ccoef(VGAM)
ccoef()所属R语言包：VGAM
Extract Model Constrained/Canonical Coefficients
提取模式约束/规范化系数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
ccoef is a generic function which extracts the constrained (canonical) coefficients from objects returned by certain modelling functions.
ccoef是一个泛型函数的约束（规范）的系数中提取一定的建模功能，返回的对象。
用法----------Usage----------
ccoef(object, ...)
参数----------Arguments----------
参数：object
An object for which the extraction of canonical coefficients is meaningful.
提取的典型系数的对象是有意义的。
参数：...
Other arguments fed into the specific methods function of the model.
其他参数送入的具体方法运作的模式。
Details
详细信息----------Details----------
For constrained quadratic and ordination models, canonical coefficients are the elements of the C matrix used to form the latent variables. They are highly interpretable in ecology, and are looked at as weights or loadings.
对于约束二次与排序模式，典型的系数是用于形成潜变量的C矩阵的元素。他们有高度解释的生态，看着重量或负荷。
They are also applicable for reduced-rank VGLMs.
他们还用于降维VGLMs。
值----------Value----------
The value returned depends specifically on the methods function invoked.
返回的值取决于具体的函数被调用的方法。
警告----------Warning ----------
For QO models, there is a direct inverse relationship between the scaling of the latent variables (site scores) and the tolerances. One normalization is for the latent variables to have unit variance. Another normalization is for all the species' tolerances to be unit (provided EqualTolerances is TRUE). These two normalizations cannot simultaneously hold in general. For rank R models with R>1 it becomes more complicated because the latent variables are also uncorrelated. An important argument when fitting quadratic ordination models is whether EqualTolerances is TRUE or FALSE. See Yee (2004) for details.
QO的模型，是一个直接的缩放比例的潜变量（站点分数）和公差之间的反比关系。一个标准化是有单位方差的潜变量。另一个标准化是所有物种的公差单位（提供EqualTolerances是TRUE，）。这两个归一，不能同时保持一般。对于排名R模型R>1，它会变得更加复杂，因为潜变量不相关的。装修时二次协调模型是一个重要参数是否EqualTolerances是TRUE或FALSE。的详细信息，请参阅仪（2004）。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685–701.
Constrained additive ordination. Ecology, 87, 203–213.
参见----------See Also----------
ccoef-method, ccoef.qrrvglm, ccoef.cao, coef.
ccoef-method，ccoef.qrrvglm，ccoef.cao，coef。
实例----------Examples----------
## Not run: [＃不运行：]
set.seed(111) # This leads to the global solution[这将导致全球性的解决方案]
hspider[,1:6] = scale(hspider[,1:6]) # Standardized environmental vars[标准化的环境瓦尔]
p1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi,
Auloalbi, Pardlugu, Pardmont, Pardnigr, Pardpull,
Trocterr, Zoraspin) ~
WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,
fam = quasipoissonff, data = hspider, Crow1positive=FALSE)
ccoef(p1)
## End(Not run)[＃（不执行）]
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 15:29:04
