rrvglm.control(VGAM)
rrvglm.control()所属R语言包：VGAM
Control function for rrvglm
控制为rrvglm的功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Algorithmic constants and parameters for running rrvglm are set using this function.
算法的常量和运行参数rrvglm使用此功能。
用法----------Usage----------
rrvglm.control(Rank = 1, Algorithm = c("alternating", "derivative"),
Corner = TRUE, Uncorrelated.lv = FALSE, Wmat = NULL, Svd.arg = FALSE,
Index.corner = if (length(szero))
head((1:1000)[-szero], Rank) else 1:Rank,
Ainit = NULL, Alpha = 0.5, Bestof = 1, Cinit = NULL,
Etamat.colmax = 10,
SD.Ainit = 0.02, SD.Cinit = 0.02, szero = NULL,
Norrr = ~1, trace = FALSE, Use.Init.Poisson.QO = FALSE,
checkwz = TRUE, wzepsilon = .Machine$double.eps^0.75, ...)
参数----------Arguments----------
参数：Rank
The numerical rank R of the model. Must be an element from the set {1,2,...,min(M,p2)}. Here, the vector of explanatory variables x is partitioned into (x1,x2), which is of dimension p1+p2. The variables making up x1 are given by the terms in Norrr argument, and the rest of the terms comprise x2.
数值秩R的模式。必须是元素的集合{1,2，...，分（M，P2）}。在这里，说明变量x的向量被划分为（X1，X2），它的尺寸是p1的+ p2的。弥补×1中给出的变量由Norrr的参数中的条款和条款的其余部分包括×2。
参数：Algorithm
Character string indicating what algorithm is to be used. The default is the first one.
字符串表示算法被使用。在默认情况下是第一位的。
参数：Corner
Logical indicating whether corner constraints are to be used. This is one method for ensuring a unique solution. If TRUE, Index.corner specifies the R rows of the constraint matrices that are use as the corner constraints, i.e., they hold an order-R identity matrix.
逻辑指示是否要使用角约束。这是一种方法，用于确保一个独特的解决方案。如果TRUE，Index.corner指定R的行的约束矩阵，使用的转角限制，即，他们持有的订单R的的身份矩阵。
参数：Uncorrelated.lv
Logical indicating whether uncorrelated latent variables are to be used. This is normalization forces the variance-covariance matrix of the latent variables to be diag(Rank), i.e., unit variance and uncorrelated. This constraint does not lead to a unique solution because it can be rotated.
逻辑指示是否要使用的不相关的潜变量是。这是标准化力量的潜在变量的方差 - 协方差矩阵是diag(Rank)，即，单位方差的和不相关的。此限制不导致一个独特的解决方案，因为它可以旋转。
参数：Wmat
Yet to be done.
然而，许多工作要做。
参数：Svd.arg
Logical indicating whether a singular value decomposition of the outer product is to computed. This is another normalization which ensures uniqueness. See the argument Alpha below.
逻辑指示的奇异值分解的外产物是否是计算。这又是一个标准化确保唯一性。请参阅参数Alpha下面。
参数：Index.corner
Specifies the R rows of the constraint matrices that are used for the corner constraints, i.e., they hold an order-R identity matrix.
指定R行的约束矩阵，用于角落的限制，即，他们持有的订单R单位矩阵。
参数：Alpha
The exponent in the singular value decomposition that is used in the first part: if the SVD is U %*% D %*% t(V) then the first and second parts are U %*% D^Alpha and D^(1-Alpha) %*% t(V) respectively. A value of 0.5 is "symmetrical". This argument is used only when Svd.arg=TRUE.
是用于在第一部分中的奇异值分解中的指数：如果该SVD U %*% D %*% t(V) 然后第一和第二部分是 U %*% D^Alpha和D^(1-Alpha) %*% t(V)分别。 0.5的值是“对称的”。只有当该参数用于Svd.arg=TRUE。
参数：Bestof
Integer. The best of Bestof models fitted is returned. This argument helps guard against local solutions by (hopefully) finding the global solution from many fits. The argument works only when the function generates its own initial value for C, i.e., when C is not passed in as initial values.
整数。 Bestof车型配备最好的返回。此参数有助于防止对本地解决方案（希望）发现许多适合全球性的解决方案。只有当函数生成自己的初始值C，即C不作为初始值传递的参数。
参数：Ainit, Cinit
Initial A and C matrices which may speed up convergence. They must be of the correct dimension.
最初的A和C矩阵，这可能会加速收敛。它们必须是正确的维度。
参数：Etamat.colmax
Positive integer, no smaller than Rank. Controls the amount of memory used by .Init.Poisson.QO(). It is the maximum number of columns allowed for the pseudo-response and its weights. In general, the larger the value, the better the initial value. Used only if Use.Init.Poisson.QO=TRUE.
