monoMDS(vegan)
monoMDS()所属R语言包：vegan
Global and Local Non-metric Multidimensional Scaling and
全球和本地非度量多维标度和
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function implements Kruskal's (1964a,b) non-metric multidimensional scaling (NMDS) using monotone regression and primary (“weak”) treatment of ties. In addition to traditional global NMDS, the function implements local NMDS, linear and hybrid multidimensional scaling.
功能实现克鲁斯卡尔（1964a，b）非度量多维标度（NMDS）利用单调回归和主（“弱”的关系）治疗。除了传统的全球的NMDS，函数实现本地NMDS，直链和混合的多维尺度。
用法----------Usage----------
monoMDS(dist, y, k = 2, model = c("global", "local", "linear", "hybrid"),
threshold = 0.8, maxit = 200, weakties = TRUE, stress = 1,
scaling = TRUE, pc = TRUE, smin = 0.00001, sfgrmin = 0.00001,
sratmax=0.99999, ...)
## S3 method for class 'monoMDS'
scores(x, choices = NA, ...)
## S3 method for class 'monoMDS'
plot(x, choices = c(1,2), type = "t", ...)
参数----------Arguments----------
参数：dist
Input dissimilarities.
输入不同之处。
参数：y
Starting configuration. A random configuration will be generated if this is missing.
开始配置。如果没有这个，会产生一个随机配置的。
参数：k
Number of dimensions. NB., the number of points n should be n > 2*k + 1, and preferably higher in non-metric MDS.
的维数。注意，点的数量n应该是n > 2*k + 1，并优选高于非十进制MDS。
参数：model
MDS model: "global" is normal non-metric MDS with a monotone regression, "local" is non-metric MDS with separate regressions for each point, "linear" uses linear regression, and "hybrid" uses linear regression for dissimilarities below a threshold in addition to monotone regression. See Details.
MDS型号："global"是正常的单调回归，"local"非度量MDS的每个点有独立的回归，"linear"使用线性回归和"hybrid"的非度量MDS使用线性回归相异低于阈值，除了单调回归。查看详细信息。
参数：threshold
Dissimilarity below which linear regression is used alternately with monotone regression.
相异下面的线性回归交替使用与单调的回归。
参数：maxit
Maximum number of iterations.
最大迭代次数。
参数：weakties
Use primary or weak tie treatment, where equal observed dissimilarities are allowed to have different fitted values. if FALSE, then secondary (strong) tie treatment is used, and tied values are not broken.
使用小学或弱关系处理，等于观察到的不同点是允许有不同的拟合值。如果FALSE，二级（强）领带治疗使用，并捆绑值不破。
参数：stress
Use stress type 1 or 2 (see Details).
使用压力1型或2（见详情）。
参数：scaling
Scale final scores to unit root mean squares.
单位根的规模最终得分均方。
参数：pc
Rotate final scores to principal components.
旋转主要组成部分的最后得分。
参数：smin, sfgrmin, sratmax
Convergence criteria: iterations stop when stress drops below smin, scale factor of the gradient drops below sfgrmin, or stress ratio goes over sratmax (but is still < 1).
收敛条件：迭代停止，当压力下降到低于smin，比例因子的梯度下降到低于sfgrmin，或应力比超过sratmax（但仍然是< 1）。
参数：x
A monoMDS result.
AmonoMDS结果。
参数：choices
Dimensions returned or plotted. The default NA returns all dimensions.
尺寸退回或绘制。默认NA返回所有的尺寸。
参数：type
The type of the plot: "t" for text, "p" for points, and "n" for none.
该类型的图："t"的文本，"p"点，和"n"没有。
参数：...
Other parameters to the functions (ignored in monoMDS, passed to graphical functions in plot.).
其他参数的功能（忽视的monoMDS，plot）。传递给图形功能。
Details
详细信息----------Details----------
There are several versions of non-metric multidimensional scaling in R, but monoMDS offers the following unique combination of features:
非度量多维尺度在R有好几个版本，但monoMDS提供了以下独特的功能组合：
“Weak” treatment of ties (Kruskal 1964a,b), where tied dissimilarities can be broken in monotone regression. This is especially important for cases where compared sites share no species and dissimilarities are tied to their maximum value of one. Breaking ties allows these points to be at different distances and can help in recovering very long coenoclines (gradients). Function smacofSym (smacof package) also has adequate tie treatment.
“弱”的关系处理（秩1964a，B），其中捆绑的异同，可以打破单调回归。相比，站点共享的情况下，没有品种和不同点都与自己最大的价值之一，这一点尤其重要。打破关系，使这些点是在不同的距离，可以帮助恢复非常的长coenoclines（梯度）。功能smacofSym（smacof包）也有足够的领带治疗。
Handles missing values in a meaningful way.
以一种有意义的方式处理缺失值。
Offers “local” and “hybrid” scaling in addition to usual “global” NMDS (see below).
提供“本地”和“混合型”缩放除了通常的“全球性”的NMDS（见下文）。
Uses fast compiled code (isoMDS of the MASS package also uses compiled code).
