cca.object(vegan)
cca.object()所属R语言包：vegan
Result Object from Constrained Ordination with cca, rda or capscale
结果对象从约束的协调与CCA，RDA或capscale
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Ordination methods cca, rda and capscale return similar result objects. Function capscale inherits from rda and rda inherits from cca. This inheritance structure is due to historic reasons: cca was the first of these implemented in vegan. Hence the nomenclature in cca.object reflects cca. This help page describes the internal structure of the cca object for programmers.
排序方法cca，rda和capscale返回同样的结果对象。函数capscaleinherits的rda和rda继承自cca。这种继承结构是由于历史的原因：cca是第一个素食主义者实施的这些。因此，命名cca.object反映cca。此帮助页介绍的cca对象的程序员的内部结构。
值----------Value----------
A cca object has the following elements:
Acca对象有下列元素：
参数：call
the function call.
函数调用。
参数：colsum, rowsum, rowsum.excluded
Column and row sums in cca. In rda, item colsum contains standard deviations of species and rowsum is NA. If some data were removed in na.action, the row sums of excluded observations are in item rowsum.excluded in cca (but not in rda). The rowsum.excluded add to the total (one) of rowsum.
列和行款项cca。在rda，项目colsum包含标准偏差的种类和rowsum是NA。如果一些数据被删除的na.action，行和排除的观测是在项目的rowsum.excludedcca（但不是在rda）。 rowsum.excluded加总（一）rowsum。
参数：grand.total
Grand total of community data in cca and NA in rda.
大总的cca和NA中rda社区中的数据。
参数：inertia
Text used as the name of inertia.
用作文本惯性的名称。
参数：method
Text used as the name of the ordination method.
的协调方法的名称作为文本。
参数：terms
The terms component of the formula. This is missing if the ordination was not called with formula.
在terms组成部分的formula。这是缺少的如果协调不叫formula的。
参数：terminfo
Further information on terms with three subitems: terms which is like the terms component above, but lists conditions and constraints similarly; xlev which lists the factor levels, and ordered which is TRUE to ordered factors. This is produced by vegan internal function ordiTerminfo, and it is needed in predict.cca with newdata. This is missing if the ordination was not called with formula.
更多信息，与三个分项目：terms像terms的组件上面，但是列表的条件和约束同样xlev，其中列出了因子水平，并ordered是TRUE下令因素。这是由vegan：内部功能ordiTerminfo，和它需要在predict.ccanewdata。这是缺少的如果协调不叫formula的。
参数：tot.chi
Total inertia or the sum of all eigenvalues.
总惯性的所有特征值的总和。
参数：na.action
The result of na.action if missing values in constraints were handled by na.omit or na.exclude (or NULL if there were no missing values). This is a vector of indices of missing value rows in the original data and a class of the action, usually either "omit" or "exclude".
的结果na.action，如果在约束处理缺失值的na.omit或na.exclude（或NULL，如果没有缺失值）。这是一个向量指数的原始数据中的缺失值的行和类的操作，通常是"omit"或"exclude"。
参数：pCCA, CCA, CA
Actual ordination results for conditioned (partial), constrained and unconstrained components of the model. If constraints or conditions are not given, the corresponding components CCA and pCCA are NULL. If they are specified but have zero rank and zero eigenvalue (e.g., due to aliasing), they have a standard structure like described below, but the result scores have zero columns, but the correct number of rows. The residual component is never NULL, and if there is no residual variation (like in overdefined model), its scores have zero columns. The standard print command does not show NULL components, but it prints zeros for zeroed components. Items pCCA, CCA and CA contain following items:
对空调的实际协调的结果（部分），约束和无约束的部件模型。如果约束条件未给出相应的组件CCA和pCCA是NULL。如果他们规定，但零级和零特征值（例如，由于混叠），他们有一个标准的结构，就像下面描述，但结果分数为零的列，但正确的行数。残差分量从未NULL，如果没有残留的变化（像在overdefined模型），其分数具有零列。标准的print命令不显示NULL成分，但它打印零置零组件的。项目pCCA，CCA和CA包含以下项目：
aliasThe names of the aliased constraints or conditions. Function alias.cca does not access this item directly, but it finds the aliased variables and their defining equations from the QR item.
alias的名称的别名限制或条件。功能alias.cca不访问这个项目直接，但它发现的别名变量和公式QR项目。
biplotBiplot scores of constraints. Only in CCA.
