goodness.cca(vegan)
goodness.cca()所属R语言包：vegan
Diagnostic Tools for [Constrained] Ordination (CCA,
[约束]排序的诊断工具（CCA，
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Functions goodness and inertcomp can be used to assess the goodness of fit for individual sites or species. Function vif.cca and alias.cca can be used to analyse linear dependencies among constraints and conditions. In addition, there are some other diagnostic tools (see 'Details').
功能goodness和inertcomp可以用来评估个别网站或物种的拟合优度。函数vif.cca和alias.cca可以用来分析之间的线性依赖关系的限制和条件。此外，还有一些其他的诊断工具（请参阅“详细信息”）。
用法----------Usage----------
## S3 method for class 'cca'
goodness(object, display = c("species", "sites"), choices,
model = c("CCA", "CA"), statistic = c("explained", "distance"),
summarize = FALSE, ...)
inertcomp(object, display = c("species", "sites"),
statistic = c("explained", "distance"), proportional = FALSE)
spenvcor(object)
intersetcor(object)
vif.cca(object)
## S3 method for class 'cca'
alias(object, names.only = FALSE, ...)
参数----------Arguments----------
参数：object
A result object from cca, rda, capscale or decorana.
一个结果对象cca，rda，capscale或decorana。
参数：display
Display "species" or "sites".
显示"species"或"sites"。
参数：choices
Axes shown. Default is to show all axes of the "model".
轴所示。默认是显示所有轴的"model"。
参数：model
Show constrained ("CCA") or unconstrained ("CA") results.
显示限制（"CCA"）或无约束（"CA"）结果。
参数：statistic
Statistic used: "explained" gives the cumulative percentage accounted for, "distance" shows the residual distances. Distances are not available for sites in constrained or partial analyses.
使用统计："explained"给出的累积百分比占，"distance"显示的剩余距离。距离不受限或部分分析的网站。
参数：summarize
Show only the accumulated total.
只显示累计总额。
参数：proportional
Give the inertia components as proportional for the corresponding total.
相应的总比例给予的惯性组件。
参数：names.only
Return only names of aliased variable(s) instead of defining equations.
返回别名的变量（S），而不是定义方程的唯一名称。
参数：...
Other parameters to the functions.
功能的其他参数。
Details
详细信息----------Details----------
Function goodness gives the diagnostic statistics for species or sites. The alternative statistics are the cumulative proportion of inertia accounted for by the axes, and the residual distance left unaccounted for. The conditional (“partialled out”) constraints are always regarded as explained and included in the statistics.
函数goodness给出了的诊断统计的物种或网站。另一种方法统计的累积比例占了转动惯量轴的剩余距离离开下落不明。始终被视为解释，并包括在统计中的条件（“partialled”）约束。
Function inertcomp decomposes the inertia into partial, constrained and unconstrained components for each site or species. Instead of inertia, the function can give the total dispersion or distances from the centroid for each component.
函数inertcomp为每个站点或物种的惯性分解成部分，约束和无约束的组件。相反的转动惯量，该函数可以得到的总的分散体或从每个组件的质心的距离。
Function spenvcor finds the so-called “species – environment correlation” or (weighted) correlation of weighted average scores and linear combination scores. This is a bad measure of goodness of ordination, because it is sensitive to extreme scores (like correlations are), and very sensitive to overfitting or using too many constraints. Better models often have poorer correlations. Function ordispider can show the same graphically.
函数spenvcor发现所谓的“种 - 环境相关”或（加权）相关性的加权平均分数和线性组合分数。这是一个坏的善良的协调措施，因为这是敏感的极端分数（如相关性），而且非常敏感，过度拟合或使用太多的限制。更好的模型通常有较差的相关性。功能ordispider可以显示相同的图形。
Function intersetcor finds the so-called “interset correlation” or (weighted) correlation of weighted averages scores and constraints. The defined contrasts are used for factor variables. This is a bad measure since it is a correlation. Further, it focuses on correlations between single contrasts and single axes instead of looking at the multivariate relationship. Fitted vectors (envfit) provide a better alternative. Biplot scores (see scores.cca) are a multivariate alternative for (weighted) correlation between linear combination scores and constraints.
