wcmdscale(vegan)
wcmdscale()所属R语言包：vegan
Weighted Classical (Metric) Multidimensional Scaling
（公制）加权古典多维标度
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Weighted classical multidimensional scaling, also known as weighted principal coordinates analysis.
加权古典多维标度，也被称为加权主坐标分析。
用法----------Usage----------
wcmdscale(d, k, eig = FALSE, add = FALSE, x.ret = FALSE, w)
参数----------Arguments----------
参数：d
a distance structure such as that returned by dist or a full symmetric matrix containing the dissimilarities.
距离结构，如返回dist或全对称矩阵的异同。
参数：k
the dimension of the space which the data are to be represented in; must be in {1,2,…,n-1}. If missing, all dimensions with above zero eigenvalue.
该数据要被表示的空间的维数;必须在{1,2,…,n-1}。如果缺少，所有尺寸大于零特征值。
参数：eig
indicates whether eigenvalues should be returned.
指示是否应该返回特征值。
参数：add
logical indicating if an additive constant c* should be computed, and added to the non-diagonal dissimilarities such that all n-1 eigenvalues are non-negative. <STRONG>Not implemented</STRONG>.
逻辑表示，如果添加剂的常数c*应进行计算，并添加到非对角线异同使得所有n-1特征值都是非负的。 <STRONG>不执行</ STRONG>。
参数：x.ret
indicates whether the doubly centred symmetric distance matrix should be returned.
指示是否应该返回的双中心对称的距离矩阵。
参数：w
Weights of points.
点的权重。
Details
详细信息----------Details----------
Function wcmdscale is based on function cmdscale (package stats of base R), but it uses point weights. Points with high weights will have a stronger influence on the result than those with low weights. Setting equal weights w = 1 will give ordinary multidimensional scaling.
功能wcmdscale功能的基础上，cmdscale（包stats碱基r），但它使用点的权重。点具有较高的权重，将具有更强的影响力比与低重量的结果。设置相同的权重w = 1会给普通的多维标度。
值----------Value----------
If eig = FALSE and x.ret = FALSE (default), a matrix with k columns whose rows give the coordinates of the points chosen to represent the dissimilarities. Otherwise, an object of class wcmdscale containing the components that are mostly similar as in cmdscale:
如果eig = FALSE和x.ret = FALSE（默认），矩阵k列的行给所选择的点的坐标来表示的异同。否则，类的一个对象wcmdscale包含的组件大多是类似的，作为在cmdscale：
参数：points
a matrix with k columns whose rows give the coordinates of the points chosen to represent the dissimilarities.
矩阵k列的行给所选择的点的坐标来表示的异同。
参数：eig
the n-1 eigenvalues computed during the scaling process if eig is true.
n-1特征值计算的缩放过程中，，如果eig是真实的。
参数：x
the doubly centred and weighted distance matrix if x.ret is true.
如果x.ret是真正的双中心和加权距离矩阵。
参数：GOF
Goodness of fit statistics for k axes. The first value is based on the sum of absolute values of all eigenvalues, and the second value is based on the sum of positive eigenvalues
k轴的统计数据的拟合优度。是根据所述第一值的所有本征值的绝对值的总和，和第二值是根据正的特征值的总和
参数：weights
Weights.
权重。
参数：negaxes
A matrix of scores for axes with negative eigenvalues scaled by the absolute eigenvalues similarly as points. This is NULL if there are no negative eigenvalues or k was specified, and would not include negative eigenvalues.
分数的矩阵负本征值的轴与缩放的绝对特征值同样points。这是NULL如果没有负本征值或k被指定，将不包括负本征值。
参考文献----------References----------
Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325–328.
Multivariate Analysis, London: Academic Press.
参见----------See Also----------
cmdscale. Also monoMDS, and isoMDS and sammon in package MASS.
cmdscale。 monoMDS和isoMDS和sammon的包MASS。
实例----------Examples----------
## Correspondence analysis as a weighted principal coordinates[＃作为加权的主要坐标的对应分析]
## analysis of Euclidean distances of Chi-square transformed data[＃卡方转换后的数据分析，欧氏距离]
data(dune)
rs <- rowSums(dune)/sum(dune)
d <- dist(decostand(dune, "chi"))
ord <- wcmdscale(d, w = rs, eig = TRUE)
## Ordinary CA[＃普通CA]
ca <- cca(dune)
## Eigevalues are numerically similar[＃Eigevalues在数值上是相似的]
ca$CA$eig - ord$eig
## Configurations are similar when site scores are scaled by[＃配置是类似的，当网站分数缩放]
## eigenvalues in CA[＃在CA的特征值]
procrustes(ord, ca, choices=1:19, scaling = 1)
plot(procrustes(ord, ca, choices=1:2, scaling=1))
## Reconstruction of non-Euclidean distances with negative eigenvalues[＃改的非欧氏距离，负本征值]
d <- vegdist(dune)
ord <- wcmdscale(d, eig = TRUE)
## Only positive eigenvalues:[＃只有正的特征值：]
cor(d, dist(ord$points))
## Correction with negative eigenvalues:[＃修正为负的特征值：]
cor(d, sqrt(dist(ord$points)^2 - dist(ord$negaxes)^2))
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 2012-10-1 15:15:30