biplot的双标图分数的限制。只有在CCA。
centroids(Weighted) centroids of factor levels of constraints. Only in CCA. Missing if the ordination was not called with formula.
centroids（加权）的质心因子水平的限制。只有在CCA。丢失，如果不叫的协调与formula。
eigEigenvalues of axes. In CCA and CA.
eig轴的特征值。在CCA和CA。
envcentre(Weighted) means of the original constraining or conditioning variables. In pCCA and in CCA.
envcentre（加权）原来的约束或条件变量的装置。在pCCA和在CCA。
FitThe fitted values of standardized data matrix after fitting conditions. Only in pCCA.
Fit标准化的数据矩阵拟合后的拟合值。只有在pCCA。
QRThe QR decomposition of explanatory variables as produced by qr. The constrained ordination algorithm is based on QR decomposition of constraints and conditions (environmental data). The environmental data are first centred in rda or weighted and centred in cca. The QR decomposition is used in many functions that access cca results, and it can be used to find many items that are not directly stored in the object. For examples, see coef.cca, coef.rda, vif.cca, permutest.cca, predict.cca, predict.rda, calibrate.cca. For possible uses of this component, see qr. In pCCA and CCA.
QRQR分解产生的qr的解释变量。约束的协调算法是基于QR分解的约束条件（环境）。环境数据集中在rda或加权和集中在cca。访问cca结果，它可以被用来找到许多项目，不直接存储在对象的许多功能中使用的QR分解。有关示例，请参阅coef.cca，coef.rda，vif.cca，permutest.cca，predict.cca，predict.rda，calibrate.cca。对于此组件的可能的用途，请参阅qr。在pCCA和CCA。
rankThe rank of the ordination component.
rank的排名统筹部分。
qrankThe rank of the constraints which is the difference of the ranks of QR decompositions in pCCA and CCA components. Only in CCA.
qrank排名的区别是QR分解的行列中pCCA和CCA组件的约束。只有在CCA。
tot.chiTotal inertia or the sum of all eigenvalues of the component.
tot.chi的所有特征值的组件的总惯量的总和。
imaginary.chi, imaginary.rankThe sum and rank (number) of negative eigenvalues in capscale. Only in CA and only if negative eigenvalues were found in capscale.
imaginary.chi，imaginary.rank的总和排名（数字）的负本征值capscale。只有在CA且仅当负本征值被发现在capscale。
u(Weighted) orthonormal site scores. Please note that scaled scores are not stored in the cca object, but they are made when the object is accessed with functions like scores.cca, summary.cca or plot.cca, or their rda variants. Only in CCA and CA. In the CCA component these are the so-called linear combination scores.
u（加权）的标准正交网站评分。请注意量尺化存储cca对象，但都是当对象被访问的功能，如scores.cca，summary.cca或plot.cca，或他们的 rda的变种。只有在CCA和CA。在CCA组件，这是所谓的线性组合分数。
u.eigu scaled by eigenvalues. There is no guarantee that any .eig variants of scores will be kept in the future releases.
的u.eig“u缩放特征值。谁也不能保证在未来的版本将被保存在任何.eig不同的分数。
v(Weighted) orthonormal species scores. If missing species were omitted from the analysis, this will contain attribute na.action that lists the omitted species. Only in CCA and CA.
v（加权）的标准正交物种分数。如果缺少物种从分析中忽略，这将包含属性na.action“”，其中列出了被忽略的物种。只有在CCA和CA。
v.eigv weighted by eigenvalues.
v.eig“v加权特征值。
waSite scores found as weighted averages (cca) or weighted sums (rda) of v with weights Xbar, but the multiplying effect of eigenvalues removed. These often are known as WA scores in cca. Only in CCA.
wa＃以加权平均数（cca）或加权和（rda）的v配重块Xbar，但删除的乘数效应特征值。这些通常被称为作为WA得分cca。只有在CCA。
wa.eigThe direct result of weighted averaging or weighted summation (matrix multiplication) with the resulting eigenvalue inflation.
wa.eig加权平均或加权求和（矩阵乘法），得到的特征值通货膨胀的直接结果。
wa.excluded, u.excludedWA scores for rows removed by na.action = na.exclude in CCA and CA components if these could be calculated.
wa.excluded, u.excluded WA分数的行除去na.action = na.exclude中CCA和CA成分，如果这些可以计算。
XbarThe standardized data matrix after previous stages of analysis. In CCA this is after possible pCCA or after partialling out the effects of conditions, and in CA after both pCCA and CCA. In cca the standardization is Chi-square, and in rda centring and optional scaling by species standard deviations using function scale.
