) designdist(x, "1-J/sqrt(A*B)"))
rankindex(scale(varechem), varespec, funs)
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。
|
发表于 2012-10-1 15:12:01