使用快速编译的代码（isoMDSMASS包也使用编译的代码）。
Function monoMDS uses Kruskal's (1964b) original monotone regression to minimize the stress. There are two alternatives of stress: Kruskal's (1964a,b) original or “stress 1” and an alternative version or “stress 2” (Sibson 1972). Both of these stresses can be expressed with a general formula
功能monoMDS使用克鲁斯卡尔（1964b）原本单调的回归的，以尽量减少压力。压力有两个备选方案：克鲁斯卡尔（1964a，B）的原件或“压力”和另一种版本的“压力”（Sibson的1972年）。这些压力都可以用通式表示
where d are distances among points in ordination configuration, dhat are the fitted ordination distances, and dnull are the ordination distances under null model. For “stress 1” dnull = 0, and for “stress 2” dnull = dbar or mean distances. “Stress 2” can be expressed as stress^2 = 1 - R2, whereR2 is squared correlation between fitted values and ordination distances, and so related to the “linear fit” of stressplot.
其中d是统筹配置点之间的的距离，dhat距离的拟合协调，和dnull是空模型下的协调距离。对于“压力”dnull = 0“和”压力“2dnull = dbar或平均距离。 “胁迫2”可以表示为stress^2 = 1 - R2，其中R2是平方拟合值和协调距离之间的相关性，所以涉及到的“线性拟合”stressplot。
Function monoMDS can fit several alternative NMDS variants that can be selected with argument model. The default model = "global" fits global NMDS, or Kruskal's (1964a,b) original NMDS similar to isoMDS (MASS) or smacofSym (smacof). Alternative model = "local" fits local NMDS where independent monotone regression is used for each point (Sibson 1972). Alternative model = "linear" fits a linear MDS. This fits a linear regression instead of monotone, and is not identical to metric scaling or principal coordinates analysis (cmdscale) that performs an eigenvector decomposition of dissimilarities (Gower 1966). Alternative model = "hybrid" implements hybrid MDS that uses monotone regression for all points and linear regression for dissimilarities below or at a threshold dissimilarity in alternating steps (Faith et al. 1987). Function stressplot can be used to display the kind of regression in each model.
函数monoMDS可以容纳几种可供选择的的NMDS变种可以选择参数model。默认model = "global"适合全球的NMDS，或克鲁斯卡尔（1964a，B）原NMDS类似的isoMDS（MASS）smacofSym（smacof）。备选方案model = "local"适合当地的NMDS独立单调回归被用于每个点（Sibson的1972）。替代model = "linear"适合的线性MDS的。这符合一元线性回归，而不是单调的，而且是不相同的度量缩放或主坐标分析（cmdscale）的执行特征值分解的异同（高尔1966年）。备选方案model = "hybrid"实现混合MDS时使用单调回归交替步骤（信仰等人，1987）中的所有点和线性回归或低于在threshold相异的异同。函数stressplot可以用来显示在每个model种回归。
Scaling, orientation and direction of the axes is arbitrary. However, the function always centres the axes, and the default scaling is to scale the configuration ot unit root mean square and to rotate the axes (argument pc) to principal components so that the first dimension shows the major variation. It is possible to rotate the solution so that the first axis is parallel to a given environmental variable using fuction metaMDSrotate.
缩放，轴的方位和方向是任意的。但是，该功能中心轴，默认scaling是规模配置OT单位根均方旋转轴（参数pc）的主要成分，所以第一维显示主要的变化。旋转解决方案是可能的，使得第一轴是平行于给定的环境变量使用的功能XiluodumetaMDSrotate。
值----------Value----------
Returns an object of class "monoMDS". The final scores are returned in item points (function scores extracts these results), and the stress in item stress. In addition, there is a large number of other items (but these may change without
返回一个对象类"monoMDS"。返回的最后得分项points（函数scores提取这些结果），和的应力项stress。此外，还有大量的其他项目（例如，但这些可能会改变而不
注意----------Note----------
This is the default NMDS function used in metaMDS. Function metaMDS adds support functions so that NMDS can be run like recommended by Minchin (1987).
这是默认NMDS函数在metaMDS。函数metaMDS增加了支持功能，因此的NMDS可以运行像明钦（1987年）的建议。
（作者）----------Author(s)----------
Peter R. Michin (Fortran core) and Jari Oksanen (R interface).
参考文献----------References----------
dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57–68.
vector methods used in multivariate analysis. Biometrika 53, 325–328.
goodness-of-fit to a nonmetric hypothesis. Psychometrika 29, 1–28.
method. Psychometrika 29, 115–129.
techniques for ecological ordinations. Vegetatio 69, 89–107.
analysis. Journal of the Royal Statistical Society B 34, 311–349.
参见----------See Also----------
metaMDS for the vegan way of running NMDS, and isoMDS and smacofSym for some alternative implementations
metaMDSvegan NMDS的运行方式，并isoMDS和smacofSym一些替代实现
实例----------Examples----------
data(dune)
dis <- vegdist(dune)
m <- monoMDS(dis, model = "loc")
m
plot(m)
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 15:08:15
