anova.cca(vegan)
anova.cca()所属R语言包：vegan
Permutation Test for Constrained Correspondence Analysis,
置换检验约束对应分析，
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function performs an ANOVA like permutation test for Constrained Correspondence Analysis (cca), Redundancy Analysis (rda) or Constrained Analysis of Principal Coordinates (capscale) to assess the significance of constraints.
该函数执行的方差分析，如置换检验约束对应分析（cca），冗余分析（rda）或约束的主坐标分析（capscale）来评估约束的意义。
用法----------Usage----------
## S3 method for class 'cca'
anova(object, alpha=0.05, beta=0.01, step=100, perm.max=9999,
by = NULL, ...)
permutest(x, ...)
## S3 method for class 'cca'
permutest(x, permutations = 99,
model = c("reduced", "direct", "full"),
first = FALSE, strata, ...)
参数----------Arguments----------
参数：object,x
A result object from cca.
一个结果对象从cca。
参数：alpha
Targeted Type I error rate.
有针对性的I型错误率。
参数：beta
Accepted Type II error rate.
接受第二类错误率。
参数：step
Number of permutations during one step.
一个步骤期间的排列数目。
参数：perm.max
Maximum number of permutations.
最大的排列数。
参数：by
Setting by = "axis" will assess significance for each constrained axis, and setting by = "terms" will assess significance for each term (sequentially from first to last), and setting by = "margin" will assess the marginal effects of the terms (each marginal term analysed in a model with all other variables).
设定by = "axis"将评估每个约束轴的意义，并设置by = "terms"将评估每学期（按顺序从第一个到最后一个）的意义，并设置by = "margin"将评估的条款的边际效应（在所有其他变量的模型分析每个边缘内）。
参数：...
Parameters passed to other functions. anova.cca passes all arguments to permutest.cca. In anova with by = "axis" you can use argument cutoff (defaults 1) which stops permutations after exceeding the given level.
参数传递给其他函数。 anova.cca传递的所有参数permutest.cca。在anovaby = "axis"，“你可以使用参数cutoff（默认1）停止超过给定的级别排列。
参数：permutations
Number of permutations for assessing significance of constraints.
评估约束意义的排列数。
参数：model
Permutation model (partial match).
置换模型（部分匹配）。
参数：first
Assess only the significance of the first constrained eigenvalue; will be passed from anova.cca.
评估的意义，第一个约束特征值，将通过从anova.cca。
参数：strata
An integer vector or factor specifying the strata for permutation. If supplied, observations are permuted only within the specified strata.
一个整数向量或因素确定地层的置换。如果提供，观测置换仅在指定的阶层。
Details
详细信息----------Details----------
Functions anova.cca and permutest.cca implement an ANOVA like permutation test for the joint effect of constraints in cca, rda or capscale. Functions anova.cca and permutest.cca differ in printout style and in interface. Function permutest.cca is the proper workhorse, but anova.cca passes all parameters to permutest.cca.
功能anova.cca和permutest.cca实施置换检验ANOVA像共同作用的制约因素cca，rda或capscale。功能anova.cca和permutest.cca在打印输出的风格和界面不同。功能permutest.cca是正确的主力，但anova.cca所有参数传递到permutest.cca。
In anova.cca the number of permutations is controlled by targeted “critical” P value (alpha) and accepted Type II or rejection error (beta). If the results of permutations differ from the targeted alpha at risk level given by beta, the permutations are terminated. If the current estimate of P does not differ significantly from alpha of the alternative hypothesis, the permutations are continued with step new permutations (at the first step, the number of permutations is step - 1). However, with by = "terms" a fixed number of permutations will be used, and this is given by argument permutations, or if this is missing, by step.
在anova.cca的排列数是通过有针对性的“关键”控制P值（alpha），并接受Type II或拒绝错误（beta）。如果结果的排列不同的目标alpha在水平beta风险，排列终止。如果目前估计的P并没有差异显著，从alpha的另一种假说，排列继续step新的排列组合（在第一步的排列数是step - 1）。然而，by = "terms"固定数量的排列将被使用，这是由参数permutations，如果没有这个，step。
The function permutest.cca implements a permutation test for the “significance” of constraints in cca, rda or capscale. Community data are permuted with choice model = "direct", residuals after partial CCA/RDA/CAP with choice model = "reduced" (default), and residuals after CCA/RDA/CAP under choice model = "full". If there is no partial CCA/RDA/CAP stage, model = "reduced" simply permutes the data and is equivalent to model = "direct". The test statistic is “pseudo-F”, which is the ratio of constrained and unconstrained total Inertia (Chi-squares, variances or something similar), each divided by their respective ranks. If there are no conditions (“partial” terms), the sum of all eigenvalues remains constant, so that pseudo-F and eigenvalues would give equal results. In partial CCA/RDA/CAP, the effect of conditioning variables (“covariables”) is removed before permutation, and these residuals are added to the non-permuted fitted values of partial CCA (fitted values of X ~ Z). Consequently, the total Chi-square is not fixed, and test based on pseudo-F would differ from the test based on plain eigenvalues. CCA is a weighted method, and environmental data are re-weighted at each permutation step using permuted weights.
