Coef.qrrvglm-class(VGAM)
Coef.qrrvglm-class()所属R语言包：VGAM
Class “Coef.qrrvglm”
类“Coef.qrrvglm”
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The most pertinent matrices and other quantities pertaining to a QRR-VGLM (CQO model).
最相关的矩阵和其他有关的一个QRR VGLM（CQO模型）的数量。
类对象----------Objects from the Class----------
Objects can be created by calls of the form Coef(object, ...) where object is an object of class "qrrvglm" (created by cqo).
对象可以通过检测的形式Coef(object, ...)其中object是类的一个对象"qrrvglm"（创建cqo）。
In this document, R is the rank, M is the number of linear predictors and n is the number of observations.
在这份文件中，R是等级，M是线性预测的数量和n的若干意见。
插槽----------Slots----------
A: Of class "matrix", A, which are the linear "coefficients" of the matrix of latent variables.
A：对类"matrix"，A，潜变量的矩阵是线性的系数。
B1: Of class "matrix", B1.
B1：类"matrix"，B1。
C: Of class "matrix", C, the
C类"matrix"，C，
Constrained: Logical. Whether the model is
Constrained：逻辑。模型是否
D: Of class "array", D[,,j] is an order-Rank matrix, for j = 1,...,M. Ideally, these are negative-definite in order to make the response curves/surfaces bell-shaped.
D：类"array"，D[,,j]是为了Rank矩阵，为j= 1，...，M。理想的情况下，这些都是负面正定的，以使响应曲线/钟形的表面。
Rank: The rank (dimension, number of latent variables)
Rank：秩（维数，潜变量的数量）
lv: n by R matrix
lv：nR矩阵
lvOrder: Of class "matrix", the permutation returned when the function order is applied to each column of lv. This enables each column of lv to be easily sorted.
lvOrder：类"matrix"，置换时返回的功能order被施加到每一列lv。这使得每个列的lv可以很容易地排序。
Maximum: Of class "numeric", the M maximum fitted values. That is, the fitted values at the optima for Norrr = ~ 1 models.
Maximum类"numeric"，M最大的拟合值。也就是说，在最优Norrr = ~ 1模型的拟合值。
NOS: Number of species.
NOS：物种的数量。
Optimum: Of class "matrix", the values of the latent variables where the optima are. If the curves are not bell-shaped, then the value will
Optimum：类"matrix"，潜变量的值的最优解。如果不是钟形曲线，则该值将
OptimumOrder: Of class "matrix", the permutation returned when the function order is applied to each column of Optimum. This enables each row of Optimum to be easily sorted.
OptimumOrder：类"matrix"，置换时返回的功能order被施加到每一列Optimum。这使得每行的Optimum可以很容易地排序。
bellshaped: Vector of logicals: is each
bellshaped：每个向量的逻辑：
dispersion: Dispersion parameter(s).
dispersion：分散参数（S）。
Dzero: Vector of logicals, is each of the response curves linear in the latent variable(s)? It will be if and only if D[,,j] equals O, for
Dzero：媒介的逻辑值，是每一个潜变量（s）的响应曲线的线性？这将是如果，只有D[,,j]等于O，
Tolerance: Object of class "array", Tolerance[,,j] is an order-Rank matrix, for j = 1,...,M, being the matrix of tolerances (squared if on the diagonal). These are denoted by T in Yee (2004). Ideally, these are positive-definite in order to make the response curves/surfaces bell-shaped. The tolerance matrices satisfy T_s = -(0.5 D_s^(-1).
Tolerance：类的对象"array"，Tolerance[,,j]是为了Rank矩阵，为j= 1，...，M，矩阵的公差（如果对角线上的平方）。这些都记由T在仪（2004）。在理想的情况下，这些是正定的，以使响应曲线/钟形的表面。的耐受性矩阵满足T_s = -(0.5 D_s^(-1)。
（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.
参见----------See Also----------
Coef.qrrvglm, cqo, print.Coef.qrrvglm.
Coef.qrrvglm，cqo，print.Coef.qrrvglm。
实例----------Examples----------
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)
y1 = rpois(n, lambda1)
y2 = rpois(n, lambda2)
y3 = rpois(n, lambda3)
yy = cbind(y1,y2,y3)
# vvv p1 = cqo(yy ~ x2 + x3 + x4, fam=poissonff, trace=FALSE)[VVV P1 = cqo（YY~X2 + X3 + X4，FAM = 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 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。
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发表于 2012-10-1 15:30:11