正整数，不小于Rank。控制使用的内存量的.Init.Poisson.QO()。是允许为伪响应及其权重的列的最大数目。在一般情况下，该值越大，更好的初始值。用只有Use.Init.Poisson.QO=TRUE。
参数：szero
Integer vector specifying which rows of the constraint matrices are to be all zeros. These are called structural zeros.
整数向量确定的约束矩阵的行为全零。这些被称为结构零。
参数：SD.Ainit, SD.Cinit
Standard deviation of the initial values for the elements of A and C. These are normally distributed with mean zero. This argument is used only if Use.Init.Poisson.QO = FALSE.
A和C这些元素的初始值的标准偏差的正态分布的均值为零。这种说法是只有Use.Init.Poisson.QO = FALSE。
参数：Norrr
Formula giving terms that are not to be included in the reduced-rank regression. That is, Norrr specifes which explanatory variables are in the x1 vector of rrvglm, and the rest go into x2. The x1 variables constitute the \bold{B}1 matrix in Yee and Hastie (2003). Those x2 variables which are subject to the reduced-rank regression correspond to the \bold{B}2 matrix. Set Norrr = NULL for the reduced-rank regression to be applied to every explanatory variable including the intercept.
公式给不包括在降秩回归的条款。也就是说，Norrrspecifes解释性变量是在x1的矢量rrvglm的，其余的进入x2。 x1变量构成的\bold{B}1矩阵中怡康和哈斯蒂（2003）。那些x2的降秩回归的变量对应\bold{B}2矩阵。设置Norrr = NULL降秩回归被应用到每一个解释变量包括拦截。
参数：trace
Logical indicating if output should be produced for each iteration.
逻辑表明，如果输出应为每个迭代。
参数：Use.Init.Poisson.QO
Logical indicating whether the .Init.Poisson.QO() should be used to obtain initial values for the C. The function uses a new method that can work well if the data are Poisson counts coming from an equal-tolerances QRR-VGLM (CQO). This option is less realistic for RR-VGLMs compared to QRR-VGLMs.
逻辑.Init.Poisson.QO()是否应该被用来获得初始值的C.该函数使用一种新方法，可以很好地工作，如果数据是泊松计数等于公差QRR VGLM（CQO）。此选项是不太现实为RR-VGLMs相比，QRR-VGLMs的。
参数：checkwz
logical indicating whether the diagonal elements of the working weight matrices should be checked whether they are sufficiently positive, i.e., greater than wzepsilon. If not, any values less than wzepsilon are replaced with this value.
逻辑指示的工作的权重矩阵的对角线元素是否应检查是否有足够的正面，即，大于wzepsilon。如果没有，任何值小于wzepsilon这个值被替换。
参数：wzepsilon
Small positive number used to test whether the diagonals of the working weight matrices are sufficiently positive.
小的正数，用来测试工作的权重矩阵的对角线是否有足够的积极。
参数：...
Variables in ... are passed into vglm.control. If the derivative algorithm is used, then ... are also passed into rrvglm.optim.control.
中的变量...是传递到vglm.control。如果导数的算法，然后...也被传递到rrvglm.optim.control。
Details
详细信息----------Details----------
VGAM supports three normalizations to ensure a unique solution. Of these, only corner constraints will work with summary of RR-VGLM objects.
VGAM支持三年的标准化，以确保一个独特的解决方案。其中，只有角落的限制将工作与summary的的RR-VGLM对象。
值----------Value----------
A list with components matching the input names. Some error checking is done, but not much.
组件的列表匹配输入的名字。某些错误进行检查，但数量不多。
注意----------Note----------
The arguments in this function begin with an upper case letter to help avoid interference with those of vglm.control.
在这个函数中的参数开始一个大写字母，以帮助避免干扰的vglm.control。
In the example below a rank-1 stereotype model (Anderson, 1984) is fitted.
在下面的例子中一个等级-1的刻板模式（安德森，1984）安装。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Reduced-rank vector generalized linear models. Statistical Modelling, 3, 15–41.
参见----------See Also----------
rrvglm, rrvglm.optim.control, rrvglm-class, vglm, vglm.control, cqo.
rrvglm，rrvglm.optim.control，rrvglm-class，vglm，vglm.control，cqo。
实例----------Examples----------
set.seed(111)
pneumo <- transform(pneumo, let = log(exposure.time),
x3 = runif(nrow(pneumo))) # x3 is random noise[X3是随机噪声]
fit <- rrvglm(cbind(normal, mild, severe) ~ let + x3,
multinomial, pneumo, Rank = 1, Index.corner = 2)
constraints(fit)
vcov(fit)
summary(fit)
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 15:52:14
