seg.control(segmented)
seg.control()所属R语言包：segmented
Auxiliary for controlling segmented model fitting
辅助控制分段模型拟合
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Auxiliary function as user interface for 'segmented' fitting. Typically only used when calling any 'segmented' method (segmented.lm or segmented.glm).
分割配件的用户界面的辅助功能。通常仅当调用任何“分割”的方法（segmented.lm或segmented.glm）。
用法----------Usage----------
seg.control(toll = 1e-04, it.max = 10, display = FALSE,
stop.if.error = TRUE, K = 10, quant = FALSE, last = TRUE, maxit.glm = 25, h = 1,
n.boot=20, size.boot=NULL, gap=FALSE, jt=FALSE, nonParam=TRUE,
random=TRUE, powers=c(1,1), seed=NULL)
参数----------Arguments----------
参数：toll
positive convergence tolerance.
积极收敛公差。
参数：it.max
integer giving the maximal number of iterations.
整数，给出的最大数目的迭代。
参数：display
logical indicating if the value of the working objective function should be printed at each iteration. The working objective function is the objective function of the working model including the gap coefficients.
逻辑指示是否应打印在每次迭代时的工作的目标函数的值。的工作目标函数为目标函数的工作模式，包括的差距系数。
参数：stop.if.error
logical indicating if non-admissible break-points should be removed during the estimating algorithm. Set it to FALSE if you want to perform a sort of "automatic" breakpoint selection, provided that several starting values are provided for the breakpoints. See argument psi in segmented.lm or segmented.glm. The idea of removing "non-admissible" break-points during the iterative process is discussed in Muggeo and Adelfio (2011) and it is not compatible with the bootstrap restart algorithm.
逻辑估计算法在非允许的突破点应该被删除。将其设置为FALSE如果你要执行一种“自动”断点选择，提供数初始值的断点。见参数psisegmented.lm或segmented.glm。除去非受理突破点的迭代过程期间的想法讨论在Muggeo和Adelfio（2011），并重新启动的bootstrap算法，这是不兼容。
参数：K
the number of quantiles (or equally-spaced values) to supply as starting values for the breakpoints when the psi argument of segmented is set to NA. K is ignored when psi is different from NA.
数的位数（或相等间隔的值）提供初始值的断点时psisegmented参数设置为NA。 K被忽略，当psi是不同NA。
参数：quant
logical, indicating how the starting values should be selected. If FALSE equally-spaced values are used, otherwise the quantiles. Ignored when psi is different from NA.
逻辑，指示应选择初始值如何。如果FALSE同样间隔的值，否则位数。忽略，当psi是不同NA。
参数：last
logical indicating if output should include only the last fitted model.
逻辑表明，如果输出应该只包括最后的拟合模型。
参数：maxit.glm
integer giving the maximum number of inner IWLS iterations (see details).
整数的最大数量的内IWLS的迭代（见详情）。
参数：h
positive factor (from zero to one) modifying the increments in breakpoint updates during the estimation process (see details).
在评估过程中的积极因素（从零到一）修改的增量中断点，更新（见详情）。
参数：n.boot
number of bootstrap samples used in the bootstrap restarting algorithm. If 0 the standard algorithm, i.e. without bootstrap restart, is used. Default to 10 that appears to be sufficient in most of problems. However when multiple breakpoints have to be estimated it is suggested to increase n.boot, e.g. n.boot=50.
在引导重新启动算法使用的bootstrap样本数量。如果为0，即不举重新启动，使用标准算法。默认为10，这似乎是足以在大多数问题。然而，当有多个断点进行估计，建议增加n.boot，例如n.boot=50。
参数：size.boot
the size of the bootstrap samples. If NULL, it is taken equal to the actual sample size.
bootstrap样本的大小。如果NULL，它被视为等于实际样本大小。
参数：gap
logical, if FALSE the gap coefficients are always constrained to zero at the convergence.
逻辑，如果FALSE的间隙系数始终限制在收敛到零。
参数：jt
logical. If TRUE the values of the segmented variable(s) are jittered before fitting the model to the bootstrap resamples.
逻辑。如果TRUE分段变量的值（S）抖动，拟合模型前的引导重新采样。
参数：nonParam
if TRUE nonparametric bootstrap (i.e. case-resampling) is used, otherwise residual-based. Currently working only for LM fits. It is not clear what residuals should be used for GLMs.
