decorana(vegan)
decorana()所属R语言包：vegan
Detrended Correspondence Analysis and Basic Reciprocal Averaging
除趋势对应分析和基本的倒数平均
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Performs detrended correspondence analysis and basic reciprocal averaging or orthogonal correspondence analysis.
执行趋势对应分析和基本的倒数平均或正交的对应分析。
用法----------Usage----------
decorana(veg, iweigh=0, iresc=4, ira=0, mk=26, short=0,
before=NULL, after=NULL)
## S3 method for class 'decorana'
plot(x, choices=c(1,2), origin=TRUE,
display=c("both","sites","species","none"),
cex = 0.8, cols = c(1,2), type, xlim, ylim, ...)
## S3 method for class 'decorana'
text(x, display = c("sites", "species"), labels,
choices = 1:2, origin = TRUE, select, ...)
## S3 method for class 'decorana'
points(x, display = c("sites", "species"),
choices=1:2, origin = TRUE, select, ...)
## S3 method for class 'decorana'
summary(object, digits=3, origin=TRUE,
display=c("both", "species","sites","none"), ...)
## S3 method for class 'summary.decorana'
print(x, head = NA, tail = head, ...)
downweight(veg, fraction = 5)
## S3 method for class 'decorana'
scores(x, display=c("sites","species"), choices=1:4,
origin=TRUE, ...)
参数----------Arguments----------
参数：veg
Community data, a matrix-like object.
社区数据，类似矩阵的目的。
参数：iweigh
Downweighting of rare species (0: no).
Downweighting的珍稀物种（0：无）。
参数：iresc
Number of rescaling cycles (0: no rescaling).
改变大小的周期数（0：无重新标度）。
参数：ira
Type of analysis (0: detrended, 1: basic reciprocal averaging).
分析类型（0：1：非趋势，基本倒数平均）。
参数：mk
Number of segments in rescaling.
在改变大小的段数。
参数：short
Shortest gradient to be rescaled.
最短梯度重新调节。
参数：before
Hill's piecewise transformation: values before transformation.
希尔的分段改造：改造前的值。
参数：after
Hill's piecewise transformation: values after transformation – these must correspond to values in before.
希尔的分段改造：改造后的值 - 这些都必须在before对应的值。
参数：x, object
A decorana result object.
Adecorana的结果对象。
参数：choices
Axes shown.
轴所示。
参数：origin
Use true origin even in detrended correspondence analysis.
使用真正的原产地，即使在偏对应分析。
参数：display
Display only sites, only species, both or neither.
仅显示网站，唯一的物种，或者两者都不是。
参数：cex
Plot character size.
图字符的大小。
参数：cols
Colours used for sites and species.
颜色使用的网站和物种。
参数：type
Type of plots, partial match to "text", "points" or "none".
图类型，部分匹配"text"，"points"或"none"。
参数：labels
Optional text to be used instead of row names.
可选使用的文本，而不是行名称。
参数：select
Items to be displayed. This can either be a logical vector which is TRUE for displayed items or a vector of indices of displayed items.
要被显示的资料。这可以是一个逻辑向量TRUE显示的项目或显示项目的矢量的索引。
参数：xlim, ylim
the x and y limits (min,max) of the plot.
x和y的限制（最小值，最大值）的图。
参数：digits
Number of digits in summary output.
摘要输出的数字号码。
参数：head, tail
Number of rows printed from the head and tail of species and site scores. Default NA prints all.
打印的头部和尾部的物种与本站分数的行数。默认NA打印。
参数：fraction
Abundance fraction where downweighting begins.
丰度分数开始在那里downweighting，。
参数：...
Other arguments for plot function.
plot函数的其他参数。
Details
详细信息----------Details----------
In late 1970s, correspondence analysis became the method of choice for ordination in vegetation science, since it seemed better able to cope with non-linear species responses than principal components analysis. However, even correspondence analysis can produce an arc-shaped configuration of a single gradient. Mark Hill developed detrended correspondence analysis to correct two assumed "faults" in correspondence analysis: curvature of straight gradients and packing of sites at the ends of the gradient.