函数permutest.cca实现了置换检验的“意义”的约束cca，rda或capscale。社区数据置换的选择model = "direct"，残留物后，部分CCA /的RDA / CAP与选择model = "reduced"（默认），和残差后CCA / RDA / CAP下选择model = "full"。如果没有偏CCA / RDA / CAP级，model = "reduced"简单地置换的数据和，相当于model = "direct"。检验统计量是“伪F”，这是比约束和无约束的总惯量（智广场，差异或类似的东西），除以它们各自的行列。如果没有条件（“部分”的条款）的所有特征值的总和保持不变，使伪F和特征值会得到相同的结果。位部分CCA / RDA / CAP，条件变量的效果（“协变量”）除去在置换之前，这些残差被添加到非排列的拟合值偏CCA（X ~ Z）的拟合值。因此，总的卡方是不固定的，和试验的基础上的伪F从测试不同的是根据在普通的特征值。 CCA是一个加权的方法和环境数据重新加权每个排列一步使用置换的权重的。
The default test is for the sum of all constrained eigenvalues. Setting first = TRUE will perform a test for the first constrained eigenvalue. Argument first can be set either in anova.cca or in permutest.cca. It is also possible to perform significance tests for each axis or for each term (constraining variable) using argument by in anova.cca. Setting by = "axis" will perform separate significance tests for each constrained axis. All previous constrained axes will be used as conditions (“partialled out”) and a test for the first constrained eigenvalues is performed (Legendre et al. 2011). You can stop permutation tests after exceeding a given significance level with argument cutoff to speed up calculations in large models. Setting by = "terms" will perform separate significance test for each term (constraining variable). The terms are assessed sequentially from first to last, and the order of the terms will influence their significances. Setting by = "margin" will perform separate significance test for each marginal term in a model with all other terms. The marginal test also accepts a scope argument for the drop.scope which can be a character vector of term labels that are analysed, or a fitted model of lower scope. The marginal effects are also known as “Type III” effects, but the current function only evaluates marginal terms. It will, for instance, ignore main effects that are included in interaction terms. In calculating pseudo-F, all terms are compared to the same residual of the full model. Permutations for all axes or terms will start from the same .Random.seed, and the seed will be advanced to the value after the longest permutation at the exit from the function.
默认的测试是所有约束的特征值的总和。设置first = TRUE将执行测试的第一个约束的特征值。参数first可以在anova.cca或permutest.cca。另外，也可以为每个轴或为每个术语进行显着性检验（约束变量）使用参数by在anova.cca。设置by = "axis"将执行不同的显着性检验每个约束轴。所有以前的约束的轴将被用作条件（“partialled满分”）和执行的第一约束的特征值的一个测试（勒让德等人，2011）。您可以停止排列的测试参数cutoff加快大型模型计算后超过给定的显着性水平。设置by = "terms"将执行不同的显着性检验，每学期（约束变量）。按顺序从第一个到最后的条款进行评估和条款的顺序会影响他们的意义。设置by = "margin"将执行不同的每个边缘内的显着性检验模型中的所有其他条款。边际测试也接受scopedrop.scope这可以是一个字符向量分析的术语的标签，或拟合模型的较低范围的参数。边际效应也被称为“III型”的影响，但目前的功能，只计算边际条款。 ，例如，忽略交互项中所包含的主要影响。在计算伪F，所有条款均比较完整的模型同样的残留。排列所有轴或条款的从相同.Random.seed，种子将提前到最长的排列在退出函数后的值。
值----------Value----------
Function permutest.cca returns an object of class "permutest.cca", which has its own print method. The function anova.cca calls permutest.cca, fills an anova table and uses print.anova for printing.
函数permutest.cca返回一个对象类"permutest.cca"，有它自己的print方法。函数anova.cca调用permutest.cca，填补了anova表，并使用print.anova的印刷。
注意----------Note----------
Some cases of anova need access to the original data on constraints (at least by = "term" and by = "margin"), and they may fail if data are unavailable.
anova某些情况下需要访问原始数据的约束（至少by = "term"和by = "margin"），而且他们可能会失败，如果数据是不可用的。
The default permutation model changed from "direct" to "reduced" in vegan version 1.14-11 (release version 1.15-0), and you must explicitly set model = "direct" for compatibility with the old version.
的默认排列model改为"direct"到"reduced"vegan版本1.14-11（发布版1.15-0），则必须明确地设置model = "direct"与旧版本的兼容性。
Tests by = "terms" and by = "margin" are consistent only when model = "direct".
测试by = "terms"和by = "margin"是一致的只有当model = "direct"。
（作者）----------Author(s)----------
Jari Oksanen
参考文献----------References----------
English ed. Elsevier.
significance of canonical axes in redundancy analysis. Methods in Ecology and Evolution 2, 269–277.
参见----------See Also----------
cca, rda, capscale to get something to analyse. Function drop1.cca calls anova.cca with by = "margin", and add1.cca an analysis for single terms additions, which can be used in automatic or semiautomatic model building (see
cca，rda，capscale得到的东西来分析。功能drop1.cca调用anova.ccaby = "margin"和add1.cca分析单条款的补充，可用于自动或半自动模式建设（见
实例----------Examples----------
data(varespec)
data(varechem)
vare.cca <- cca(varespec ~ Al + P + K, varechem)
## overall test[＃整体测试]
anova(vare.cca)
## Test for axes[＃轴测试]
anova(vare.cca, by="axis", perm.max=500)
## Sequential test for terms[＃顺序测试条款]
anova(vare.cca, by="terms", permu=200)
## Marginal or Type III effects[＃边际或III型效应]
anova(vare.cca, by="margin")
## Marginal test knows 'scope'[＃边际测试知道“范围”]
anova(vare.cca, by = "m", scope="P")
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注：
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发表于 2012-10-1 15:02:54
