concurvity(mgcv)
concurvity()所属R语言包：mgcv
GAM concurvity measures
的GAM concurvity措施
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Produces summary measures of concurvity between gam components.
产生gam组件之间的摘要措施concurvity。
用法----------Usage----------
concurvity(b,full=TRUE)
参数----------Arguments----------
参数：b
An object inheriting from class "gam".
继承类"gam"对象。
参数：full
If TRUE then concurvity of each term with the whole of the rest of the model is considered. If FALSE then pairwise concurvity measures between each smooth term (as well as the parametric component) are considered.
如果TRUE然后每个模型的其余部分与整体的长期concurvity考虑。如果FALSE然后彼此之间平稳长期（以及参数的组件）的成对concurvity措施被认为是。
Details
详情----------Details----------
Concurvity occurs when some smooth term in a model could be approximated by one or more of the other smooth terms in the model. This is often the case when a smooth of space is included in a model, along with smooths of other covariates that also vary more or less smoothly in space. Similarly it tends to be an issue in models including a smooth of time, along with smooths of other time varying covariates.
concurvity发生时，一些模型中的平稳长期可以由一个或多个模型中的其他平滑条款近似。这往往是情况时，流畅的空间是包含在模型中，连同其他变项也有所不同空间或多或少顺利平滑，。同样，它往往是一个包括一段时间的平稳，随着其他时间抚平变变量的模型中的问题。
Concurvity can be viewed as a generalization of co-linearity, and causes similar problems of interpretation. It can also make estimates somewhat unstable (so that they become sensitive to apparently innocuous modelling details, for example).
可以看作concurvity共线性的推广，并导致类似问题的解释。它也可以使估计有些不稳定（使他们变得敏感，显然是无害的造型细节，例如）。
This routine computes three related indices of concurvity, all bounded between 0 and 1, with 0 indicating no problem, and 1 indicating total lack of identifiability. The three indices are all based on the idea that a smooth term, f, in the model can be decomposed into a part, g, that lies entirely in the space of one or more other terms in the model, and a remainder part that is completely within the term's own space. If g makes up a large part of f then there is a concurvity problem. The indices used are all based on the square of ||g||/||f||, that is the ratio of the squared Euclidean norms of the vectors of f and g evaluated at the observed covariate values.
这个例程计算三个相关指数concurvity的，0和1之间的范围内的全部，0表示没有问题，1表示总缺乏辨识。三个指数均平稳来看，F模型中，可以分解成部分，G，完全在于空间模型中的一个或多个其他条款的基础上，余数部分完全在自己的空间。如果g使得f的一个大的一部分，那么有一个concurvity问题。指数所用的都是基于方| | G | | / | | F | |，这是协的观测值f和g的评价向量的平方欧几里德规范的比例。
The three measures are as follows
三项措施如下
worst This is the largest value that the square of ||g||/||f|| could take for any coefficient vector. This is a fairly pessimistic measure, as it looks at the worst case irrespective of data. This is the only measure that is symmetric.
最坏这是最大的值的平方| | G | | / | | F | |可以采取任何系数向量。这是一个相当悲观的措施，因为它看起来在最坏的情况下，不论数据。这是唯一的衡量标准是对称的。
observed This just returns the value of the square of ||g||/||f|| according to the estimated coefficients.
观察这只是返回值的平方| | G | | / | | F | |据估计系数。
estimate This is the squared F-norm of the basis for g divided by the F-norm of the basis for f. It is a measure of the extent to which the f basis can be explained by the g basis. It does not suffer from the pessimism or potential for
估计这是F-范数为f的基础上划分为G的基础上的F-范数的平方。这是一个衡量程度的f的基础，可以由G的基础上解释。它从悲观的或潜在的不吃亏
值----------Value----------
If full=TRUE a matrix with one column for each term and one row for each of the 3 concurvity measures detailed below. If full=FALSE a list of 3 matrices, one for each of the three concurvity measures detailed below. Each row of the matrix relates to how the model terms depend on the model term supplying that rows name.
如果以下措施的详细full=TRUE3 concurvity的每学期一列一列的矩阵。如果full=FALSE3矩阵，每个三个concurvity一个名单如下措施的详细。矩阵的每一行与模型模型提供的行名长期依赖。
作者（S）----------Author(s)----------
Simon N. Wood <a href="mailto:simon.wood@r-project.org">simon.wood@r-project.org</a>
参考文献----------References----------
举例----------Examples----------
library(mgcv)
## simulate data with concurvity...[＃模拟数据与concurvity ...]
set.seed(8);n<- 200
f2 <- function(x) 0.2 * x^11 * (10 * (1 - x))^6 + 10 *
(10 * x)^3 * (1 - x)^10
t <- sort(runif(n)) ## first covariate[＃第一个协变量]
## make covariate x a smooth function of t + noise...[＃协变量XA T +噪声平滑函数...]
x <- f2(t) + rnorm(n)*3
## simulate response dependent on t and x...[＃模拟响应依赖于t和x ...]
y <- sin(4*pi*t) + exp(x/20) + rnorm(n)*.3
## fit model...[＃适合模型...]
b <- gam(y ~ s(t,k=15) + s(x,k=15),method="REML")
## assess concurvity between each term and `rest of model'...[＃评估concurvity之间每届任期和模型截断“......]
concurvity(b)
## ... and now look at pairwise concurvity between terms...[＃...现在看条款之间的成对concurvity ...]
concurvity(b,full=FALSE)
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注：
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发表于 2012-2-16 19:11:00