在20世纪70年代末，对应分析成为植物科学协调的首选方法，因为它似乎能够更好地应对非线性种反应比主成分分析。然而，即使对应分析可以制作的圆弧状的结构的一个单一的梯度。马克希尔开发除趋势对应分析纠正两个假定故障在对应分析：曲率的直线梯度和包装的端部的梯度的网站。
The curvature is removed by replacing the orthogonalization of axes with detrending. In orthogonalization successive axes are made non-correlated, but detrending should remove all systematic dependence between axes. Detrending is performed using a five-segment smoothing window with weights (1,2,3,2,1) on mk segments — which indeed is more robust than the suggested alternative of detrending by polynomials. The packing of sites at the ends of the gradient is undone by rescaling the axes after extraction. After rescaling, the axis is supposed to be scaled by "SD" units, so that the average width of Gaussian species responses is supposed to be one over whole axis. Other innovations were the piecewise linear transformation of species abundances and downweighting of rare species which were regarded to have an unduly high influence on ordination axes.
曲率被删除，取代正交轴的非趋势。连续轴正交不相关，但非趋势应该删除所有的系统轴之间的依赖关系。去趋势是使用五段平滑窗口mk段的权数（1,2,3,2,1） - 这的确是更强大的比去趋势的多项式建议的替代方案。的包装的端部的梯度的位点的是，通过重新调整轴提取后撤消。重新缩放后的，轴应该是由“SD”为单位进行缩放，使高斯物种反应的平均宽度被认为是超过整个轴之一。其他创新的分段线性变换的物种丰度和downweighting的罕见的物种被认为有过高的协调轴的影响。
It seems that detrending actually works by twisting the ordination space, so that the results look non-curved in two-dimensional projections ("lolly paper effect"). As a result, the points usually have an easily recognized triangular or diamond shaped pattern, obviously an artefact of detrending. Rescaling works differently than commonly presented, too. decorana does not use, or even evaluate, the widths of species responses. Instead, it tries to equalize the weighted variance of species scores on axis segments (parameter mk has only a small effect, since decorana finds the segment number from the current estimate of axis length). This equalizes response widths only for the idealized species packing model, where all species initially have unit width responses and equally spaced modes.
看来，去趋势实际工作通过扭曲的协调空间，这样的结果看起来弯曲的二维投影（“棒棒糖纸效果”）。其结果是，通常的点有一个很容易识别的三角形或钻石形图案，很明显的人为去趋势。标度的作品，也不同于通常会出现的。 decorana不使用，甚至评价，物种反应的宽度。相反，它试图以平衡的轴段（参数mk只有小的影响，因为decorana发现的段数轴的长度从目前的估计），加权变异的物种分数。此相等的响应宽度只有理想化的物种包装模型，其中所有物种最初单位的宽度响应和等距模式。
The summary method prints the ordination scores, possible prior weights used in downweighting, and the marginal totals after applying these weights. The plot method plots species and site scores. Classical decorana scaled the axes so that smallest site score was 0 (and smallest species score was negative), but summary, plot and scores use the true origin, unless origin = FALSE.
summary方法打印的协调分数，可能之前在downweighting权，以及应用这些权重后的边际总计。 plot方法图物种和现场评分。古典decorana缩放轴，使最小的网站得分为0（最小的品种得分为负），但summary，plot和scores使用的真正来源，除非 origin = FALSE。
In addition to proper eigenvalues, the function also reports 'decorana values' in detrended analysis. These "decorana values" are the values that the legacy code of decorana returns as "eigenvalues". They are estimated internally during iteration, and it seems that detrending interferes the estimation so that these values are generally too low and have unclear interpretation. Moreover, 'decorana values' are estimated before rescaling which will change the eigenvalues. The proper eigenvalues are estimated after extraction of the axes and they are the ratio of biased weighted variances of site and species scores even in detrended and rescaled solutions. The "decorana values" are provided only for the compatibility with legacy software, and they should not be used.
