minPenalty(smoothSurv)
minPenalty()所属R语言包：smoothSurv
Minimize the penalty term under the two (mean and variance) constraints
（均值和方差）的约束下，最小化惩罚项
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function minimizes
此功能最大限度地减少
with respect to a[1],...,a[g] under the constraints
就a[1],...,a[g]的制约下
and
和
where
哪里
with one of a's fixed to zero.
a的与一个固定为零。
Note that the minimum is always zero. We are thus mainly interested in the point where the minimum is reached.
需要注意的是，最小为始终为零。因此，我们主要的兴趣在该点达到最小。
用法----------Usage----------
minPenalty(knots = NULL, dist.range = c(-6, 6), by.knots = 0.3, sdspline = NULL,
difforder = 3, init.c,
maxiter = 200, rel.tolerance = 1e-10, toler.chol = 1e-15, toler.eigen = 1e-3,
maxhalf = 10, debug = 0, info = TRUE)
参数----------Arguments----------
参数：knots
A vector of knots mu[1], ..., mu[g].
一个向量节mu[1], ..., mu[g]。
参数：dist.range
Approximate minimal and maximal knot. If not given by knots the knots are determined as c(seq(0, dist.range[2], by = by.knots), seq(0, dist.range[1], by = -by.knots)). The sequence of knots is sorted and multiple entries are removed.
近似最小和最大的结。如果没有给出knots的结被确定为c(seq(0, dist.range[2], by = by.knots), seq(0, dist.range[1], by = -by.knots))。的顺序进行排序和删除多个项目的结。
参数：by.knots
The distance between the two knots used when building a vector of knots if these are not given by knots.
时使用构建的向量的结，如果这些都没有给出knots两个结之间的距离。
参数：sdspline
Standard deviation sigma0^2 of the basis G-spline (here it appeares only in the variance constraint). If not given it is determined as 2/3 times the maximal distance between the two knots. If sdspline >= 1 it is changed to 0.9 to be able to satisfy the constraints.
标准偏差sigma0^2的基础上G-样条曲线（在这里会出现大的方差约束）。如果没有给出，它被确定为2/3倍的两个结之间的最大距离。如果sdspline> = 1变为0.9能够满足约束条件的。
参数：difforder
The order of the finite difference used in the penalty term.
为了惩罚项的有限差分。
参数：init.c
Optional vector of the initial values for the G-spline coefficients c, all values must lie between 0 and 1 and must sum up to 1.
可选的G-样条系数c的初始值的矢量，所有的值必须位于0和1之间，必须总结1。
参数：maxiter
Maximum number of Newton-Raphson iterations.
Newton-Raphson迭代的最大数量。
参数：rel.tolerance
(Relative) tolerance to declare the convergence. For this function, the convergence is declared if absolute value of the penalty is lower than rel.tolerance and if both constraints are satisfied up to rel.tolerance.
（相对）的容限申报收敛。对于这个函数，收敛宣布，如果绝对值的罚款低于rel.tolerance如果同时满足约束条件的到rel.tolerance。
参数：toler.chol
Tolerance to declare Cholesky decomposition singular.
申报Cholesky分解奇异的宽容。
参数：toler.eigen
Tolerance to declare an eigen value of a matrix to be zero.
公差声明一个矩阵的本征值是零。
参数：maxhalf
Maximum number of step-halving steps if updated estimate leads to a decrease of the objective function.
最大数量的逐步减少一半的步骤，如果最新的估计，导致减少的目标函数。
参数：debug
If non-zero print debugging information.
，如果非零打印调试信息。
参数：info
If TRUE information concerning the iteration process is printed during the computation to the standard output.
如果迭代过程的真实信息打印到标准输出在计算过程中。
值----------Value----------
A list with the components “spline”, “penalty”, “warning”, “fail”.
的组件列表“曲线”，“罚款”，“警告”，“失败”。
参数：spline
A data frame with columns named “Knot”, “SD basis”, “c coef.” and “a coef.” which gives the optimal values of c[1],...,c[g] and a[1],...,a[g] in the latter two columns. This data.frame can be further worked out using the function eval.Gspline.
一个数据框列命名为“云水谣”，“SD基础”，“C系数。”和“系数”给出了最优值c[1],...,c[g]和a[1],...,a[g]后者的两列。该数据框，可以进一步加工使用的功能eval.Gspline。
参数：penalty
The value of the penalty term when declaring convergence.
值的惩罚项申报时收敛。
参数：warning
Possible warnings concerning the convergence.
可能的警告有关的收敛。
参数：fail
Failure indicator. It is zero if everything went OK.
故障指示器。如果一切正常，它是零。
（作者）----------Author(s)----------
Arno拧t Kom谩rek <a href="mailto:arnost.komarek[AT]mff.cuni.cz">arnost.komarek[AT]mff.cuni.cz</a>
实例----------Examples----------
optimum <- minPenalty(knots=seq(-4.2, 4.2, by = 0.3), sdspline=0.2, difforder=3)
where <- optimum$spline
print(where)
show <- eval.Gspline(where, seq(-4.2, 4.2, by=0.05))
plot(show, type="l", bty="n", lwd=2)
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。
|
发表于 2012-9-30 10:32:03