函数intersetcor发现所谓的“interset相关”或（加权）的加权平均分数和约束的相关性。用于因子变量定义的对比。这是一个坏的措施，因为这是一个相关。此外，它着重于单的对比，单轴之间的相关性，而不是看多变量的关系。合身的向量（envfit）提供一个更好的选择。双标图成绩（见scores.cca）（加权）线性组合的分数之间的相关性和约束是一个多元的选择。
Function vif.cca gives the variance inflation factors for each constraint or contrast in factor constraints. In partial ordination, conditioning variables are analysed together with constraints. Variance inflation is a diagnostic tool to identify useless constraints. A common rule is that values over 10 indicate redundant constraints. If later constraints are complete linear combinations of conditions or previous constraints, they will be completely removed from the estimation, and no biplot scores or centroids are calculated for these aliased constraints. A note will be printed with default output if there are aliased constraints. Function alias will give the linear coefficients defining the aliased constraints, or only their names with argument names.only = TRUE.
函数vif.cca给出了方差膨胀因子每个约束或对比因素的制约。在部分协调一起进行分析，调节变量的约束。方差膨胀是一种诊断工具，以找出无用的限制。一个通用的规则是，超过10的值表示冗余约束。如果购买的约束条件或以前的约束是完整的线性组合，它们将完全除去从估计，而且没有的双标图分数或质心计算这些别名约束。默认的输出，如果有别名的限制，将打印的注意事项。功能alias线性相关系数定义别名的限制，他们的名字与参数names.only = TRUE。
值----------Value----------
The functions return matrices or vectors as is appropriate.
这些函数返回矩阵或向量是适当的。
注意----------Note----------
It is a common practise to use goodness statistics to remove species from ordination plots, but this may not be a good idea, as the total inertia is not a meaningful concept in cca, in particular for rare species.
它是一种常见的做法是使用goodness统计，从协调地除去种，但是这可能不是一个好主意，因为在cca的总的惯量是没有意义的概念，尤其是对稀有物种。
Function vif is defined as generic in package car (vif), but if you have not loaded that package you must specify the call as vif.cca. Variance inflation factor is useful diagnostic tool for detecting nearly collinear constraints, but these are not a problem with algorithm used in this package to fit a constrained ordination.
函数vif被定义为通用包car（vif），但如果你没装包，你必须指定调用vif.cca。方差膨胀因子是有用的诊断工具用于检测近线的制约因素，但这些都不是问题，在此包中使用的算法，以适应制约的协调。
（作者）----------Author(s)----------
Jari Oksanen. The <code>vif.cca</code> relies heavily on the code by
W. N. Venables. <code>alias.cca</code> is a simplified version of
<code><a href="../../stats/html/alias.html">alias.lm</a></code>.
参考文献----------References----------
analysis. Academic Press, London.
13–15.
参见----------See Also----------
cca, rda, capscale,
cca，rda，capscale，
实例----------Examples----------
data(dune)
data(dune.env)
mod <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env)
goodness(mod)
goodness(mod, summ = TRUE)
# Inertia components[惯性组件]
inertcomp(mod, prop = TRUE)
inertcomp(mod, stat="d")
# vif.cca [vif.cca]
vif.cca(mod)
# Aliased constraints[别名的制约因素]
mod <- cca(dune ~ ., dune.env)
mod
vif.cca(mod)
alias(mod)
with(dune.env, table(Management, Manure))
# The standard correlations (not recommended)[该标准的相关性（不推荐）]
spenvcor(mod)
intersetcor(mod)
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。
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发表于 2012-10-1 15:06:38