除了适当的特征值，该函数还“decorana值在去趋势分析报告。这些“decorana值”的值的遗留代码的decorana回报为“特征值”。估计他们内部在迭代过程中，似乎非趋势干涉估计，这些值一般都非常低，有不清楚的解释。此外，“decorana值”，然后再重新定标，这将改变的特征值估计。正确的特征值估计后提取的轴，他们是比偏颇的加权变异的地点和物种的分数甚至在去趋势和重新调整的解决方案。 decorana值仅设置为与传统的软件的兼容性，并且它们不应该被使用。
值----------Value----------
decorana returns an object of class "decorana", which has print, summary and plot methods.
decorana类"decorana"，其中有print，summary和plot方法返回一个对象。
注意----------Note----------
decorana uses the central numerical engine of the original Fortran code (which is in the public domain), or about 1/3 of the original program. I have tried to implement the original behaviour, although a great part of preparatory steps were written in R language, and may differ somewhat from the original code. However, well-known bugs are corrected and strict criteria used (Oksanen & Minchin 1997).
decorana使用原来的Fortran代码（这是在公共领域）的中央数字引擎，或约1/3的原程序。我试图执行原来的行为，虽然有很大一部分准备步骤都写在R语言，并可能会有所不同，从原来的代码。然而，众所周知的错误被纠正和严格的标准（奥克萨宁明钦1997年）。
Please note that there really is no need for piecewise transformation or even downweighting within decorana, since there are more powerful and extensive alternatives in R, but these options are included for compliance with the original software. If a different fraction of abundance is needed in downweighting, function downweight must be applied before decorana. Function downweight indeed can be applied prior to correspondence analysis, and so it can be used together with cca, too.
请注意，实在没有必要为分段改造或甚至downweighting内decorana，因为有更多的在R的强大和广泛的替代品，但这些选项包括符合原来的软件。如果需要丰富的downweighting不同的部分，函数downweight必须在申请前decorana。函数downweight确实可以被应用到对应分析之前，因此它可以被一起使用cca，太。
The function finds only four axes: this is not easily changed.
该功能只找到4轴：这是不会轻易改变。
（作者）----------Author(s)----------
Mark O. Hill wrote the original Fortran code, the <font face="Courier New,Courier" color="#666666"><b>R</b></font> port was by
Jari Oksanen.
参考文献----------References----------
an improved ordination technique. Vegetatio <STRONG>42</STRONG>, 47–58.
results under changes in input data order: explanations and remedies. Journal of Vegetation Science <STRONG>8</STRONG>, 447–454.
参见----------See Also----------
For unconstrained ordination, non-metric multidimensional scaling in monoMDS may be more robust (see also metaMDS). Constrained (or "canonical") correspondence analysis can be made with cca. Orthogonal correspondence analysis can be made with corresp, or with decorana or cca, but the scaling of results vary (and the one in decorana corresponds to scaling = -1 in cca.). See predict.decorana for adding new points to an ordination.
为了不受约束的协调，非度量多维尺度在monoMDS可能是更强大的（见metaMDS）。约束（或称“规范”），对应分析可以用cca。正交对应分析可以用corresp，或用decorana或cca，但结果的缩放比例的不同而不同（和一个在decoranascaling = -1 cca）。见predict.decorana添加新的点的协调。
实例----------Examples----------
data(varespec)
vare.dca <- decorana(varespec)
vare.dca
summary(vare.dca)
plot(vare.dca)
### the detrending rationale:[＃＃的去趋势的理由：]
gaussresp <- function(x,u) exp(-(x-u)^2/2)
x <- seq(0,6,length=15) ## The gradient[＃梯度]
u <- seq(-2,8,len=23) ## The optima[＃最优解]
pack <- outer(x,u,gaussresp)
matplot(x, pack, type="l", main="Species packing")
opar <- par(mfrow=c(2,2))
plot(scores(prcomp(pack)), asp=1, type="b", main="PCA")
plot(scores(decorana(pack, ira=1)), asp=1, type="b", main="CA")
plot(scores(decorana(pack)), asp=1, type="b", main="DCA")
plot(scores(cca(pack ~ x), dis="sites"), asp=1, type="b", main="CCA")
### Let's add some noise:[＃＃让我们再增加一些噪音：]
noisy <- (0.5 + runif(length(pack)))*pack
par(mfrow=c(2,1))
matplot(x, pack, type="l", main="Ideal model")
matplot(x, noisy, type="l", main="Noisy model")
par(mfrow=c(2,2))
plot(scores(prcomp(noisy)), type="b", main="PCA", asp=1)
plot(scores(decorana(noisy, ira=1)), type="b", main="CA", asp=1)
plot(scores(decorana(noisy)), type="b", main="DCA", asp=1)
plot(scores(cca(noisy ~ x), dis="sites"), asp=1, type="b", main="CCA")
par(opar)
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 15:05:07