Xbar标准化的数据矩阵分析后，以前的阶段。在CCA“”这是后可能pCCA或后条件的影响partialling，并在CA后都pCCA和CCA。在cca的标准化是卡方，并在rda定心和可选的扩展使用功能scale种标准差。
NA行动和子集----------NA Action and Subset----------
If the constraints had missing values or subsets, and na.action was set to na.exclude or na.omit, the result will have some extra items:
如果约束遗漏值或子集，na.actionna.exclude或na.omit，其结果将有一些额外的项目：
subset subset evaluated as a logical vector
subset子集评为逻辑向量
na.action The object returned by na.action which is a named vector of indices of removed items. The class of the vector is either "omit" or "exclude" as set by na.action. The na.action is applied after subset so that the indices refer to the subset
na.actionna.action返回的对象是一个名为矢量指数移除的项目。类的向量是，是"omit"或"exclude"的na.action。 na.action后subset中，该指数的子集
residuals.zombie A zombie vector of the length of number of rows in the residual ordination. R versions before 2.13.0 may use this vector to find the number of valid observations, and it is provided for their use although this is useless in R 2.13.0 and in vegan. Currently R uses nobs.cca to find
residuals.zombie僵尸矢量的长度中残留的协调行数。前R版本2.13.0可以使用此向量找到有效观测数，和它被设置为它们的使用，虽然这是无用的在R 2.13.0和在vegan。目前的R使用nobs.cca
rowsum.excluded Row sums of removed observations. Only
rowsum.excluded行删除的观测值的总和。仅
CCA$wa.excluded The WA scores for sites (found from community data) in constrained ordination if na.action was na.exclude and the scores could be calculated. The scores cannot be found for
CCA$wa.excludedWA分数的网站（从社区数据）在受约束的协调，如果na.actionna.exclude的分数可以计算出来的。分数不能找到
CA$u.excluded Row scores for sites in unconstrained
CA$u.excluded行分数在无约束的网站
capscale----------capscale----------
Function capscale may add some items depending on its arguments:
函数capscale可能会增加一些项目，根据它的参数：
metaMDSdist The data set name if
metaMDSdist的数据集的名称，如果
ac Additive constant used if add = TRUE.
ac加常数如果add = TRUE。
adjust Adjustment of dissimilarities: see
adjust调整的不同点：
（作者）----------Author(s)----------
Jari Oksanen
参考文献----------References----------
ed. Elsevier.
参见----------See Also----------
The description here provides a hacker's interface. For more user friendly access to the cca object see alias.cca, coef.cca, deviance.cca, predict.cca, scores.cca, summary.cca, vif.cca, weights.cca, spenvcor or rda variants of these functions. You can use as.mlm to cast a cca.object into result of multiple response linear model (lm) in order to more easily find some statistics (which in principle could be directly found from the cca.object as well).
这里的说明提供一个黑客的接口。更多的用户友好访问cca对象alias.cca，coef.cca，deviance.cca，predict.cca，scores.cca，summary.cca， vif.cca，weights.cca，spenvcor或rda这些功能的变种。你可以用as.mlm投一个cca.object（lm）为了更容易地找到一些统计数据（原则上可以直接从<多响应线性模型，并将结果X>）。
实例----------Examples----------
# Some species will be missing in the analysis, because only a subset[有些物种将丢失的分析，因为只有一小部分]
# of sites is used below.[下面的网站。]
data(dune)
data(dune.env)
mod <- cca(dune[1:15,] ~ ., dune.env[1:15,])
# Look at the names of missing species[看名失踪物种]
attr(mod$CCA$v, "na.action")
# Look at the names of the aliased variables:[看的别名变量的名称：]
mod$CCA$alias
# Access directly constrained weighted orthonormal species and site[直接访问约束加权正交的种类和网站]
# scores, constrained eigenvalues and margin sums.[分数，约束特征值和保证金数额。]
spec <- mod$CCA$v
sites <- mod$CCA$u
eig <- mod$CCA$eig
rsum <- mod$rowsum
csum <- mod$colsum
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 15:04:29
