gam.side(mgcv)
gam.side()所属R语言包：mgcv
Identifiability side conditions for a GAM
一个自由亚齐运动的辨识方条件
译者：生物统计家园网 机器人LoveR
描述----------Description----------
GAM formulae with repeated variables only correspond to identifiable models given some side conditions. This routine works out appropriate side conditions, based on zeroing redundant parameters. It is called from mgcv:::gam.setup and is not intended to be called by users.
自由亚齐运动公式反复变量的只对应识别模型给出了一些副作用条件。这个日常工作的适当方的条件下，零冗余参数的基础上。它被称为mgcv:::gam.setup不打算由用户调用。
The method identifies nested and repeated variables by their names, but numerically evaluates which constraints need to be imposed. Constraints are always applied to smooths of more variables in preference to smooths of fewer variables. The numerical approach allows appropriate constraints to be applied to models constructed using any smooths, including user defined smooths.
该方法识别嵌套和重复的变量，他们的名字，但数值计算需要征收哪些约束。始终应用到更多的变数中优先考虑的变量较少平滑平滑约束。允许适当的约束被应用到模型构建使用任何平滑，包括用户定义的平滑数值方法。
用法----------Usage----------
gam.side(sm,Xp,tol=.Machine$double.eps^.5)
参数----------Arguments----------
参数：sm
A list of smooth objects as returned by smooth.construct.
顺利对象名单返回smooth.construct。
参数：Xp
The model matrix for the strictly parametric model components.
严格的参数模型组件模型矩阵。
参数：tol
The tolerance to use when assessing linear dependence of smooths.
平滑评估线性关系时使用的公差。
Details
详情----------Details----------
Models such as y~s(x)+s(z)+s(x,z) can be estimated by gam, but require identifiability constraints to be applied, to make them identifiable. This routine does this, effectively setting redundant parameters to zero. When the redundancy is between smooths of lower and higher numbers of variables, the constraint is always applied to the smooth of the higher number of variables.
如y~s(x)+s(z)+s(x,z)可gam估计，但需要辨识约束，使他们识别模型。这个例程这样做，有效地设置冗余参数为零。当冗余之间的平滑较低和较高的数字变量，约束变量较多的顺利应用。
Dependent smooths are identified symbolically, but which constraints are needed to ensure identifiability of these smooths is determined numerically, using fixDependence. This makes the routine rather general, and not dependent on any particular basis.
依赖平滑象征性地确定，但是这需要约束，以确保这些平滑的辨识确定的数值，用fixDependence。这使得常规，而一般情况下，不依赖任何特定的基础上。
Xp is used to check whether there is a constant term in the model (or columns that can be linearly combined to give a constant). This is because centred smooths can appear independent, when they would be dependent if there is a constant in the model, so dependence testing needs to take account of this.
Xp是用来检查是否有一个模型中的常数项（或可以线性组合给常数列）。这是因为中心平滑可能出现独立的，当他们将取决于是否有模型中的常数，所以依赖测试需要考虑到这一点。
值----------Value----------
A list of smooths, with model matrices and penalty matrices adjusted to automatically impose the required constraints. Any smooth that has been modified will have an attribute "del.index", listing the columns of its model matrix that were deleted. This index is used in the creation of prediction matrices for the term.
平滑的列表，调整，自动施加必要的约束与模型矩阵和罚款矩阵。任何已修改的平稳将有一个属性"del.index"，列出其模型矩阵的列被删除。该指数是用来创造的长期预测的矩阵。
作者（S）----------Author(s)----------
Simon N. Wood <a href="mailto:simon.wood@r-project.org">simon.wood@r-project.org</a>
举例----------Examples----------
set.seed(7)
dat <- gamSim(n=400,scale=2) ## simulate data[＃模拟数据]
## estimate model with redundant smooth interaction...[＃估计与冗余流畅的互动模型...]
b<-gam(y~s(x0)+s(x1)+s(x0,x1)+s(x2),data=dat)
plot(b,pages=1)
## Simulate data with real interation...[＃模拟数据与真实互为作用的...]
dat <- gamSim(2,n=500,scale=.1)
old.par<-par(mfrow=c(2,2))
## a fully nested tensor product example[＃完全嵌套的张量积范例]
b<-gam(y~s(x,bs="cr",k=6)+s(z,bs="cr",k=6)+te(x,z,k=6),
data=dat$data)
plot(b)
par(old.par)
rm(dat)
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 2012-2-17 10:25:14