如果TRUE非参数自举（即情况下，重采样），否则剩余。 LM适合目前的工作。目前尚不清楚什么的残差应该用于GLMS。
参数：random
if TRUE, when the algorithm fails to obtain a solution, random values are employed to obtain candidate values.
如果TRUE，该算法失败时，得到一种溶液，随机值来获得候选值。
参数：powers
The powers of the pseudo covariates employed by the algorithm. These are possibly altered during the iterative process to stabilize the estimation procedure. Usually of no interest for the user.
权力的伪协变量的算法。这些可能是在迭代过程中改变，到稳定估计过程。一般为用户没有兴趣。
参数：seed
The seed to be passed on set.seed() when n.boot>0. Setting the seed can be useful to replicate the results when the bootstrap restart algorithm is employed.
种子传递set.seed()n.boot>0。设置的种子可能是有用的，当重新启动自举算法采用复制的结果。
Details
详细信息----------Details----------
Fitting a "segmented" GLM model is attained via fitting iteratively standard GLMs. The number of (outer) iterations is governed by it.max, while the (maximum) number of (inner) iterations to fit the GLM at each fixed value of psi is fixed via maxit.glm. Usually three-four inner iterations may be sufficient.
“分段”GLM模型拟合获得通过迭代装修标准GLMS。 （外）的迭代的数量是受it.max，同时的（最大）数目（内层）的迭代被固定在每一个固定的值，以适应的GLM个psi通过maxit.glm。一般三四个内迭代就足够了。
When the starting value for the breakpoints is set to NA for any segmented variable specified in seg.Z, K values (quantiles or equally-spaced) are selected as starting values for the breakpoints. In this case, it may be useful to set also stop.if.error=FALSE to automate the procedure, see Muggeo and Adelfio (2011). The maximum number of iterations (it.max) should be also increased when the "automatic" procedure is used.
当值的断点设置为NA分段变量中指定的seg.Z，K值（位数的等距）被选为初始值的断点。在这种情况下，它可能是有用的设置也stop.if.error=FALSE的自动化过程，请参见Muggeo和Adelfio（2011）。 automatic的程序是用迭代（it.max）的最大数目时，也需要增加。
If last=TRUE, the object resulting from segmented.lm (or segmented.glm) is a list of fitted GLM; the i-th model is the segmented model with the values of the breakpoints at the i-th iteration.
如果last=TRUE，segmented.lm（segmented.glm）是列表中第i个模型拟合GLM是在我的断点值的分段模型与对象次迭代。
Sometimes to stabilize the procedure, it can be useful to set h<1 to reduce the increments in the breakpoint updates. At each iteration the updated estimate is usually given by psi.new=psi.old+increm. By setting h<1 (actually min(abs(h),1) is considered) causes the following updates of the breakpoint estimate: psi.new=psi.old+h*increm.
有时，为了稳定程序，它可以是有用的设置h<1，以减少在断点的增量更新。在每次迭代更新后的预算通常由psi.new=psi.old+increm。通过设置h<1（实际上min(abs(h),1)被认为是）使更新的断点估计如下：psi.new=psi.old+h*increm。
Since version 0.2-9.0 segmented implements the bootstrap restarting algorithm described in Wood (2011). The bootstrap restarting is expected to escape the local optima of the objective function when the segmented relationship is flat. Notice bootstrap restart runs n.boot iterations regardless of toll that only affects convergence within the inner loop.
由于版本0.2-9.0 segmented实现了自举重新启动木（2011年）中描述的算法。自举重新启动预期逃脱局部最优的目标函数时的分段的关系是平坦的。请注意引导重新启动运行n.boot迭代，无论toll只影响收敛内的内循环。
值----------Value----------
A list with the arguments as components.
作为组件的参数列表。
（作者）----------Author(s)----------
Vito Muggeo
参考文献----------References----------
continuous measurements. Bioinformatics 27, 161–166.
by bootstrap restarting. Biometrics 57, 240–244.
实例----------Examples----------
#decrease the maximum number inner iterations and display the [减少内迭代的最大数量，并显示]
#evolution of the (outer) iterations[进化的（外）的迭代]
seg.control(display = TRUE, maxit.glm=4)
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 00:22:51
