R语言 VGAM包 Coef.qrrvglm()函数中文帮助文档(中英文对照)

loveR

Coef.qrrvglm(VGAM)
Coef.qrrvglm()所属R语言包：VGAM

                                         Returns Important Matrices etc. of a QO Object
                                         返回重要矩阵等的QO对象

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This methods function returns important matrices etc. of a  QO object.
这个方法，函数返回矩阵等一个QO对象的。


用法----------Usage----------


Coef.qrrvglm(object, varlvI = FALSE, reference = NULL, ...)



参数----------Arguments----------

参数：object
A CQO or UQO object. The former has class "qrrvglm".  
一个的CQO或UQO对象。前者有类"qrrvglm"。


参数：varlvI
Logical indicating whether to scale the site scores (latent variables) to have variance-covariance matrix equal to the rank-R identity matrix. All models have uncorrelated site scores (latent variables), and this option stretches or shrinks the ordination axes if TRUE. See below for further details.  
逻辑，指出是否要扩展的网站评分（潜变量），有平等的排名R的的身份矩阵的方差 - 协方差矩阵。所有型号不相关的网站评分（潜变量），而这个选项拉伸或收缩的协调轴TRUE。请参阅下面进一步的细节。


参数：reference
Integer or character. Specifies the reference species. By default, the reference species is found by searching sequentially starting from the first species until a positive-definite tolerance matrix is found. Then this tolerance matrix is transformed to the identity matrix. Then the sites scores (latent variables) are made uncorrelated. See below for further details.     
整数或字符。指定的基准种。默认情况下，参考物种被发现通过搜索顺序从第一个物种，直到公差矩阵正定的。这种宽容矩阵转化为单位矩阵。然后，网站评分（潜变量）都是不相关的。请参阅下面进一步的细节。


参数：...
Currently unused.  
目前未使用。


Details

详细信息----------Details----------

If ITolerances=TRUE or EqualTolerances=TRUE (and its estimated tolerance matrix is positive-definite) then all species' tolerances are unity by transformation or by definition, and the spread of the site scores can be compared to them. Vice versa, if one wishes to compare the tolerances with the sites score variability then setting varlvI=TRUE is more appropriate.
如果ITolerances=TRUE或EqualTolerances=TRUE（其估计的耐受性矩阵是正定的），那么所有种类的公差单位的转型或定义，可以比较他们的网站得分的传播。反之，如果一个人希望，比较公差的网站得分的变化，然后设置varlvI=TRUE是比较合适的。

For rank-2 QRR-VGLMs, one of the species can be chosen so that the angle of its major axis and minor axis is zero, i.e., parallel to the ordination axes.  This means the effect on the latent vars is independent on that species, and that its tolerance matrix is diagonal. The argument reference allows one to choose which is the reference species, which must have a positive-definite tolerance matrix, i.e., is bell-shaped.  If reference is not specified, then the code will try to choose some reference species starting from the first species. Although the reference argument could possibly be offered as an option when fitting the model, it is currently available after fitting the model, e.g., in the functions Coef.qrrvglm and lvplot.qrrvglm.
对于等级-2 QRR VGLMs，物种之一可以选择，使得其长轴和短轴的角度是零，即，平行于排序轴。这指于潜增值的效果是独立的物种，并且其公差矩阵是对角的。参数reference允许一个选择有一定的参考物种，它必须有一个的容忍度正定矩阵，即是钟形。 reference如果没有被指定，那么代码将尽量选择有一定的参考物种从一个物种。虽然reference参数可能会被作为选件提供拟合模型时，它是目前可用的拟合模型，例如，中的功能Coef.qrrvglm和lvplot.qrrvglm。


值----------Value----------

The A, B1, C,  T,  D matrices/arrays are returned, along with other slots.  For UQO, C is undefined. The returned object has class "Coef.qrrvglm" (see Coef.qrrvglm-class).
的A，B1，C，T，D矩阵/阵列返回，以及与其他时隙。对于UQO，C是不确定的。返回的对象的类"Coef.qrrvglm"（见Coef.qrrvglm-class）。


注意----------Note----------

Consider an equal-tolerances Poisson/binomial CQO model with Norrr = ~ 1. For R=1 it has about 2*S+p2 parameters. For R=2 it has about 3*S+2*p_2 parameters. Here, S is the number of species, and p2=p-1 is the number of environmental variables making up the latent variable. For an unequal-tolerances Poisson/binomial CQO model with Norrr = ~ 1, it has about 3*S-1+p2 parameters for R=1, and about 6*S -3 +2*p2 parameters for R=2. Since the total number of data points is n*S, where n is the number of sites, it pays to divide the number of data points by the number of parameters to get some idea about how much information the parameters contain.
考虑平等的公差泊松/二项式的CQO模型与Norrr = ~ 1。对于R=12*S+p2参数。对于R=23*S+2*p_2参数。在这里，S是物种的数量，和p2=p-1是多少潜变量的环境变量。对于不平等的公差泊松/二项式的CQO模型与Norrr = ~ 1，3*S-1+p2参数R=1，和6*S -3 +2*p2参数R=2。由于数据点的总数是n*S，n的网站数量，它支付数除以数据点的参数的数量得到一些想法多少信息参数包含的内容。


（作者）----------Author(s)----------


Thomas W. Yee



参考文献----------References----------

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----------

cqo, Coef.qrrvglm-class, print.Coef.qrrvglm, lvplot.qrrvglm.
cqo，Coef.qrrvglm-class，print.Coef.qrrvglm，lvplot.qrrvglm。


实例----------Examples----------


set.seed(123)
x2 = rnorm(n <- 100)
x3 = rnorm(n)
x4 = rnorm(n)
lv1 = 0 + x3 - 2*x4
lambda1 = exp(3 - 0.5 * (lv1-0)^2)
lambda2 = exp(2 - 0.5 * (lv1-1)^2)
lambda3 = exp(2 - 0.5 * ((lv1+4)/2)^2) # Unequal tolerances[不平等的公差]
y1 = rpois(n, lambda1)
y2 = rpois(n, lambda2)
y3 = rpois(n, lambda3)
set.seed(111)
# vvv p1 = cqo(cbind(y1,y2,y3) ~ x2 + x3 + x4, poissonff, trace=FALSE)[VVV p1的= cqo（CBIND体（y1，Y2，Y3）2倍+×3 + x4的，poissonff，跟踪= FALSE）]
## Not run: [＃不运行：]
lvplot(p1, y=TRUE, lcol=1:3, pch=1:3, pcol=1:3)

## End(Not run)[＃（不执行）]
# vvv Coef(p1)[VVV系数（P1）]
# vvv print(Coef(p1), digits=3)[：VVV打印（系数系数（p1）时，位数= 3）]

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。